url
stringlengths 58
61
| repository_url
stringclasses 1
value | labels_url
stringlengths 72
75
| comments_url
stringlengths 67
70
| events_url
stringlengths 65
68
| html_url
stringlengths 46
51
| id
int64 599M
1.94B
| node_id
stringlengths 18
32
| number
int64 1
6.3k
| title
stringlengths 1
290
| user
stringlengths 870
1.16k
| labels
stringclasses 78
values | state
stringclasses 2
values | locked
bool 1
class | assignee
stringclasses 66
values | assignees
stringclasses 78
values | milestone
stringclasses 8
values | comments
stringlengths 2
193k
| created_at
stringlengths 25
25
| updated_at
stringlengths 25
25
| closed_at
stringlengths 25
25
⌀ | author_association
stringclasses 3
values | active_lock_reason
float64 | body
stringlengths 1
228k
⌀ | reactions
stringlengths 191
197
| timeline_url
stringlengths 67
70
| performed_via_github_app
float64 | state_reason
stringclasses 3
values | draft
float64 0
1
⌀ | pull_request
stringlengths 289
315
⌀ | is_pull_request
bool 2
classes |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
https://api.github.com/repos/huggingface/datasets/issues/382 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/382/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/382/comments | https://api.github.com/repos/huggingface/datasets/issues/382/events | https://github.com/huggingface/datasets/issues/382 | 655,290,482 | MDU6SXNzdWU2NTUyOTA0ODI= | 382 | 1080 | {'avatar_url': 'https://avatars.githubusercontent.com/u/60942503?v=4', 'events_url': 'https://api.github.com/users/saq194/events{/privacy}', 'followers_url': 'https://api.github.com/users/saq194/followers', 'following_url': 'https://api.github.com/users/saq194/following{/other_user}', 'gists_url': 'https://api.github.com/users/saq194/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/saq194', 'id': 60942503, 'login': 'saq194', 'node_id': 'MDQ6VXNlcjYwOTQyNTAz', 'organizations_url': 'https://api.github.com/users/saq194/orgs', 'received_events_url': 'https://api.github.com/users/saq194/received_events', 'repos_url': 'https://api.github.com/users/saq194/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/saq194/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/saq194/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/saq194'} | [] | closed | false | null | [] | null | [] | 2020-07-11 22:29:07+00:00 | 2020-07-11 22:49:38+00:00 | 2020-07-11 22:49:38+00:00 | NONE | null | null | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/382/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/382/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/381 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/381/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/381/comments | https://api.github.com/repos/huggingface/datasets/issues/381/events | https://github.com/huggingface/datasets/issues/381 | 655,277,119 | MDU6SXNzdWU2NTUyNzcxMTk= | 381 | NLp | {'avatar_url': 'https://avatars.githubusercontent.com/u/68147610?v=4', 'events_url': 'https://api.github.com/users/Spartanthor/events{/privacy}', 'followers_url': 'https://api.github.com/users/Spartanthor/followers', 'following_url': 'https://api.github.com/users/Spartanthor/following{/other_user}', 'gists_url': 'https://api.github.com/users/Spartanthor/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/Spartanthor', 'id': 68147610, 'login': 'Spartanthor', 'node_id': 'MDQ6VXNlcjY4MTQ3NjEw', 'organizations_url': 'https://api.github.com/users/Spartanthor/orgs', 'received_events_url': 'https://api.github.com/users/Spartanthor/received_events', 'repos_url': 'https://api.github.com/users/Spartanthor/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/Spartanthor/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/Spartanthor/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/Spartanthor'} | [] | closed | false | null | [] | null | [] | 2020-07-11 20:50:14+00:00 | 2020-07-11 20:50:39+00:00 | 2020-07-11 20:50:39+00:00 | NONE | null | null | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/381/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/381/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/378 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/378/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/378/comments | https://api.github.com/repos/huggingface/datasets/issues/378/events | https://github.com/huggingface/datasets/issues/378 | 655,226,316 | MDU6SXNzdWU2NTUyMjYzMTY= | 378 | [dataset] Structure of MLQA seems unecessary nested | {'avatar_url': 'https://avatars.githubusercontent.com/u/7353373?v=4', 'events_url': 'https://api.github.com/users/thomwolf/events{/privacy}', 'followers_url': 'https://api.github.com/users/thomwolf/followers', 'following_url': 'https://api.github.com/users/thomwolf/following{/other_user}', 'gists_url': 'https://api.github.com/users/thomwolf/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/thomwolf', 'id': 7353373, 'login': 'thomwolf', 'node_id': 'MDQ6VXNlcjczNTMzNzM=', 'organizations_url': 'https://api.github.com/users/thomwolf/orgs', 'received_events_url': 'https://api.github.com/users/thomwolf/received_events', 'repos_url': 'https://api.github.com/users/thomwolf/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/thomwolf/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/thomwolf/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/thomwolf'} | [] | closed | false | null | [] | null | ['Same for the RACE dataset: https://github.com/huggingface/nlp/blob/master/datasets/race/race.py\r\n\r\nShould we scan all the datasets to remove this pattern of un-necessary nesting?'
"You're right, I think we don't need to use the nested dictionary. \r\n"] | 2020-07-11 15:16:08+00:00 | 2020-07-15 16:17:20+00:00 | 2020-07-15 16:17:20+00:00 | MEMBER | null | The features of the MLQA dataset comprise several nested dictionaries with a single element inside (for `questions` and `ids`): https://github.com/huggingface/nlp/blob/master/datasets/mlqa/mlqa.py#L90-L97
Should we keep this @mariamabarham @patrickvonplaten? Was this added for compatibility with tfds?
```python
features=nlp.Features(
{
"context": nlp.Value("string"),
"questions": nlp.features.Sequence({"question": nlp.Value("string")}),
"answers": nlp.features.Sequence(
{"text": nlp.Value("string"), "answer_start": nlp.Value("int32"),}
),
"ids": nlp.features.Sequence({"idx": nlp.Value("string")})
``` | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/378/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/378/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/377 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/377/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/377/comments | https://api.github.com/repos/huggingface/datasets/issues/377/events | https://github.com/huggingface/datasets/issues/377 | 655,215,790 | MDU6SXNzdWU2NTUyMTU3OTA= | 377 | Iyy!!! | {'avatar_url': 'https://avatars.githubusercontent.com/u/68154535?v=4', 'events_url': 'https://api.github.com/users/ajinomoh/events{/privacy}', 'followers_url': 'https://api.github.com/users/ajinomoh/followers', 'following_url': 'https://api.github.com/users/ajinomoh/following{/other_user}', 'gists_url': 'https://api.github.com/users/ajinomoh/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/ajinomoh', 'id': 68154535, 'login': 'ajinomoh', 'node_id': 'MDQ6VXNlcjY4MTU0NTM1', 'organizations_url': 'https://api.github.com/users/ajinomoh/orgs', 'received_events_url': 'https://api.github.com/users/ajinomoh/received_events', 'repos_url': 'https://api.github.com/users/ajinomoh/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/ajinomoh/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/ajinomoh/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/ajinomoh'} | [] | closed | false | null | [] | null | [] | 2020-07-11 14:11:07+00:00 | 2020-07-11 14:30:51+00:00 | 2020-07-11 14:30:51+00:00 | NONE | null | null | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/377/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/377/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/376 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/376/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/376/comments | https://api.github.com/repos/huggingface/datasets/issues/376/events | https://github.com/huggingface/datasets/issues/376 | 655,047,826 | MDU6SXNzdWU2NTUwNDc4MjY= | 376 | to_pandas conversion doesn't always work | {'avatar_url': 'https://avatars.githubusercontent.com/u/7353373?v=4', 'events_url': 'https://api.github.com/users/thomwolf/events{/privacy}', 'followers_url': 'https://api.github.com/users/thomwolf/followers', 'following_url': 'https://api.github.com/users/thomwolf/following{/other_user}', 'gists_url': 'https://api.github.com/users/thomwolf/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/thomwolf', 'id': 7353373, 'login': 'thomwolf', 'node_id': 'MDQ6VXNlcjczNTMzNzM=', 'organizations_url': 'https://api.github.com/users/thomwolf/orgs', 'received_events_url': 'https://api.github.com/users/thomwolf/received_events', 'repos_url': 'https://api.github.com/users/thomwolf/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/thomwolf/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/thomwolf/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/thomwolf'} | [] | closed | false | null | [] | null | ['**Edit**: other topic previously in this message moved to a new issue: https://github.com/huggingface/nlp/issues/387'
"Could you try to update pyarrow to >=0.17.0 ? It should fix the `to_pandas` bug\r\n\r\nAlso I'm not sure that structures like list<struct> are fully supported in the lib (none of the datasets use that).\r\nIt can cause issues when using dataset transforms like `filter` for example"] | 2020-07-10 21:33:31+00:00 | 2022-10-04 18:05:39+00:00 | 2022-10-04 18:05:39+00:00 | MEMBER | null | For some complex nested types, the conversion from Arrow to python dict through pandas doesn't seem to be possible.
Here is an example using the official SQUAD v2 JSON file.
This example was found while investigating #373.
```python
>>> squad = load_dataset('json', data_files={nlp.Split.TRAIN: ["./train-v2.0.json"]}, download_mode=nlp.GenerateMode.FORCE_REDOWNLOAD, version="1.0.0", field='data')
>>> squad['train']
Dataset(schema: {'title': 'string', 'paragraphs': 'list<item: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>>'}, num_rows: 442)
>>> squad['train'][0]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/thomwolf/Documents/GitHub/datasets/src/nlp/arrow_dataset.py", line 589, in __getitem__
format_kwargs=self._format_kwargs,
File "/Users/thomwolf/Documents/GitHub/datasets/src/nlp/arrow_dataset.py", line 529, in _getitem
outputs = self._unnest(self._data.slice(key, 1).to_pandas().to_dict("list"))
File "pyarrow/array.pxi", line 559, in pyarrow.lib._PandasConvertible.to_pandas
File "pyarrow/table.pxi", line 1367, in pyarrow.lib.Table._to_pandas
File "/Users/thomwolf/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/pandas_compat.py", line 766, in table_to_blockmanager
blocks = _table_to_blocks(options, table, categories, ext_columns_dtypes)
File "/Users/thomwolf/miniconda2/envs/datasets/lib/python3.7/site-packages/pyarrow/pandas_compat.py", line 1101, in _table_to_blocks
list(extension_columns.keys()))
File "pyarrow/table.pxi", line 881, in pyarrow.lib.table_to_blocks
File "pyarrow/error.pxi", line 105, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Not implemented type for Arrow list to pandas: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>
```
cc @lhoestq would we have a way to detect this from the schema maybe?
Here is the schema for this pretty complex JSON:
```python
>>> squad['train'].schema
title: string
paragraphs: list<item: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>>
child 0, item: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>
child 0, qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>
child 0, item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>
child 0, question: string
child 1, id: string
child 2, answers: list<item: struct<text: string, answer_start: int64>>
child 0, item: struct<text: string, answer_start: int64>
child 0, text: string
child 1, answer_start: int64
child 3, is_impossible: bool
child 4, plausible_answers: list<item: struct<text: string, answer_start: int64>>
child 0, item: struct<text: string, answer_start: int64>
child 0, text: string
child 1, answer_start: int64
child 1, context: string
``` | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/376/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/376/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/375 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/375/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/375/comments | https://api.github.com/repos/huggingface/datasets/issues/375/events | https://github.com/huggingface/datasets/issues/375 | 655,023,307 | MDU6SXNzdWU2NTUwMjMzMDc= | 375 | TypeError when computing bertscore | {'avatar_url': 'https://avatars.githubusercontent.com/u/13269577?v=4', 'events_url': 'https://api.github.com/users/willywsm1013/events{/privacy}', 'followers_url': 'https://api.github.com/users/willywsm1013/followers', 'following_url': 'https://api.github.com/users/willywsm1013/following{/other_user}', 'gists_url': 'https://api.github.com/users/willywsm1013/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/willywsm1013', 'id': 13269577, 'login': 'willywsm1013', 'node_id': 'MDQ6VXNlcjEzMjY5NTc3', 'organizations_url': 'https://api.github.com/users/willywsm1013/orgs', 'received_events_url': 'https://api.github.com/users/willywsm1013/received_events', 'repos_url': 'https://api.github.com/users/willywsm1013/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/willywsm1013/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/willywsm1013/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/willywsm1013'} | [] | closed | false | null | [] | null | ['I am not able to reproduce this issue on my side.\r\nCould you give us more details about the inputs you used ?\r\n\r\nI do get another error though:\r\n```\r\n~/.virtualenvs/hf-datasets/lib/python3.7/site-packages/bert_score/utils.py in bert_cos_score_idf(model, refs, hyps, tokenizer, idf_dict, verbose, batch_size, device, all_layers)\r\n 371 return sorted(list(set(l)), key=lambda x: len(x.split(" ")))\r\n 372 \r\n--> 373 sentences = dedup_and_sort(refs + hyps)\r\n 374 embs = []\r\n 375 iter_range = range(0, len(sentences), batch_size)\r\n\r\nValueError: operands could not be broadcast together with shapes (0,) (2,)\r\n```\r\nThat\'s because it gets numpy arrays as input and not lists. See #387 '
'The other issue was fixed by #403 \r\n\r\nDo you still get this issue @willywsm1013 ?\r\n'] | 2020-07-10 20:37:44+00:00 | 2022-06-01 15:15:59+00:00 | 2022-06-01 15:15:59+00:00 | NONE | null | Hi,
I installed nlp 0.3.0 via pip, and my python version is 3.7.
When I tried to compute bertscore with the code:
```
import nlp
bertscore = nlp.load_metric('bertscore')
# load hyps and refs
...
print (bertscore.compute(hyps, refs, lang='en'))
```
I got the following error.
```
Traceback (most recent call last):
File "bert_score_evaluate.py", line 16, in <module>
print (bertscore.compute(hyps, refs, lang='en'))
File "/home/willywsm/anaconda3/envs/torcher/lib/python3.7/site-packages/nlp/metric.py", line 200, in compute
output = self._compute(predictions=predictions, references=references, **metrics_kwargs)
File "/home/willywsm/anaconda3/envs/torcher/lib/python3.7/site-packages/nlp/metrics/bertscore/fb176889831bf0ce995ed197edc94b2e9a83f647a869bb8c9477dbb2d04d0f08/bertscore.py", line 105, in _compute
hashcode = bert_score.utils.get_hash(model_type, num_layers, idf, rescale_with_baseline)
TypeError: get_hash() takes 3 positional arguments but 4 were given
```
It seems like there is something wrong with get_hash() function? | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/375/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/375/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/374 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/374/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/374/comments | https://api.github.com/repos/huggingface/datasets/issues/374/events | https://github.com/huggingface/datasets/pull/374 | 654,895,066 | MDExOlB1bGxSZXF1ZXN0NDQ3NTMxMzUy | 374 | Add dataset post processing for faiss indexes | {'avatar_url': 'https://avatars.githubusercontent.com/u/42851186?v=4', 'events_url': 'https://api.github.com/users/lhoestq/events{/privacy}', 'followers_url': 'https://api.github.com/users/lhoestq/followers', 'following_url': 'https://api.github.com/users/lhoestq/following{/other_user}', 'gists_url': 'https://api.github.com/users/lhoestq/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/lhoestq', 'id': 42851186, 'login': 'lhoestq', 'node_id': 'MDQ6VXNlcjQyODUxMTg2', 'organizations_url': 'https://api.github.com/users/lhoestq/orgs', 'received_events_url': 'https://api.github.com/users/lhoestq/received_events', 'repos_url': 'https://api.github.com/users/lhoestq/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/lhoestq/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/lhoestq/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/lhoestq'} | [] | closed | false | null | [] | null | ["I changed the `wiki_dpr` script to ignore the last 24 examples for now. Hopefully we'll have the full version soon.\r\nThe datasets_infos.json and the data on GCS are updated.\r\n\r\nAnd I also added a check to make sure we don't have post processing resources in sub-directories."
'I added a dummy config that can be loaded with:\r\n```python\r\nwiki = load_dataset("wiki_dpr", "dummy_psgs_w100_no_embeddings", with_index=True, split="train")\r\n```\r\nIt\'s only 6MB of arrow files and 30MB of index'] | 2020-07-10 16:25:59+00:00 | 2020-07-13 13:44:03+00:00 | 2020-07-13 13:44:01+00:00 | MEMBER | null | # Post processing of datasets for faiss indexes
Now that we can have datasets with embeddings (see `wiki_pr` for example), we can allow users to load the dataset + get the Faiss index that comes with it to do nearest neighbors queries.
## Implementation proposition
- Faiss indexes have to be added to the `nlp.Dataset` object, and therefore it's in a different scope that what are doing the `_split_generators` and `_generate_examples` methods of `nlp.DatasetBuilder`. Therefore I added a new method for post processing of the `nlp.Dataset` object called `_post_process` (name could change)
- The role of `_post_process` is to apply dataset transforms (filter/map etc.) or indexing functions (add_faiss_index) to modify/enrich the `nlp.Dataset` object. It is not part of the `download_and_prepare` process (that is focused on arrow files creation) so the post processing is run inside the `as_dataset` method.
- `_post_process` can generate new files (cached files from dataset transforms or serialized faiss indexes) and their names are defined by `_post_processing_resources`
- as we know what are the post processing resources, we can download them automatically from google storage instead of computing them if they're available (as we do for arrow files)
I'd happy to discuss these choices !
## The `wiki_dpr` index
It takes 1h20 and ~7GB of memory to compute. The final index is 1.42GB and takes ~1.5GB of memory.
This is pretty cool given that a naive flat index would take 170GB of memory to store the 21M vectors of dim 768.
I couldn't use directly the Faiss `index_factory` as I needed to set the metric to inner product.
## Example of usage
```python
import nlp
dset = nlp.load_dataset(
"wiki_dpr",
"psgs_w100_with_nq_embeddings",
split="train",
with_index=True
)
print(len(dset), dset.list_indexes()) # (21015300, ['embeddings'])
```
(it also works with the dataset configuration without the embeddings because I added the index file in google storage for this one too)
## Demo
You can also check a demo on google colab that shows how to use it with the DPRQuestionEncoder from transformers:
https://colab.research.google.com/drive/1FakNU8W5EPMcWff7iP1H6REg3XSS0YLp?usp=sharing
| {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/374/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/374/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/374.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/374', 'merged_at': '2020-07-13T13:44:01Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/374.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/374'} | true |
https://api.github.com/repos/huggingface/datasets/issues/373 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/373/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/373/comments | https://api.github.com/repos/huggingface/datasets/issues/373/events | https://github.com/huggingface/datasets/issues/373 | 654,845,133 | MDU6SXNzdWU2NTQ4NDUxMzM= | 373 | Segmentation fault when loading local JSON dataset as of #372 | {'avatar_url': 'https://avatars.githubusercontent.com/u/24683907?v=4', 'events_url': 'https://api.github.com/users/vegarab/events{/privacy}', 'followers_url': 'https://api.github.com/users/vegarab/followers', 'following_url': 'https://api.github.com/users/vegarab/following{/other_user}', 'gists_url': 'https://api.github.com/users/vegarab/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/vegarab', 'id': 24683907, 'login': 'vegarab', 'node_id': 'MDQ6VXNlcjI0NjgzOTA3', 'organizations_url': 'https://api.github.com/users/vegarab/orgs', 'received_events_url': 'https://api.github.com/users/vegarab/received_events', 'repos_url': 'https://api.github.com/users/vegarab/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/vegarab/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/vegarab/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/vegarab'} | [] | closed | false | null | [] | null | ['I\'ve seen this sort of thing before -- it might help to delete the directory -- I\'ve also noticed that there is an error with the json Dataloader for any data I\'ve tried to load. I\'ve replaced it with this, which skips over the data feature population step:\r\n\r\n\r\n```python\r\nimport os\r\n\r\nimport pyarrow.json as paj\r\n\r\nimport nlp as hf_nlp\r\n\r\nfrom nlp import DatasetInfo, BuilderConfig, SplitGenerator, Split, utils\r\nfrom nlp.arrow_writer import ArrowWriter\r\n\r\n\r\nclass JSONDatasetBuilder(hf_nlp.ArrowBasedBuilder):\r\n BUILDER_CONFIG_CLASS = BuilderConfig\r\n\r\n def _info(self):\r\n return DatasetInfo()\r\n\r\n def _split_generators(self, dl_manager):\r\n """ We handle string, list and dicts in datafiles\r\n """\r\n if isinstance(self.config.data_files, (str, list, tuple)):\r\n files = self.config.data_files\r\n if isinstance(files, str):\r\n files = [files]\r\n return [SplitGenerator(name=Split.TRAIN, gen_kwargs={"files": files})]\r\n splits = []\r\n for split_name in [Split.TRAIN, Split.VALIDATION, Split.TEST]:\r\n if split_name in self.config.data_files:\r\n files = self.config.data_files[split_name]\r\n if isinstance(files, str):\r\n files = [files]\r\n splits.append(SplitGenerator(name=split_name, gen_kwargs={"files": files}))\r\n return splits\r\n\r\n def _prepare_split(self, split_generator):\r\n fname = "{}-{}.arrow".format(self.name, split_generator.name)\r\n fpath = os.path.join(self._cache_dir, fname)\r\n\r\n writer = ArrowWriter(path=fpath)\r\n\r\n generator = self._generate_tables(**split_generator.gen_kwargs)\r\n for key, table in utils.tqdm(generator, unit=" tables", leave=False):\r\n writer.write_table(table)\r\n num_examples, num_bytes = writer.finalize()\r\n\r\n split_generator.split_info.num_examples = num_examples\r\n split_generator.split_info.num_bytes = num_bytes\r\n\r\n def _generate_tables(self, files):\r\n for i, file in enumerate(files):\r\n pa_table = paj.read_json(\r\n file\r\n )\r\n yield i, pa_table\r\n\r\n```'
"Yes, deleting the directory solves the error whenever I try to rerun.\r\n\r\nBy replacing the json-loader, you mean the cached file in my `site-packages` directory? e.g. `/home/XXX/.cache/lib/python3.7/site-packages/nlp/datasets/json/(...)/json.py` \r\n\r\nWhen I was testing this out before the #372 PR was merged I had issues installing it properly locally. Since the `json.py` script was downloaded instead of actually using the one provided in the local install. Manually updating that file seemed to solve it, but it didn't seem like a proper solution. Especially when having to run this on a remote compute cluster with no access to that directory."
'I see, diving in the JSON file for SQuAD it\'s a pretty complex structure.\r\n\r\nThe best solution for you, if you have a dataset really similar to SQuAD would be to copy and modify the SQuAD data processing script. We will probably add soon an option to be able to specify file path to use instead of the automatic URL encoded in the script but in the meantime you can:\r\n- copy the [squad script](https://github.com/huggingface/nlp/blob/master/datasets/squad/squad.py) in a new script for your dataset\r\n- in the new script replace [these `urls_to_download `](https://github.com/huggingface/nlp/blob/master/datasets/squad/squad.py#L99-L102) by `urls_to_download=self.config.data_files`\r\n- load the dataset with `dataset = load_dataset(\'path/to/your/new/script\', data_files={nlp.Split.TRAIN: "./datasets/train-v2.0.json"})`\r\n\r\nThis way you can reuse all the processing logic of the SQuAD loading script.'
"This seems like a more sensible solution! Thanks, @thomwolf. It's been a little daunting to understand what these scripts actually do, due to the level of abstraction and central documentation.\r\n\r\nAm I correct in assuming that the `_generate_examples()` function is the actual procedure for how the data is loaded from file? Meaning that essentially with a file containing another format, that is the only function that requires re-implementation? I'm working with a lot of datasets that, due to licensing and privacy, cannot be published. As this library is so neatly integrated with the transformers library and gives easy access to public sets such as SQUAD and increased performance, it is very neat to be able to load my private sets as well. As of now, I have just been working on scripts for translating all my data into the SQUAD-format before using the json script, but I see that it might not be necessary after all. "
"Yes `_generate_examples()` is the main entry point. If you change the shape of the returned dictionary you also need to update the `features` in the `_info`.\r\n\r\nI'm currently writing the doc so it should be easier soon to use the library and know how to add your datasets.\r\n"
'Could you try to update pyarrow to >=0.17.0 @vegarab ?\r\nI don\'t have any segmentation fault with my version of pyarrow (0.17.1)\r\n\r\nI tested with\r\n```python\r\nimport nlp\r\ns = nlp.load_dataset("json", data_files="train-v2.0.json", field="data", split="train")\r\ns[0]\r\n# {\'title\': \'Normans\', \'paragraphs\': [{\'qas\': [{\'question\': \'In what country is Normandy located?\', \'id\':...\r\n```'
'Also if you want to have your own dataset script, we now have a new documentation !\r\nSee here:\r\nhttps://huggingface.co/nlp/add_dataset.html'
'@lhoestq \r\nFor some reason, I am not able to reproduce the segmentation fault, on pyarrow==0.16.0. Using the exact same environment and file.\r\n\r\nAnyhow, I discovered that pyarrow>=0.17.0 is required to read in a JSON file where the pandas structs contain lists. Otherwise, pyarrow complains when attempting to cast the struct:\r\n```py\r\nimport nlp\r\n>>> s = nlp.load_dataset("json", data_files="datasets/train-v2.0.json", field="data", split="train")\r\nUsing custom data configuration default\r\n>>> s[0]\r\nTraceback (most recent call last):\r\n File "<stdin>", line 1, in <module>\r\n File "/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/nlp/arrow_dataset.py", line 558, in __getitem__\r\n format_kwargs=self._format_kwargs,\r\n File "/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/nlp/arrow_dataset.py", line 498, in _getitem\r\n outputs = self._unnest(self._data.slice(key, 1).to_pandas().to_dict("list"))\r\n File "pyarrow/array.pxi", line 559, in pyarrow.lib._PandasConvertible.to_pandas\r\n File "pyarrow/table.pxi", line 1367, in pyarrow.lib.Table._to_pandas\r\n File "/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/pyarrow/pandas_compat.py", line 766, in table_to_blockmanager\r\n blocks = _table_to_blocks(options, table, categories, ext_columns_dtypes)\r\n File "/home/vegarab/.conda/envs/torch/lib/python3.7/site-packages/pyarrow/pandas_compat.py", line 1101, in _table_to_blocks\r\n list(extension_columns.keys()))\r\n File "pyarrow/table.pxi", line 881, in pyarrow.lib.table_to_blocks\r\n File "pyarrow/error.pxi", line 105, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowNotImplementedError: Not implemented type for Arrow list to pandas: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>\r\n>>> s\r\nDataset(schema: {\'title\': \'string\', \'paragraphs\': \'list<item: struct<qas: list<item: struct<question: string, id: string, answers: list<item: struct<text: string, answer_start: int64>>, is_impossible: bool, plausible_answers: list<item: struct<text: string, answer_start: int64>>>>, context: string>>\'}, num_rows: 35)\r\n```\r\n\r\nUpgrading to >=0.17.0 provides the same dataset structure, but accessing the records is possible without the same exception. \r\n\r\n'
'Very happy to see some extended documentation! '
'#376 seems to be reporting the same issue as mentioned above. '
'This issue helped me a lot, thanks.\r\nHope this issue will be fixed soon.'] | 2020-07-10 15:04:25+00:00 | 2022-10-04 18:05:47+00:00 | 2022-10-04 18:05:47+00:00 | CONTRIBUTOR | null | The last issue was closed (#369) once the #372 update was merged. However, I'm still not able to load a SQuAD formatted JSON file. Instead of the previously recorded pyarrow error, I now get a segmentation fault.
```
dataset = nlp.load_dataset('json', data_files={nlp.Split.TRAIN: ["./datasets/train-v2.0.json"]}, field='data')
```
causes
```
Using custom data configuration default
Downloading and preparing dataset json/default (download: Unknown size, generated: Unknown size, total: Unknown size) to /home/XXX/.cache/huggingface/datasets/json/default/0.0.0...
0 tables [00:00, ? tables/s]Segmentation fault (core dumped)
```
where `./datasets/train-v2.0.json` is downloaded directly from https://rajpurkar.github.io/SQuAD-explorer/.
This is consistent with other SQuAD-formatted JSON files.
When attempting to load the dataset again, I get the following:
```
Using custom data configuration default
Traceback (most recent call last):
File "dataloader.py", line 6, in <module>
'json', data_files={nlp.Split.TRAIN: ["./datasets/train-v2.0.json"]}, field='data')
File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/load.py", line 524, in load_dataset
save_infos=save_infos,
File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/builder.py", line 382, in download_and_prepare
with incomplete_dir(self._cache_dir) as tmp_data_dir:
File "/home/XXX/.conda/envs/torch/lib/python3.7/contextlib.py", line 112, in __enter__
return next(self.gen)
File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/builder.py", line 368, in incomplete_dir
os.makedirs(tmp_dir)
File "/home/XXX/.conda/envs/torch/lib/python3.7/os.py", line 223, in makedirs
mkdir(name, mode)
FileExistsError: [Errno 17] File exists: '/home/XXX/.cache/huggingface/datasets/json/default/0.0.0.incomplete'
```
(Not sure if you wanted this in the previous issue #369 or not as it was closed.) | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/373/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/373/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/372 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/372/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/372/comments | https://api.github.com/repos/huggingface/datasets/issues/372/events | https://github.com/huggingface/datasets/pull/372 | 654,774,420 | MDExOlB1bGxSZXF1ZXN0NDQ3NDMzNTA4 | 372 | Make the json script more flexible | {'avatar_url': 'https://avatars.githubusercontent.com/u/7353373?v=4', 'events_url': 'https://api.github.com/users/thomwolf/events{/privacy}', 'followers_url': 'https://api.github.com/users/thomwolf/followers', 'following_url': 'https://api.github.com/users/thomwolf/following{/other_user}', 'gists_url': 'https://api.github.com/users/thomwolf/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/thomwolf', 'id': 7353373, 'login': 'thomwolf', 'node_id': 'MDQ6VXNlcjczNTMzNzM=', 'organizations_url': 'https://api.github.com/users/thomwolf/orgs', 'received_events_url': 'https://api.github.com/users/thomwolf/received_events', 'repos_url': 'https://api.github.com/users/thomwolf/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/thomwolf/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/thomwolf/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/thomwolf'} | [] | closed | false | null | [] | null | [] | 2020-07-10 13:15:15+00:00 | 2020-07-10 14:52:07+00:00 | 2020-07-10 14:52:06+00:00 | MEMBER | null | Fix https://github.com/huggingface/nlp/issues/359
Fix https://github.com/huggingface/nlp/issues/369
JSON script now can accept JSON files containing a single dict with the records as a list in one attribute to the dict (previously it only accepted JSON files containing records as rows of dicts in the file).
In this case, you should indicate using `field=XXX` the name of the field in the JSON structure which contains the records you want to load. The records can be a dict of lists or a list of dicts.
E.g. to load the SQuAD dataset JSON (without using the `squad` specific dataset loading script), in which the data rows are in the `data` field of the JSON dict, you can do:
```python
from nlp import load_dataset
dataset = load_dataset('json', data_files='/PATH/TO/JSON', field='data')
``` | {'+1': 1, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 1, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/372/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/372/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/372.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/372', 'merged_at': '2020-07-10T14:52:05Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/372.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/372'} | true |
https://api.github.com/repos/huggingface/datasets/issues/371 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/371/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/371/comments | https://api.github.com/repos/huggingface/datasets/issues/371/events | https://github.com/huggingface/datasets/pull/371 | 654,668,242 | MDExOlB1bGxSZXF1ZXN0NDQ3MzQ4NDgw | 371 | Fix cached file path for metrics with different config names | {'avatar_url': 'https://avatars.githubusercontent.com/u/42851186?v=4', 'events_url': 'https://api.github.com/users/lhoestq/events{/privacy}', 'followers_url': 'https://api.github.com/users/lhoestq/followers', 'following_url': 'https://api.github.com/users/lhoestq/following{/other_user}', 'gists_url': 'https://api.github.com/users/lhoestq/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/lhoestq', 'id': 42851186, 'login': 'lhoestq', 'node_id': 'MDQ6VXNlcjQyODUxMTg2', 'organizations_url': 'https://api.github.com/users/lhoestq/orgs', 'received_events_url': 'https://api.github.com/users/lhoestq/received_events', 'repos_url': 'https://api.github.com/users/lhoestq/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/lhoestq/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/lhoestq/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/lhoestq'} | [] | closed | false | null | [] | null | ['Thanks for the fast fix!'] | 2020-07-10 10:02:24+00:00 | 2020-07-10 13:45:22+00:00 | 2020-07-10 13:45:20+00:00 | MEMBER | null | The config name was not taken into account to build the cached file path.
It should fix #368 | {'+1': 1, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 1, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/371/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/371/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/371.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/371', 'merged_at': '2020-07-10T13:45:20Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/371.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/371'} | true |
https://api.github.com/repos/huggingface/datasets/issues/370 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/370/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/370/comments | https://api.github.com/repos/huggingface/datasets/issues/370/events | https://github.com/huggingface/datasets/pull/370 | 654,304,193 | MDExOlB1bGxSZXF1ZXN0NDQ3MDU3NTIw | 370 | Allow indexing Dataset via np.ndarray | {'avatar_url': 'https://avatars.githubusercontent.com/u/4564897?v=4', 'events_url': 'https://api.github.com/users/jarednielsen/events{/privacy}', 'followers_url': 'https://api.github.com/users/jarednielsen/followers', 'following_url': 'https://api.github.com/users/jarednielsen/following{/other_user}', 'gists_url': 'https://api.github.com/users/jarednielsen/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/jarednielsen', 'id': 4564897, 'login': 'jarednielsen', 'node_id': 'MDQ6VXNlcjQ1NjQ4OTc=', 'organizations_url': 'https://api.github.com/users/jarednielsen/orgs', 'received_events_url': 'https://api.github.com/users/jarednielsen/received_events', 'repos_url': 'https://api.github.com/users/jarednielsen/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/jarednielsen/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/jarednielsen/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/jarednielsen'} | [] | closed | false | null | [] | null | ['Looks like a flaky CI, failed download from S3.'] | 2020-07-09 19:43:15+00:00 | 2020-07-10 14:05:44+00:00 | 2020-07-10 14:05:43+00:00 | CONTRIBUTOR | null | null | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/370/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/370/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/370.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/370', 'merged_at': '2020-07-10T14:05:43Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/370.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/370'} | true |
https://api.github.com/repos/huggingface/datasets/issues/369 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/369/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/369/comments | https://api.github.com/repos/huggingface/datasets/issues/369/events | https://github.com/huggingface/datasets/issues/369 | 654,186,890 | MDU6SXNzdWU2NTQxODY4OTA= | 369 | can't load local dataset: pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries | {'avatar_url': 'https://avatars.githubusercontent.com/u/24683907?v=4', 'events_url': 'https://api.github.com/users/vegarab/events{/privacy}', 'followers_url': 'https://api.github.com/users/vegarab/followers', 'following_url': 'https://api.github.com/users/vegarab/following{/other_user}', 'gists_url': 'https://api.github.com/users/vegarab/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/vegarab', 'id': 24683907, 'login': 'vegarab', 'node_id': 'MDQ6VXNlcjI0NjgzOTA3', 'organizations_url': 'https://api.github.com/users/vegarab/orgs', 'received_events_url': 'https://api.github.com/users/vegarab/received_events', 'repos_url': 'https://api.github.com/users/vegarab/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/vegarab/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/vegarab/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/vegarab'} | [{'color': '2edb81', 'default': False, 'description': 'A bug in a dataset script provided in the library', 'id': 2067388877, 'name': 'dataset bug', 'node_id': 'MDU6TGFiZWwyMDY3Mzg4ODc3', 'url': 'https://api.github.com/repos/huggingface/datasets/labels/dataset%20bug'}] | closed | false | null | [] | null | ['I am able to reproduce this with the official SQuAD `train-v2.0.json` file downloaded directly from https://rajpurkar.github.io/SQuAD-explorer/'
'I am facing this issue in transformers library 3.0.2 while reading a csv using datasets.\r\nIs this fixed in latest version? \r\nI updated the latest version 4.0.1 but still getting this error. What could cause this error?'] | 2020-07-09 16:16:53+00:00 | 2020-12-15 23:07:22+00:00 | 2020-07-10 14:52:06+00:00 | CONTRIBUTOR | null | Trying to load a local SQuAD-formatted dataset (from a JSON file, about 60MB):
```
dataset = nlp.load_dataset(path='json', data_files={nlp.Split.TRAIN: ["./path/to/file.json"]})
```
causes
```
Traceback (most recent call last):
File "dataloader.py", line 9, in <module>
["./path/to/file.json"]})
File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/load.py", line 524, in load_dataset
save_infos=save_infos,
File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/builder.py", line 432, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/builder.py", line 483, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/builder.py", line 719, in _prepare_split
for key, table in utils.tqdm(generator, unit=" tables", leave=False):
File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/tqdm/std.py", line 1129, in __iter__
for obj in iterable:
File "/home/XXX/.conda/envs/torch/lib/python3.7/site-packages/nlp/datasets/json/88c1bc5c68489f7eda549ed05a5a738527c613b3e7a4ee3524d9d233353a949b/json.py", line 53, in _generate_tables
file, read_options=self.config.pa_read_options, parse_options=self.config.pa_parse_options,
File "pyarrow/_json.pyx", line 191, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 85, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)
```
I haven't been able to find any reports of this specific pyarrow error here or elsewhere. | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/369/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/369/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/368 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/368/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/368/comments | https://api.github.com/repos/huggingface/datasets/issues/368/events | https://github.com/huggingface/datasets/issues/368 | 654,087,251 | MDU6SXNzdWU2NTQwODcyNTE= | 368 | load_metric can't acquire lock anymore | {'avatar_url': 'https://avatars.githubusercontent.com/u/2521628?v=4', 'events_url': 'https://api.github.com/users/ydshieh/events{/privacy}', 'followers_url': 'https://api.github.com/users/ydshieh/followers', 'following_url': 'https://api.github.com/users/ydshieh/following{/other_user}', 'gists_url': 'https://api.github.com/users/ydshieh/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/ydshieh', 'id': 2521628, 'login': 'ydshieh', 'node_id': 'MDQ6VXNlcjI1MjE2Mjg=', 'organizations_url': 'https://api.github.com/users/ydshieh/orgs', 'received_events_url': 'https://api.github.com/users/ydshieh/received_events', 'repos_url': 'https://api.github.com/users/ydshieh/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/ydshieh/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/ydshieh/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/ydshieh'} | [] | closed | false | null | [] | null | ["I found that, in the same process (or the same interactive session), if I do\r\n\r\nimport nlp\r\n\r\nm1 = nlp.load_metric('glue', 'mrpc')\r\nm2 = nlp.load_metric('glue', 'sst2')\r\n\r\nI will get the same error `ValueError: Cannot acquire lock, caching file might be used by another process, you should setup a unique 'experiment_id'`."] | 2020-07-09 14:04:09+00:00 | 2020-07-10 13:45:20+00:00 | 2020-07-10 13:45:20+00:00 | NONE | null | I can't load metric (glue) anymore after an error in a previous run. I even removed the whole cache folder `/home/XXX/.cache/huggingface/`, and the issue persisted. What are the steps to fix this?
Traceback (most recent call last):
File "/home/XXX/miniconda3/envs/ML-DL-py-3.7/lib/python3.7/site-packages/nlp/metric.py", line 101, in __init__
self.filelock.acquire(timeout=1)
File "/home/XXX/miniconda3/envs/ML-DL-py-3.7/lib/python3.7/site-packages/filelock.py", line 278, in acquire
raise Timeout(self._lock_file)
filelock.Timeout: The file lock '/home/XXX/.cache/huggingface/metrics/glue/1.0.0/1-glue-0.arrow.lock' could not be acquired.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "examples_huggingface_nlp.py", line 268, in <module>
main()
File "examples_huggingface_nlp.py", line 242, in main
dataset, metric = get_dataset_metric(glue_task)
File "examples_huggingface_nlp.py", line 77, in get_dataset_metric
metric = nlp.load_metric('glue', glue_config, experiment_id=1)
File "/home/XXX/miniconda3/envs/ML-DL-py-3.7/lib/python3.7/site-packages/nlp/load.py", line 440, in load_metric
**metric_init_kwargs,
File "/home/XXX/miniconda3/envs/ML-DL-py-3.7/lib/python3.7/site-packages/nlp/metric.py", line 104, in __init__
"Cannot acquire lock, caching file might be used by another process, "
ValueError: Cannot acquire lock, caching file might be used by another process, you should setup a unique 'experiment_id' for this run.
I0709 15:54:41.008838 139854118430464 filelock.py:318] Lock 139852058030936 released on /home/XXX/.cache/huggingface/metrics/glue/1.0.0/1-glue-0.arrow.lock
| {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/368/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/368/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/367 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/367/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/367/comments | https://api.github.com/repos/huggingface/datasets/issues/367/events | https://github.com/huggingface/datasets/pull/367 | 654,012,984 | MDExOlB1bGxSZXF1ZXN0NDQ2ODIxNTAz | 367 | Update Xtreme to add PAWS-X es | {'avatar_url': 'https://avatars.githubusercontent.com/u/38249783?v=4', 'events_url': 'https://api.github.com/users/mariamabarham/events{/privacy}', 'followers_url': 'https://api.github.com/users/mariamabarham/followers', 'following_url': 'https://api.github.com/users/mariamabarham/following{/other_user}', 'gists_url': 'https://api.github.com/users/mariamabarham/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/mariamabarham', 'id': 38249783, 'login': 'mariamabarham', 'node_id': 'MDQ6VXNlcjM4MjQ5Nzgz', 'organizations_url': 'https://api.github.com/users/mariamabarham/orgs', 'received_events_url': 'https://api.github.com/users/mariamabarham/received_events', 'repos_url': 'https://api.github.com/users/mariamabarham/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/mariamabarham/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/mariamabarham/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/mariamabarham'} | [] | closed | false | null | [] | null | [] | 2020-07-09 12:14:37+00:00 | 2020-07-09 12:37:11+00:00 | 2020-07-09 12:37:10+00:00 | CONTRIBUTOR | null | This PR adds the `PAWS-X.es` in the Xtreme dataset #362 | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/367/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/367/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/367.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/367', 'merged_at': '2020-07-09T12:37:10Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/367.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/367'} | true |
https://api.github.com/repos/huggingface/datasets/issues/366 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/366/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/366/comments | https://api.github.com/repos/huggingface/datasets/issues/366/events | https://github.com/huggingface/datasets/pull/366 | 653,954,896 | MDExOlB1bGxSZXF1ZXN0NDQ2NzcyODE2 | 366 | Add quora dataset | {'avatar_url': 'https://avatars.githubusercontent.com/u/13795113?v=4', 'events_url': 'https://api.github.com/users/ghomasHudson/events{/privacy}', 'followers_url': 'https://api.github.com/users/ghomasHudson/followers', 'following_url': 'https://api.github.com/users/ghomasHudson/following{/other_user}', 'gists_url': 'https://api.github.com/users/ghomasHudson/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/ghomasHudson', 'id': 13795113, 'login': 'ghomasHudson', 'node_id': 'MDQ6VXNlcjEzNzk1MTEz', 'organizations_url': 'https://api.github.com/users/ghomasHudson/orgs', 'received_events_url': 'https://api.github.com/users/ghomasHudson/received_events', 'repos_url': 'https://api.github.com/users/ghomasHudson/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/ghomasHudson/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/ghomasHudson/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/ghomasHudson'} | [] | closed | false | null | [] | null | ['Tests seem to be failing because of pandas'
"Kaggle needs authentification to download datasets. We don't have a way to handle that in the lib for now"] | 2020-07-09 10:34:22+00:00 | 2020-07-13 17:35:21+00:00 | 2020-07-13 17:35:21+00:00 | CONTRIBUTOR | null | Added the [Quora question pairs dataset](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs).
Implementation Notes:
- I used the original version provided on the quora website. There's also a [Kaggle competition](https://www.kaggle.com/c/quora-question-pairs) which has a nice train/test split but I can't find an easy way to download it.
- I've made the questions into a list:
```python
{
"questions": [
{"id":0, "text": "Is this an example question?"},
{"id":1, "text": "Is this a sample question?"},
],
...
}
```
rather than:
```python
{
"question1": "Is this an example question?",
"question2": "Is this a sample question?"
"qid0": 0
"qid1": 1
...
}
```
Not sure if this was the right call.
- Can't find a good citation for this dataset | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/366/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/366/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/366.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/366', 'merged_at': '2020-07-13T17:35:21Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/366.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/366'} | true |
https://api.github.com/repos/huggingface/datasets/issues/365 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/365/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/365/comments | https://api.github.com/repos/huggingface/datasets/issues/365/events | https://github.com/huggingface/datasets/issues/365 | 653,845,964 | MDU6SXNzdWU2NTM4NDU5NjQ= | 365 | How to augment data ? | {'avatar_url': 'https://avatars.githubusercontent.com/u/43774355?v=4', 'events_url': 'https://api.github.com/users/astariul/events{/privacy}', 'followers_url': 'https://api.github.com/users/astariul/followers', 'following_url': 'https://api.github.com/users/astariul/following{/other_user}', 'gists_url': 'https://api.github.com/users/astariul/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/astariul', 'id': 43774355, 'login': 'astariul', 'node_id': 'MDQ6VXNlcjQzNzc0MzU1', 'organizations_url': 'https://api.github.com/users/astariul/orgs', 'received_events_url': 'https://api.github.com/users/astariul/received_events', 'repos_url': 'https://api.github.com/users/astariul/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/astariul/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/astariul/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/astariul'} | [] | closed | false | null | [] | null | ['Using batched map is probably the easiest way at the moment.\r\nWhat kind of augmentation would you like to do ?'
'Some samples in the dataset are too long, I want to divide them in several samples.'
"Using batched map is the way to go then.\r\nWe'll make it clearer in the docs that map could be used for augmentation.\r\n\r\nLet me know if you think there should be another way to do it. Or feel free to close the issue otherwise."
"It just feels awkward to use map to augment data. Also it means it's not possible to augment data in a non-batched way.\r\n\r\nBut to be honest I have no idea of a good API..."
"Or for non-batched samples, how about returning a tuple ?\r\n\r\n```python\r\ndef aug(sample):\r\n # Simply copy the existing data to have x2 amount of data\r\n return sample, sample\r\n\r\ndataset = dataset.map(aug)\r\n```\r\n\r\nIt feels really natural and easy, but :\r\n\r\n* it means the behavior with batched data is different\r\n* I don't know how doable it is backend-wise\r\n\r\n@lhoestq "
"As we're working with arrow's columnar format we prefer to play with batches that are dictionaries instead of tuples.\r\nIf we have tuple it implies to re-format the data each time we want to write to arrow, which can lower the speed of map for example.\r\n\r\nIt's also a matter of coherence, as we don't want users to be confused whether they have to return dictionaries for some functions and tuples for others when they're doing batches."] | 2020-07-09 07:52:37+00:00 | 2020-07-10 09:12:07+00:00 | 2020-07-10 08:22:15+00:00 | NONE | null | Is there any clean way to augment data ?
For now my work-around is to use batched map, like this :
```python
def aug(samples):
# Simply copy the existing data to have x2 amount of data
for k, v in samples.items():
samples[k].extend(v)
return samples
dataset = dataset.map(aug, batched=True)
``` | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/365/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/365/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/364 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/364/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/364/comments | https://api.github.com/repos/huggingface/datasets/issues/364/events | https://github.com/huggingface/datasets/pull/364 | 653,821,597 | MDExOlB1bGxSZXF1ZXN0NDQ2NjY0NzM5 | 364 | add MS MARCO dataset | {'avatar_url': 'https://avatars.githubusercontent.com/u/38249783?v=4', 'events_url': 'https://api.github.com/users/mariamabarham/events{/privacy}', 'followers_url': 'https://api.github.com/users/mariamabarham/followers', 'following_url': 'https://api.github.com/users/mariamabarham/following{/other_user}', 'gists_url': 'https://api.github.com/users/mariamabarham/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/mariamabarham', 'id': 38249783, 'login': 'mariamabarham', 'node_id': 'MDQ6VXNlcjM4MjQ5Nzgz', 'organizations_url': 'https://api.github.com/users/mariamabarham/orgs', 'received_events_url': 'https://api.github.com/users/mariamabarham/received_events', 'repos_url': 'https://api.github.com/users/mariamabarham/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/mariamabarham/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/mariamabarham/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/mariamabarham'} | [] | closed | false | null | [] | null | ['The dummy data for v2.1 is missing as far as I can see. I think running the dummy data command should work correctly here. '
'Also, it might be that the structure of the dummy data is wrong - looking at `generate_examples` the structure does not look too easy.'
"The fact that the dummy data for v2.1 is missing shouldn't make the test fails I think. But as you mention the dummy data structure of v1.1 is wrong. I tried to rename files but it does not solve the issue."
'Is MS mARCO added to nlp library?I am not able to view it?'
'> Is MS mARCO added to nlp library?I am not able to view it?\r\n\r\nHi @parthplc ,the PR is not merged yet. The dummy data structure is still failing. Maybe @patrickvonplaten can help with it.'
'Dataset is fixed and should be ready for use. @mariamabarham @lhoestq feel free to merge whenever!'
'> Dataset is fixed and should be ready for use. @mariamabarham @lhoestq feel free to merge whenever!\r\n\r\nthanks'] | 2020-07-09 07:11:19+00:00 | 2020-08-06 06:15:49+00:00 | 2020-08-06 06:15:48+00:00 | CONTRIBUTOR | null | This PR adds the MS MARCO dataset as requested in this issue #336. MS mARCO has multiple task including:
- Passage and Document Retrieval
- Keyphrase Extraction
- QA and NLG
This PR only adds the 2 versions of the QA and NLG task dataset which was realeased with the original paper here https://arxiv.org/pdf/1611.09268.pdf
Tests are failing because of the dummy data. I tried to fix it without success. Can you please have a look at it? @patrickvonplaten , @lhoestq | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/364/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/364/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/364.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/364', 'merged_at': '2020-08-06T06:15:48Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/364.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/364'} | true |
https://api.github.com/repos/huggingface/datasets/issues/363 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/363/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/363/comments | https://api.github.com/repos/huggingface/datasets/issues/363/events | https://github.com/huggingface/datasets/pull/363 | 653,821,172 | MDExOlB1bGxSZXF1ZXN0NDQ2NjY0NDIy | 363 | Adding support for generic multi dimensional tensors and auxillary image data for multimodal datasets | {'avatar_url': 'https://avatars.githubusercontent.com/u/14030663?v=4', 'events_url': 'https://api.github.com/users/eltoto1219/events{/privacy}', 'followers_url': 'https://api.github.com/users/eltoto1219/followers', 'following_url': 'https://api.github.com/users/eltoto1219/following{/other_user}', 'gists_url': 'https://api.github.com/users/eltoto1219/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/eltoto1219', 'id': 14030663, 'login': 'eltoto1219', 'node_id': 'MDQ6VXNlcjE0MDMwNjYz', 'organizations_url': 'https://api.github.com/users/eltoto1219/orgs', 'received_events_url': 'https://api.github.com/users/eltoto1219/received_events', 'repos_url': 'https://api.github.com/users/eltoto1219/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/eltoto1219/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/eltoto1219/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/eltoto1219'} | [] | closed | false | null | [] | null | ["Thank you! I just marked this as a draft PR. It probably would be better to create specific Array2D and Array3D classes as needed instead of a generic MultiArray for now, it should simplify the code a lot too so, I'll update it as such. Also i was meaning to reply earlier, but I wanted to thank you for the testing script you sent me earlier since it ended up being tremendously helpful. "
'Okay, I just converted the MultiArray class to Array2D, and got rid of all those "globals()"! \r\n\r\nThe main issues I had were that when including a "pa.ExtensionType" as a column, the ordinary methods to batch the data would not work and it would throw me some mysterious error, so I first cleaned up my code to order the row to match the schema (because when including extension types the row is disordered ) and then made each row a pa.Table and then concatenated all the tables. Also each n-dimensional vector class we implement will be size invariant which is some good news. '
'Okay awesome! I just added your suggestions and changed up my recursive functions. \r\n\r\nHere is the traceback for the when I use the original code in the write_on_file method:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File "<stdin>", line 33, in <module>\r\n File "/home/eltoto/nlp/src/nlp/arrow_writer.py", line 214, in finalize\r\n self.write_on_file()\r\n File "/home/eltoto/nlp/src/nlp/arrow_writer.py", line 134, in write_on_file\r\n pa_array = pa.array(self.current_rows, type=self._type)\r\n File "pyarrow/array.pxi", line 269, in pyarrow.lib.array\r\n File "pyarrow/array.pxi", line 38, in pyarrow.lib._sequence_to_array\r\n File "pyarrow/error.pxi", line 106, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowNotImplementedError: MakeBuilder: cannot construct builder for type extension<arrow.py_extension_type>\r\n\r\nshell returned 1\r\n```\r\n\r\nI think when trying to cast an extension array within a list of dictionaries, some method gets called that bugs out Arrow and somehow doesn\'t get called when adding a single row to a a table and then appending multiple tables together. I tinkered with this for a while but could not find any workaround. \r\n\r\nIn the case that this new method causes bad compression/worse performance, we can explicitly set the batch size in the pa.Table.to_batches(***batch_size***) method, which will return a list of batches. Perhaps, we can check that the batch size is not too large converting the table to batches after X many rows are appended to it by following the batch_size check below.'
"> I think when trying to cast an extension array within a list of dictionaries, some method gets called that bugs out Arrow and somehow doesn't get called when adding a single row to a a table and then appending multiple tables together. I tinkered with this for a while but could not find any workaround.\r\n\r\nIndeed that's weird.\r\n\r\n> In the case that this new method causes bad compression/worse performance, we can explicitly set the batch size in the pa.Table.to_batches(batch_size) method, which will return a list of batches. Perhaps, we can check that the batch size is not too large converting the table to batches after X many rows are appended to it by following the batch_size check below.\r\n\r\nThe argument of `pa.Table.to_batches` is not `batch_size` but `max_chunksize`, which means that right now it would have no effects (each chunk is of length 1).\r\n\r\nWe can fix that just by doing `entries.combine_chunks().to_batches(batch_size)`. In that case it would write by chunk of 1000 which is what we want. I don't think it will slow down the writing by much, but we may have to do a benchmark just to make sure. If speed is ok we could even replace the original code to always write chunks this way.\r\n\r\nDo you still have errors that need to be fixed ?"
'@lhoestq Nope all should be good! \r\n\r\nWould you like me to add the entries.combine_chunks().to_batch_size() code + benchmark?'
'> @lhoestq Nope all should be good!\r\n\r\nAwesome :)\r\n\r\nI think it would be good to start to add some tests then.\r\nYou already have `test_multi_array.py` which is a good start, maybe you can place it in /tests and make it a `unittest.TestCase` ?\r\n\r\n> Would you like me to add the entries.combine_chunks().to_batch_size() code + benchmark?\r\n\r\nThat would be interesting. We don\'t want reading/writing to be the bottleneck of dataset processing for example in terms of speed. Maybe we could test the write + read speed of different datasets:\r\n- write speed + read speed a dataset with `nlp.Array2D` features\r\n- write speed + read speed a dataset with `nlp.Sequence(nlp.Sequence(nlp.Value("float32")))` features\r\n- write speed + read speed a dataset with `nlp.Sequence(nlp.Value("float32"))` features (same data but flatten)\r\nIt will be interesting to see the influence of `.combine_chunks()` on the `Array2D` test too.\r\n\r\nWhat do you think ?'
"Well actually it looks like we're still having the `print(dataset[0])` error no ?"
'I just tested your code to try to understand better.\r\n\r\n\r\n- First thing you must know is that we\'ve switched from `dataset._data.to_pandas` to `dataset._data.to_pydict` by default when we call `dataset[0]` in #423 . Right now it raises an error but it can be fixed by adding this method to `ExtensionArray2D`:\r\n\r\n```python\r\n def to_pylist(self):\r\n return self.to_numpy().tolist()\r\n```\r\n\r\n- Second, I noticed that `ExtensionArray2D.to_numpy()` always return a (5, 5) shape in your example. I thought `ExtensionArray` was for possibly multiple examples and so I was expecting a shape like (1, 5, 5) for example. Did I miss something ?\r\nTherefore when I apply the fix I mentioned (adding to_pylist), it returns one example per row in each image (in your example of 2 images of shape 5x5, I get `len(dataset._data.to_pydict()["image"]) == 10 # True`)\r\n\r\n[EDIT] I changed the reshape step in `ExtensionArray2D.to_numpy()` by\r\n```python\r\nnumpy_arr = numpy_arr.reshape(len(self), *ExtensionArray2D._construct_shape(self.storage))\r\n```\r\nand it did the job: `len(dataset._data.to_pydict()["image"]) == 2 # True`\r\n\r\n- Finally, I was able to make `to_pandas` work though, by implementing custom array dtype in pandas with arrow conversion (I got inspiration from [here](https://gist.github.com/Eastsun/a59fb0438f65e8643cd61d8c98ec4c08) and [here](https://pandas.pydata.org/pandas-docs/version/1.0.0/development/extending.html#compatibility-with-apache-arrow))\r\n\r\nMaybe you could add me in your repo so I can open a PR to add these changes to your branch ?'
"`combine_chunks` doesn't seem to work btw:\r\n`ArrowNotImplementedError: concatenation of extension<arrow.py_extension_type>`"
'> > @lhoestq Nope all should be good!\r\n> \r\n> Awesome :)\r\n> \r\n> I think it would be good to start to add some tests then.\r\n> You already have `test_multi_array.py` which is a good start, maybe you can place it in /tests and make it a `unittest.TestCase` ?\r\n> \r\n> > Would you like me to add the entries.combine_chunks().to_batch_size() code + benchmark?\r\n> \r\n> That would be interesting. We don\'t want reading/writing to be the bottleneck of dataset processing for example in terms of speed. Maybe we could test the write + read speed of different datasets:\r\n> \r\n> * write speed + read speed a dataset with `nlp.Array2D` features\r\n> * write speed + read speed a dataset with `nlp.Sequence(nlp.Sequence(nlp.Value("float32")))` features\r\n> * write speed + read speed a dataset with `nlp.Sequence(nlp.Value("float32"))` features (same data but flatten)\r\n> It will be interesting to see the influence of `.combine_chunks()` on the `Array2D` test too.\r\n> \r\n> What do you think ?\r\n\r\nYa! that should be no problem at all, Ill use the timeit module and get back to you with the results sometime over the weekend.'
'Thank you for all your help getting the pandas and row indexing for the dataset to work! For `print(dataset[0])`, I considered the workaround of doing `print(dataset["col_name"][0])` a temporary solution, but ya, I was never able to figure out how to previously get it to work. I\'ll add you to my repo right now, let me know if you do not see the invite. Also lastly, it is strange how the to_batches method is not working, so I can check that out while I add some speed tests + add the multi dim test under the unit tests this weekend. '
'I created the PR :)\r\nI also tested `to_batches` and it works on my side'
'Sorry for the bit of delay! I just added the tests, the PR into my fork, and some speed tests. It should be fairly easy to add more tests if we need. Do you think there is anything else to checkout?'
"Cool thanks for adding the tests :) \r\n\r\nNext step is merge master into this branch.\r\nNot sure I understand what you did in your last commit, but it looks like you discarded all the changes from master ^^'\r\n\r\nWe've done some changes in the features logic on master, so let me know if you need help merging it.\r\n\r\nAs soon as we've merged from master, we'll have to make sure that we have extensive tests and we'll be good to do !\r\nAbout the lxmert dataset, we can probably keep it for another PR as soon as we have working 2d features. What do you think ?"
"We might want to merge this after tomorrow's release though to avoid potential side effects @lhoestq "
"Yep I'm sure we can have it not for tomorrow's release but for the next one ;)"
'haha, when I tried to rebase, I ran into some conflicts. In that last commit, I restored the features.py from the previous commit on the branch in my fork because upon updating to master, the pandasdtypemanger and pandas extension types disappeared. If you actually could help me with merging in what is needed, that would actually help a lot. \r\n\r\nOther than that, ya let me go ahead and move the dataloader code out of this PR. Perhaps we could discuss in the slack channelk soon about what to do with that because we can either just support the pretraining corpus for lxmert or try to implement the full COCO and visual genome datasets (+VQA +GQA) which im sure people would be pretty happy about. \r\n\r\nAlso we can talk more tests soon too when you are free. \r\n\r\nGoodluck on the release tomorrow guys!'
"Not sure why github thinks there are conflicts here, as I just rebased from the current master branch.\r\nMerging into master locally works on my side without conflicts\r\n```\r\ngit checkout master\r\ngit reset --hard origin/master\r\ngit merge --no-ff eltoto1219/support_multi_dim_tensors_for_images\r\nMerge made by the 'recursive' strategy.\r\n datasets/lxmert_pretraining_beta/lxmert_pretraining_beta.py | 89 +++++++++++++++++++++++++++++++++++++\r\n datasets/lxmert_pretraining_beta/test_multi_array.py | 45 +++++++++++++++++++\r\n datasets/lxmert_pretraining_beta/to_arrow_data.py | 371 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n src/nlp/arrow_dataset.py | 24 +++++-----\r\n src/nlp/arrow_writer.py | 22 ++++++++--\r\n src/nlp/features.py | 229 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++---\r\n tests/test_array_2d.py | 210 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\r\n 7 files changed, 969 insertions(+), 21 deletions(-)\r\n create mode 100644 datasets/lxmert_pretraining_beta/lxmert_pretraining_beta.py\r\n create mode 100644 datasets/lxmert_pretraining_beta/test_multi_array.py\r\n create mode 100644 datasets/lxmert_pretraining_beta/to_arrow_data.py\r\n create mode 100644 tests/test_array_2d.py\r\n```"
"I put everything inside one commit from the master branch but the merge conflicts on github'side were still there for some reason.\r\nClosing and re-opening the PR fixed the conflict check on github's side."
'Almost done ! It still needs a pass on the docs/comments and maybe a few more tests.\r\n\r\nI had to do several changes for type inference in the ArrowWriter to make it support custom types.'
'Ok this is now ready for review ! Thanks for your awesome work in this @eltoto1219 \r\n\r\nSummary of the changes:\r\n- added new feature type `Array2D`, that can be instantiated like `Array2D("float32")` for example\r\n- added pyarrow extension type `Array2DExtensionType` and array `Array2DExtensionArray` that take care of converting from and to arrow. `Array2DExtensionType`\'s storage is a list of list of any pyarrow array.\r\n- added pandas extension type `PandasArrayExtensionType` and array `PandasArrayExtensionArray` for conversion from and to arrow/python objects\r\n- refactor of the `ArrowWriter` write and write_batch functions to support extension types while preserving the type inference behavior.\r\n- added a utility object `TypedSequence` that is helpful to combine extension arrays and type inference inside the writer\'s methods.\r\n- added speed test for sequences writing (printed as warnings in pytest)\r\n- breaking: set disable_nullable to False by default as pyarrow\'s type inference returns nullable fields\r\n\r\nAnd there are plenty of new tests, mainly in `test_array2d.py` and `test_arrow_writer.py`.\r\n\r\nNote that there are some collisions in `arrow_dataset.py` with #513 so let\'s be careful when we\'ll merge this one.\r\n\r\nI know this is a big PR so feel free to ask questions'
"I'll add Array3D, 4D.. tomorrow but it should take only a few lines. The rest won't change"
'I took your comments into account and I added Array[3-5]D.\r\nI changed the storage type to fixed lengths lists. I had to update the `to_numpy` function because of that. Indeed slicing a FixedLengthListArray returns a view a of the original array, while in the previous case slicing a ListArray copies the storage.\r\n'] | 2020-07-09 07:10:30+00:00 | 2020-08-24 09:59:35+00:00 | 2020-08-24 09:59:35+00:00 | CONTRIBUTOR | null | nlp/features.py:
The main factory class is MultiArray, every single time this class is called, a corresponding pyarrow extension array and type class is generated (and added to the list of globals for future use) for a given root data type and set of dimensions/shape. I provide examples on working with this in datasets/lxmert_pretraining_beta/test_multi_array.py
src/nlp/arrow_writer.py
I had to add a method for writing batches that include extension array types because despite having a unique class for each multidimensional array shape, pyarrow is unable to write any other "array-like" data class to a batch object unless it is of the type pyarrow.ExtensionType. The problem in this is that when writing multiple batches, the order of the schema and data to be written get mixed up (where the pyarrow datatype in the schema only refers to as ExtensionAray, but each ExtensionArray subclass has a different shape) ... possibly I am missing something here and would be grateful if anyone else could take a look!
datasets/lxmert_pretraining_beta/lxmert_pretraining_beta.py & datasets/lxmert_pretraining_beta/to_arrow_data.py:
I have begun adding the data from the original LXMERT paper (https://arxiv.org/abs/1908.07490) hosted here: (https://github.com/airsplay/lxmert). The reason I am not pulling from the source of truth for each individual dataset is because it seems that there will also need to be functionality to aggregate multimodal datasets to create a pre-training corpus (:sleepy: ).
For now, this is just being used to test and run edge-cases for the MultiArray feature, so ive labeled it as "beta_pretraining"!
(still working on the pretraining, just wanted to push out the new functionality sooner than later) | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/363/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/363/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/363.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/363', 'merged_at': '2020-08-24T09:59:35Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/363.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/363'} | true |
https://api.github.com/repos/huggingface/datasets/issues/362 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/362/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/362/comments | https://api.github.com/repos/huggingface/datasets/issues/362/events | https://github.com/huggingface/datasets/issues/362 | 653,766,245 | MDU6SXNzdWU2NTM3NjYyNDU= | 362 | [dateset subset missing] xtreme paws-x | {'avatar_url': 'https://avatars.githubusercontent.com/u/50871412?v=4', 'events_url': 'https://api.github.com/users/jerryIsHere/events{/privacy}', 'followers_url': 'https://api.github.com/users/jerryIsHere/followers', 'following_url': 'https://api.github.com/users/jerryIsHere/following{/other_user}', 'gists_url': 'https://api.github.com/users/jerryIsHere/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/jerryIsHere', 'id': 50871412, 'login': 'jerryIsHere', 'node_id': 'MDQ6VXNlcjUwODcxNDEy', 'organizations_url': 'https://api.github.com/users/jerryIsHere/orgs', 'received_events_url': 'https://api.github.com/users/jerryIsHere/received_events', 'repos_url': 'https://api.github.com/users/jerryIsHere/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/jerryIsHere/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/jerryIsHere/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/jerryIsHere'} | [] | closed | false | null | [] | null | ["You're right, thanks for pointing it out. We will update it "] | 2020-07-09 05:04:54+00:00 | 2020-07-09 12:38:42+00:00 | 2020-07-09 12:38:42+00:00 | CONTRIBUTOR | null | I tried nlp.load_dataset('xtreme', 'PAWS-X.es') but get the value error
It turns out that the subset for Spanish is missing
https://github.com/google-research-datasets/paws/tree/master/pawsx | {'+1': 1, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 1, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/362/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/362/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/361 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/361/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/361/comments | https://api.github.com/repos/huggingface/datasets/issues/361/events | https://github.com/huggingface/datasets/issues/361 | 653,757,376 | MDU6SXNzdWU2NTM3NTczNzY= | 361 | 🐛 [Metrics] ROUGE is non-deterministic | {'avatar_url': 'https://avatars.githubusercontent.com/u/43774355?v=4', 'events_url': 'https://api.github.com/users/astariul/events{/privacy}', 'followers_url': 'https://api.github.com/users/astariul/followers', 'following_url': 'https://api.github.com/users/astariul/following{/other_user}', 'gists_url': 'https://api.github.com/users/astariul/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/astariul', 'id': 43774355, 'login': 'astariul', 'node_id': 'MDQ6VXNlcjQzNzc0MzU1', 'organizations_url': 'https://api.github.com/users/astariul/orgs', 'received_events_url': 'https://api.github.com/users/astariul/received_events', 'repos_url': 'https://api.github.com/users/astariul/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/astariul/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/astariul/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/astariul'} | [] | closed | false | null | [] | null | ['Hi, can you give a full self-contained example to reproduce this behavior?'
'> Hi, can you give a full self-contained example to reproduce this behavior?\r\n\r\nThere is a notebook in the post ;)'
'> If I run the ROUGE metric 2 times, with same predictions / references, the scores are slightly different.\r\n> \r\n> Refer to [this Colab notebook](https://colab.research.google.com/drive/1wRssNXgb9ldcp4ulwj-hMJn0ywhDOiDy?usp=sharing) for reproducing the problem.\r\n> \r\n> Example of F-score for ROUGE-1, ROUGE-2, ROUGE-L in 2 differents run :\r\n> \r\n> > [\'0.3350\', \'0.1470\', \'0.2329\']\r\n> > [\'0.3358\', \'0.1451\', \'0.2332\']\r\n> \r\n> Why ROUGE is not deterministic ?\r\n\r\nThis is because of rouge\'s `BootstrapAggregator` that uses sampling to get confidence intervals (low, mid, high).\r\nYou can get deterministic scores per sentence pair by using\r\n```python\r\nscore = rouge.compute(rouge_types=["rouge1", "rouge2", "rougeL"], use_aggregator=False)\r\n```\r\nOr you can set numpy\'s random seed if you still want to use the aggregator.'
'Maybe we can set all the random seeds of numpy/torch etc. while running `metric.compute` ?'
'We should probably indeed!'
"Now if you re-run the notebook, the two printed results are the same @colanim\r\n```\r\n['0.3356', '0.1466', '0.2318']\r\n['0.3356', '0.1466', '0.2318']\r\n```\r\nHowever across sessions, the results may change (as numpy's random seed can be different). You can prevent that by setting your seed:\r\n```python\r\nrouge = nlp.load_metric('rouge', seed=42)\r\n```"
'> \r\n\r\nMinor nit: Note that "aggregator" is misspelled in this command. Should be `use_aggregator=False`. '
'Thanks, I fixed the code snippet'] | 2020-07-09 04:39:37+00:00 | 2022-09-09 15:20:55+00:00 | 2020-07-20 23:48:37+00:00 | NONE | null | If I run the ROUGE metric 2 times, with same predictions / references, the scores are slightly different.
Refer to [this Colab notebook](https://colab.research.google.com/drive/1wRssNXgb9ldcp4ulwj-hMJn0ywhDOiDy?usp=sharing) for reproducing the problem.
Example of F-score for ROUGE-1, ROUGE-2, ROUGE-L in 2 differents run :
> ['0.3350', '0.1470', '0.2329']
['0.3358', '0.1451', '0.2332']
---
Why ROUGE is not deterministic ? | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/361/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/361/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/360 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/360/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/360/comments | https://api.github.com/repos/huggingface/datasets/issues/360/events | https://github.com/huggingface/datasets/issues/360 | 653,687,176 | MDU6SXNzdWU2NTM2ODcxNzY= | 360 | [Feature request] Add dataset.ragged_map() function for many-to-many transformations | {'avatar_url': 'https://avatars.githubusercontent.com/u/4564897?v=4', 'events_url': 'https://api.github.com/users/jarednielsen/events{/privacy}', 'followers_url': 'https://api.github.com/users/jarednielsen/followers', 'following_url': 'https://api.github.com/users/jarednielsen/following{/other_user}', 'gists_url': 'https://api.github.com/users/jarednielsen/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/jarednielsen', 'id': 4564897, 'login': 'jarednielsen', 'node_id': 'MDQ6VXNlcjQ1NjQ4OTc=', 'organizations_url': 'https://api.github.com/users/jarednielsen/orgs', 'received_events_url': 'https://api.github.com/users/jarednielsen/received_events', 'repos_url': 'https://api.github.com/users/jarednielsen/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/jarednielsen/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/jarednielsen/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/jarednielsen'} | [] | closed | false | null | [] | null | ["Actually `map(batched=True)` can already change the size of the dataset.\r\nIt can accept examples of length `N` and returns a batch of length `M` (can be null or greater than `N`).\r\n\r\nI'll make that explicit in the doc that I'm currently writing."
'You\'re two steps ahead of me :) In my testing, it also works if `M` < `N`.\r\n\r\nA batched map of different length seems to work if you directly overwrite all of the original keys, but fails if any of the original keys are preserved.\r\n\r\nFor example,\r\n```python\r\n# Create a dummy dataset\r\ndset = load_dataset("wikitext", "wikitext-2-raw-v1")["test"]\r\ndset = dset.map(lambda ex: {"length": len(ex["text"]), "foo": 1})\r\n\r\n# Do an allreduce on each batch, overwriting both keys\r\ndset.map(lambda batch: {"length": [sum(batch["length"])], "foo": [1]})\r\n# Dataset(schema: {\'length\': \'int64\', \'foo\': \'int64\'}, num_rows: 5)\r\n\r\n# Now attempt an allreduce without touching the `foo` key\r\ndset.map(lambda batch: {"length": [sum(batch["length"])]})\r\n# This fails with the error message below\r\n```\r\n\r\n```bash\r\n File "/path/to/nlp/src/nlp/arrow_dataset.py", line 728, in map\r\n arrow_schema = pa.Table.from_pydict(test_output).schema\r\n File "pyarrow/io.pxi", line 1532, in pyarrow.lib.Codec.detect\r\n File "pyarrow/table.pxi", line 1503, in pyarrow.lib.Table.from_arrays\r\n File "pyarrow/public-api.pxi", line 390, in pyarrow.lib.pyarrow_wrap_table\r\n File "pyarrow/error.pxi", line 85, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: Column 1 named foo expected length 1 but got length 2\r\n```\r\n\r\nAdding the `remove_columns=["length", "foo"]` argument to `map()` solves the issue. Leaving the above error for future visitors. Perfect, thank you!'] | 2020-07-09 01:04:43+00:00 | 2020-07-09 19:31:51+00:00 | 2020-07-09 19:31:51+00:00 | CONTRIBUTOR | null | `dataset.map()` enables one-to-one transformations. Input one example and output one example. This is helpful for tokenizing and cleaning individual lines.
`dataset.filter()` enables one-to-(one-or-none) transformations. Input one example and output either zero/one example. This is helpful for removing portions from the dataset.
However, some dataset transformations are many-to-many. Consider constructing BERT training examples from a dataset of sentences, where you map `["a", "b", "c"] -> ["a[SEP]b", "a[SEP]c", "b[SEP]c", "c[SEP]b", ...]`
I propose a more general `ragged_map()` method that takes in a batch of examples of length `N` and return a batch of examples `M`. This is different from the `map(batched=True)` method, which takes examples of length `N` and returns a batch of length `N`, processing individual examples in parallel. I don't have a clear vision of how this would be implemented efficiently and lazily, but would love to hear the community's feedback on this.
My specific use case is creating an end-to-end ELECTRA data pipeline. I would like to take the raw WikiText data and generate training examples from this using the `ragged_map()` method, then export to TFRecords and train quickly. This would be a reproducible pipeline with no bash scripts. Currently I'm relying on scripts like https://github.com/google-research/electra/blob/master/build_pretraining_dataset.py, which are less general.
| {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/360/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/360/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/359 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/359/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/359/comments | https://api.github.com/repos/huggingface/datasets/issues/359/events | https://github.com/huggingface/datasets/issues/359 | 653,656,279 | MDU6SXNzdWU2NTM2NTYyNzk= | 359 | ArrowBasedBuilder _prepare_split parse_schema breaks on nested structures | {'avatar_url': 'https://avatars.githubusercontent.com/u/2000204?v=4', 'events_url': 'https://api.github.com/users/timothyjlaurent/events{/privacy}', 'followers_url': 'https://api.github.com/users/timothyjlaurent/followers', 'following_url': 'https://api.github.com/users/timothyjlaurent/following{/other_user}', 'gists_url': 'https://api.github.com/users/timothyjlaurent/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/timothyjlaurent', 'id': 2000204, 'login': 'timothyjlaurent', 'node_id': 'MDQ6VXNlcjIwMDAyMDQ=', 'organizations_url': 'https://api.github.com/users/timothyjlaurent/orgs', 'received_events_url': 'https://api.github.com/users/timothyjlaurent/received_events', 'repos_url': 'https://api.github.com/users/timothyjlaurent/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/timothyjlaurent/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/timothyjlaurent/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/timothyjlaurent'} | [] | closed | false | null | [] | null | ['Hi, it depends on what it is in your `dataset_builder.py` file. Can you share it?\r\n\r\nIf you are just loading `json` files, you can also directly use the `json` script (which will find the schema/features from your JSON structure):\r\n\r\n```python\r\nfrom nlp import load_dataset\r\nds = load_dataset("json", data_files=rel_datafiles)\r\n```'
'The behavior I\'m seeing is from the `json` script. \r\nI hacked this together to overcome the error with the `JSON` dataloader\r\n\r\n```\r\nclass DatasetBuilder(hf_nlp.ArrowBasedBuilder):\r\n BUILDER_CONFIG_CLASS = BuilderConfig\r\n\r\n def _info(self):\r\n return DatasetInfo()\r\n\r\n def _split_generators(self, dl_manager):\r\n """ We handle string, list and dicts in datafiles\r\n """\r\n if isinstance(self.config.data_files, (str, list, tuple)):\r\n files = self.config.data_files\r\n if isinstance(files, str):\r\n files = [files]\r\n return [SplitGenerator(name=Split.TRAIN, gen_kwargs={"files": files})]\r\n splits = []\r\n for split_name in [Split.TRAIN, Split.VALIDATION, Split.TEST]:\r\n if split_name in self.config.data_files:\r\n files = self.config.data_files[split_name]\r\n if isinstance(files, str):\r\n files = [files]\r\n splits.append(SplitGenerator(name=split_name, gen_kwargs={"files": files}))\r\n return splits\r\n\r\n def _prepare_split(self, split_generator):\r\n fname = "{}-{}.arrow".format(self.name, split_generator.name)\r\n fpath = os.path.join(self._cache_dir, fname)\r\n\r\n writer = ArrowWriter(path=fpath)\r\n\r\n generator = self._generate_tables(**split_generator.gen_kwargs)\r\n for key, table in utils.tqdm(generator, unit=" tables", leave=False):\r\n writer.write_table(table)\r\n num_examples, num_bytes = writer.finalize()\r\n\r\n split_generator.split_info.num_examples = num_examples\r\n split_generator.split_info.num_bytes = num_bytes\r\n # this is where the error is coming from\r\n # def parse_schema(schema, schema_dict):\r\n # for field in schema:\r\n # if pa.types.is_struct(field.type):\r\n # schema_dict[field.name] = {}\r\n # parse_schema(field.type, schema_dict[field.name])\r\n # elif pa.types.is_list(field.type) and pa.types.is_struct(field.type.value_type):\r\n # schema_dict[field.name] = {}\r\n # parse_schema(field.type.value_type, schema_dict[field.name])\r\n # else:\r\n # schema_dict[field.name] = Value(str(field.type))\r\n # \r\n # parse_schema(writer.schema, features)\r\n # self.info.features = Features(features)\r\n\r\n def _generate_tables(self, files):\r\n for i, file in enumerate(files):\r\n pa_table = paj.read_json(\r\n file\r\n )\r\n yield i, pa_table\r\n```\r\n\r\nSo I basically just don\'t populate the `self.info.features` though this doesn\'t seem to cause any problems in my downstream applications. \r\n\r\nThe other workaround I was doing was to just use pyarrow.json to build a table and then to create the Dataset with its constructor or from_table methods. `load_dataset` has nice split logic, so I\'d prefer to use that.\r\n\r\n'
"Also noticed that if you for example in a loader script\r\n\r\n```\r\nfrom nlp import ArrowBasedBuilder\r\n\r\nclass MyBuilder(ArrowBasedBuilder):\r\n...\r\n\r\n```\r\nand use that in the subclass, it will be on the module's __dict__ and will be selected before the `MyBuilder` subclass, and it will raise `NotImplementedError` on its `_generate_examples` method... In the code it check for abstract classes but Builder and ArrowBasedBuilder aren't abstract classes, they're regular classes with `@abstract_methods`."
'Indeed this is part of a more general limitation which is the fact that we should generate and update the `features` from the auto-inferred Arrow schema when they are not provided (also happen when a user change the schema using `map()`, the features should be auto-generated and guessed as much as possible to keep the `features` synced with the underlying Arrow table schema).\r\n\r\nWe will try to solve this soon.'] | 2020-07-08 23:24:05+00:00 | 2020-07-10 14:52:06+00:00 | 2020-07-10 14:52:06+00:00 | NONE | null | I tried using the Json dataloader to load some JSON lines files. but get an exception in the parse_schema function.
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-23-9aecfbee53bd> in <module>
55 from nlp import load_dataset
56
---> 57 ds = load_dataset("../text2struct/model/dataset_builder.py", data_files=rel_datafiles)
58
59
~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
522 download_mode=download_mode,
523 ignore_verifications=ignore_verifications,
--> 524 save_infos=save_infos,
525 )
526
~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
430 verify_infos = not save_infos and not ignore_verifications
431 self._download_and_prepare(
--> 432 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
433 )
434 # Sync info
~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
481 try:
482 # Prepare split will record examples associated to the split
--> 483 self._prepare_split(split_generator, **prepare_split_kwargs)
484 except OSError:
485 raise OSError("Cannot find data file. " + (self.manual_download_instructions or ""))
~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/builder.py in _prepare_split(self, split_generator)
736 schema_dict[field.name] = Value(str(field.type))
737
--> 738 parse_schema(writer.schema, features)
739 self.info.features = Features(features)
740
~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/builder.py in parse_schema(schema, schema_dict)
734 parse_schema(field.type.value_type, schema_dict[field.name])
735 else:
--> 736 schema_dict[field.name] = Value(str(field.type))
737
738 parse_schema(writer.schema, features)
<string> in __init__(self, dtype, id, _type)
~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/features.py in __post_init__(self)
55
56 def __post_init__(self):
---> 57 self.pa_type = string_to_arrow(self.dtype)
58
59 def __call__(self):
~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/features.py in string_to_arrow(type_str)
32 if str(type_str + "_") not in pa.__dict__:
33 raise ValueError(
---> 34 f"Neither {type_str} nor {type_str + '_'} seems to be a pyarrow data type. "
35 f"Please make sure to use a correct data type, see: "
36 f"https://arrow.apache.org/docs/python/api/datatypes.html#factory-functions"
ValueError: Neither list<item: string> nor list<item: string>_ seems to be a pyarrow data type. Please make sure to use a correct data type, see: https://arrow.apache.org/docs/python/api/datatypes.html#factory-functions
```
If I create the dataset imperatively, using a pyarrow table, the dataset is created correctly. If I override the `_prepare_split` method to avoid calling the validate schema, the dataset can load as well. | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/359/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/359/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/358 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/358/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/358/comments | https://api.github.com/repos/huggingface/datasets/issues/358/events | https://github.com/huggingface/datasets/pull/358 | 653,645,121 | MDExOlB1bGxSZXF1ZXN0NDQ2NTI0NjQ5 | 358 | Starting to add some real doc | {'avatar_url': 'https://avatars.githubusercontent.com/u/7353373?v=4', 'events_url': 'https://api.github.com/users/thomwolf/events{/privacy}', 'followers_url': 'https://api.github.com/users/thomwolf/followers', 'following_url': 'https://api.github.com/users/thomwolf/following{/other_user}', 'gists_url': 'https://api.github.com/users/thomwolf/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/thomwolf', 'id': 7353373, 'login': 'thomwolf', 'node_id': 'MDQ6VXNlcjczNTMzNzM=', 'organizations_url': 'https://api.github.com/users/thomwolf/orgs', 'received_events_url': 'https://api.github.com/users/thomwolf/received_events', 'repos_url': 'https://api.github.com/users/thomwolf/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/thomwolf/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/thomwolf/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/thomwolf'} | [] | closed | false | null | [] | null | ["Ok this is starting to be really big so it's probably good to merge this first version of the doc and continue in another PR :)\r\n\r\nThis first version of the doc can be explored here: https://2219-250213286-gh.circle-artifacts.com/0/docs/_build/html/index.html"] | 2020-07-08 22:53:03+00:00 | 2020-07-14 09:58:17+00:00 | 2020-07-14 09:58:15+00:00 | MEMBER | null | Adding a lot of documentation for:
- load a dataset
- explore the dataset object
- process data with the dataset
- add a new dataset script
- share a dataset script
- full package reference
This version of the doc can be explored here: https://2219-250213286-gh.circle-artifacts.com/0/docs/_build/html/index.html
Also:
- fix a bug in `train_test_split`
- update the `csv` script
- add a verbose argument to the dataset processing methods
Still missing:
- doc for the metrics
- how to directly upload a community provided dataset with the CLI
- clean up more docstrings
- add the `features` argument to `load_dataset` (should be another PR) | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/358/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/358/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/358.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/358', 'merged_at': '2020-07-14T09:58:15Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/358.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/358'} | true |
https://api.github.com/repos/huggingface/datasets/issues/357 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/357/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/357/comments | https://api.github.com/repos/huggingface/datasets/issues/357/events | https://github.com/huggingface/datasets/pull/357 | 653,642,292 | MDExOlB1bGxSZXF1ZXN0NDQ2NTIyMzU2 | 357 | Add hashes to cnn_dailymail | {'avatar_url': 'https://avatars.githubusercontent.com/u/2238344?v=4', 'events_url': 'https://api.github.com/users/jbragg/events{/privacy}', 'followers_url': 'https://api.github.com/users/jbragg/followers', 'following_url': 'https://api.github.com/users/jbragg/following{/other_user}', 'gists_url': 'https://api.github.com/users/jbragg/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/jbragg', 'id': 2238344, 'login': 'jbragg', 'node_id': 'MDQ6VXNlcjIyMzgzNDQ=', 'organizations_url': 'https://api.github.com/users/jbragg/orgs', 'received_events_url': 'https://api.github.com/users/jbragg/received_events', 'repos_url': 'https://api.github.com/users/jbragg/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/jbragg/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/jbragg/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/jbragg'} | [] | closed | false | null | [] | null | ['Looks you to me :)\r\n\r\nCould you also update the json file that goes with the dataset script by doing \r\n```\r\nnlp-cli test ./datasets/cnn_dailymail --save_infos --all_configs\r\n```\r\nIt will update the features metadata and the size of the dataset with your changes.'
'@lhoestq I ran that command.\r\n\r\nThanks for the helpful repository!'] | 2020-07-08 22:45:21+00:00 | 2020-07-13 14:16:38+00:00 | 2020-07-13 14:16:38+00:00 | CONTRIBUTOR | null | The URL hashes are helpful for comparing results from other sources. | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/357/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/357/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/357.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/357', 'merged_at': '2020-07-13T14:16:38Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/357.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/357'} | true |
https://api.github.com/repos/huggingface/datasets/issues/356 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/356/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/356/comments | https://api.github.com/repos/huggingface/datasets/issues/356/events | https://github.com/huggingface/datasets/pull/356 | 653,537,388 | MDExOlB1bGxSZXF1ZXN0NDQ2NDM3MDQ5 | 356 | Add text dataset | {'avatar_url': 'https://avatars.githubusercontent.com/u/4564897?v=4', 'events_url': 'https://api.github.com/users/jarednielsen/events{/privacy}', 'followers_url': 'https://api.github.com/users/jarednielsen/followers', 'following_url': 'https://api.github.com/users/jarednielsen/following{/other_user}', 'gists_url': 'https://api.github.com/users/jarednielsen/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/jarednielsen', 'id': 4564897, 'login': 'jarednielsen', 'node_id': 'MDQ6VXNlcjQ1NjQ4OTc=', 'organizations_url': 'https://api.github.com/users/jarednielsen/orgs', 'received_events_url': 'https://api.github.com/users/jarednielsen/received_events', 'repos_url': 'https://api.github.com/users/jarednielsen/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/jarednielsen/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/jarednielsen/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/jarednielsen'} | [] | closed | false | null | [] | null | [] | 2020-07-08 19:21:53+00:00 | 2020-07-10 14:19:03+00:00 | 2020-07-10 14:19:03+00:00 | CONTRIBUTOR | null | Usage:
```python
from nlp import load_dataset
dset = load_dataset("text", data_files="/path/to/file.txt")["train"]
```
I created a dummy_data.zip which contains three files: `train.txt`, `test.txt`, `dev.txt`. Each of these contains two lines. It passes
```bash
RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_text
```
but I would like a second set of eyes to ensure I did it right.
| {'+1': 2, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 1, 'hooray': 3, 'laugh': 0, 'rocket': 0, 'total_count': 6, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/356/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/356/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/356.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/356', 'merged_at': '2020-07-10T14:19:03Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/356.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/356'} | true |
https://api.github.com/repos/huggingface/datasets/issues/355 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/355/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/355/comments | https://api.github.com/repos/huggingface/datasets/issues/355/events | https://github.com/huggingface/datasets/issues/355 | 653,451,013 | MDU6SXNzdWU2NTM0NTEwMTM= | 355 | can't load SNLI dataset | {'avatar_url': 'https://avatars.githubusercontent.com/u/13238952?v=4', 'events_url': 'https://api.github.com/users/jxmorris12/events{/privacy}', 'followers_url': 'https://api.github.com/users/jxmorris12/followers', 'following_url': 'https://api.github.com/users/jxmorris12/following{/other_user}', 'gists_url': 'https://api.github.com/users/jxmorris12/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/jxmorris12', 'id': 13238952, 'login': 'jxmorris12', 'node_id': 'MDQ6VXNlcjEzMjM4OTUy', 'organizations_url': 'https://api.github.com/users/jxmorris12/orgs', 'received_events_url': 'https://api.github.com/users/jxmorris12/received_events', 'repos_url': 'https://api.github.com/users/jxmorris12/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/jxmorris12/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/jxmorris12/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/jxmorris12'} | [] | closed | false | null | [] | null | ["I just added the processed files of `snli` on our google storage, so that when you do `load_dataset` it can download the processed files from there :)\r\n\r\nWe are thinking about having available those processed files for more datasets in the future, because sometimes files aren't available (like for `snli`), or the download speed is too slow, or sometimes the files take time to be processed."
'Closing this one. Feel free to re-open if you have other questions :)'
'Thank you!'] | 2020-07-08 16:54:14+00:00 | 2020-07-18 05:15:57+00:00 | 2020-07-15 07:59:01+00:00 | CONTRIBUTOR | null | `nlp` seems to load `snli` from some URL based on nlp.stanford.edu. This subdomain is frequently down -- including right now, when I'd like to load `snli` in a Colab notebook, but can't.
Is there a plan to move these datasets to huggingface servers for a more stable solution?
Btw, here's the stack trace:
```
File "/content/nlp/src/nlp/builder.py", line 432, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/content/nlp/src/nlp/builder.py", line 466, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/content/nlp/src/nlp/datasets/snli/e417f6f2e16254938d977a17ed32f3998f5b23e4fcab0f6eb1d28784f23ea60d/snli.py", line 76, in _split_generators
dl_dir = dl_manager.download_and_extract(_DATA_URL)
File "/content/nlp/src/nlp/utils/download_manager.py", line 217, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/content/nlp/src/nlp/utils/download_manager.py", line 156, in download
lambda url: cached_path(url, download_config=self._download_config,), url_or_urls,
File "/content/nlp/src/nlp/utils/py_utils.py", line 190, in map_nested
return function(data_struct)
File "/content/nlp/src/nlp/utils/download_manager.py", line 156, in <lambda>
lambda url: cached_path(url, download_config=self._download_config,), url_or_urls,
File "/content/nlp/src/nlp/utils/file_utils.py", line 198, in cached_path
local_files_only=download_config.local_files_only,
File "/content/nlp/src/nlp/utils/file_utils.py", line 356, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://nlp.stanford.edu/projects/snli/snli_1.0.zip
``` | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/355/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/355/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/354 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/354/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/354/comments | https://api.github.com/repos/huggingface/datasets/issues/354/events | https://github.com/huggingface/datasets/pull/354 | 653,357,617 | MDExOlB1bGxSZXF1ZXN0NDQ2MjkyMTc4 | 354 | More faiss control | {'avatar_url': 'https://avatars.githubusercontent.com/u/42851186?v=4', 'events_url': 'https://api.github.com/users/lhoestq/events{/privacy}', 'followers_url': 'https://api.github.com/users/lhoestq/followers', 'following_url': 'https://api.github.com/users/lhoestq/following{/other_user}', 'gists_url': 'https://api.github.com/users/lhoestq/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/lhoestq', 'id': 42851186, 'login': 'lhoestq', 'node_id': 'MDQ6VXNlcjQyODUxMTg2', 'organizations_url': 'https://api.github.com/users/lhoestq/orgs', 'received_events_url': 'https://api.github.com/users/lhoestq/received_events', 'repos_url': 'https://api.github.com/users/lhoestq/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/lhoestq/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/lhoestq/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/lhoestq'} | [] | closed | false | null | [] | null | ["> Ok, so we're getting rid of the `FaissGpuOptions`?\r\n\r\nWe support `device=...` because it's simple, but faiss GPU options can be used in so many ways (you can set different gpu options for the different parts of your index for example) that it's probably better to let the user create and configure its index and then use `custom_index=...`"] | 2020-07-08 14:45:20+00:00 | 2020-07-09 09:54:54+00:00 | 2020-07-09 09:54:51+00:00 | MEMBER | null | Allow users to specify a faiss index they created themselves, as sometimes indexes can be composite for examples | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/354/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/354/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/354.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/354', 'merged_at': '2020-07-09T09:54:51Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/354.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/354'} | true |
https://api.github.com/repos/huggingface/datasets/issues/353 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/353/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/353/comments | https://api.github.com/repos/huggingface/datasets/issues/353/events | https://github.com/huggingface/datasets/issues/353 | 653,250,611 | MDU6SXNzdWU2NTMyNTA2MTE= | 353 | [Dataset requests] New datasets for Text Classification | {'avatar_url': 'https://avatars.githubusercontent.com/u/7353373?v=4', 'events_url': 'https://api.github.com/users/thomwolf/events{/privacy}', 'followers_url': 'https://api.github.com/users/thomwolf/followers', 'following_url': 'https://api.github.com/users/thomwolf/following{/other_user}', 'gists_url': 'https://api.github.com/users/thomwolf/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/thomwolf', 'id': 7353373, 'login': 'thomwolf', 'node_id': 'MDQ6VXNlcjczNTMzNzM=', 'organizations_url': 'https://api.github.com/users/thomwolf/orgs', 'received_events_url': 'https://api.github.com/users/thomwolf/received_events', 'repos_url': 'https://api.github.com/users/thomwolf/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/thomwolf/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/thomwolf/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/thomwolf'} | [{'color': '008672', 'default': True, 'description': 'Extra attention is needed', 'id': 1935892884, 'name': 'help wanted', 'node_id': 'MDU6TGFiZWwxOTM1ODkyODg0', 'url': 'https://api.github.com/repos/huggingface/datasets/labels/help%20wanted'}
{'color': 'e99695', 'default': False, 'description': 'Requesting to add a new dataset', 'id': 2067376369, 'name': 'dataset request', 'node_id': 'MDU6TGFiZWwyMDY3Mzc2MzY5', 'url': 'https://api.github.com/repos/huggingface/datasets/labels/dataset%20request'}] | open | false | null | [] | null | ['Pinging @mariamabarham as well'
"- `nlp` has MR! It's called `rotten_tomatoes`\r\n- SST is part of GLUE, or is that just SST-2?\r\n- `nlp` also has `ag_news`, a popular news classification dataset\r\n\r\nI'd also like to see:\r\n- the Yahoo Answers topic classification dataset\r\n- the Kaggle Fake News classification dataset"
'Thanks @jxmorris12 for pointing this out. \r\n\r\nIn glue we only have SST-2 maybe we can add separately SST-1.\r\n'
'This is the homepage for the Amazon dataset: https://www.kaggle.com/datafiniti/consumer-reviews-of-amazon-products\r\n\r\nIs there an easy way to download kaggle datasets programmatically? If so, I can add this one!'
'Hi @jxmorris12 for now I think our `dl_manager` does not download from Kaggle.\r\n@thomwolf , @lhoestq'
'Pretty sure the quora dataset is the same one I implemented here: https://github.com/huggingface/nlp/pull/366'
"Great list. Any idea if Amazon Reviews has been added?\r\n\r\n- ~40 GB of text (sadly no emoji)\r\n- popular MLM pre-training dataset before bigger datasets like WebText https://arxiv.org/abs/1808.01371\r\n- turns out that binarizing the 1-5 star rating leads to great Pos/Neg/Neutral dataset, T5 paper claims to get very high accuracy (98%!) on this with small amount of finetuning https://arxiv.org/abs/2004.14546\r\n\r\nApologies if it's been included (great to see where) and if not, it's one of the better medium/large NLP dataset for semi-supervised learning, albeit a bit out of date. \r\n\r\nThanks!! \r\n\r\ncc @sshleifer "
'On the Amazon Reviews dataset, the original UCSD website has noted these are now updated to include product reviews through 2018 -- actually quite recent compared to many other datasets. Almost certainly the largest NLP dataset out there with labels!\r\nhttps://jmcauley.ucsd.edu/data/amazon/ \r\n\r\nAny chance someone has time to onboard this dataset in a HF way?\r\n\r\ncc @sshleifer '] | 2020-07-08 12:17:58+00:00 | 2022-08-04 12:08:47+00:00 | null | MEMBER | null | We are missing a few datasets for Text Classification which is an important field.
Namely, it would be really nice to add:
- [x] TREC-6 dataset (see here for instance: https://pytorchnlp.readthedocs.io/en/latest/source/torchnlp.datasets.html#torchnlp.datasets.trec_dataset) **[done]**
- #386
- [x] Yelp-5
- #1315
- [x] Movie review (Movie Review (MR) dataset [156]) **[done (same as rotten_tomatoes)]**
- [x] SST (Stanford Sentiment Treebank) **[include in glue]**
- #1934
- [ ] Multi-Perspective Question Answering (MPQA) dataset **[require authentication (indeed manual download)]**
- [x] Amazon. This is a popular corpus of product reviews collected from the Amazon website [159]. It contains labels for both binary classification and multi-class (5-class) classification
- #791
- #1389
- [x] 20 Newsgroups. The 20 Newsgroups dataset **[done]**
- #410
- [x] Sogou News dataset **[done]**
- #450
- [x] Reuters news. The Reuters-21578 dataset [165] **[done]**
- #471
- [x] DBpedia. The DBpedia dataset [170]
- #1116
- [ ] Ohsumed. The Ohsumed collection [171] is a subset of the MEDLINE database
- [ ] EUR-Lex. The EUR-Lex dataset
- [x] WOS. The Web Of Science (WOS) dataset **[done]**
- #424
- [ ] PubMed. PubMed [173]
- [x] TREC-QA: TREC-6 + TREC-50
- See above: TREC-6 dataset
- [x] Quora. The Quora dataset [180]
- #366
All these datasets are cited in https://arxiv.org/abs/2004.03705 | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 3, 'hooray': 0, 'laugh': 0, 'rocket': 3, 'total_count': 6, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/353/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/353/timeline | null | null | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/352 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/352/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/352/comments | https://api.github.com/repos/huggingface/datasets/issues/352/events | https://github.com/huggingface/datasets/pull/352 | 653,128,883 | MDExOlB1bGxSZXF1ZXN0NDQ2MTA1Mjky | 352 | 🐛[BugFix]fix seqeval | {'avatar_url': 'https://avatars.githubusercontent.com/u/20281571?v=4', 'events_url': 'https://api.github.com/users/AlongWY/events{/privacy}', 'followers_url': 'https://api.github.com/users/AlongWY/followers', 'following_url': 'https://api.github.com/users/AlongWY/following{/other_user}', 'gists_url': 'https://api.github.com/users/AlongWY/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/AlongWY', 'id': 20281571, 'login': 'AlongWY', 'node_id': 'MDQ6VXNlcjIwMjgxNTcx', 'organizations_url': 'https://api.github.com/users/AlongWY/orgs', 'received_events_url': 'https://api.github.com/users/AlongWY/received_events', 'repos_url': 'https://api.github.com/users/AlongWY/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/AlongWY/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/AlongWY/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/AlongWY'} | [] | closed | false | null | [] | null | ['I think this is good but can you detail a bit the behavior before and after your fix?'
'examples:\r\n\r\ninput: `[\'B\', \'I\', \'I\', \'O\', \'B\', \'I\']`\r\nbefore: `[(\'B\', 0, 0), (\'I\', 1, 2), (\'B\', 4, 4), (\'I\', 5, 5)]`\r\nafter: `[(\'_\', 0, 2), (\'_\', 4, 5)]`\r\n\r\ninput: `[\'B-ARGM-LOC\', \'I-ARGM-LOC\', \'I-ARGM-LOC\', \'O\', \'B-ARGM-TIME\', \'I-ARGM-TIME\']`\r\nbefore: `[(\'LOC\', 0, 2), (\'TIME\', 4, 5)]`\r\nafter: `[(\'ARGM-LOC\', 0, 2), (\'ARGM-TIME\', 4, 5)]`\r\n\r\nThis is my test code:\r\n\r\n```python\r\nfrom metrics.seqeval.seqeval import end_of_chunk, start_of_chunk\r\n\r\n\r\ndef before_get_entities(seq, suffix=False):\r\n """Gets entities from sequence.\r\n Args:\r\n seq (list): sequence of labels.\r\n Returns:\r\n list: list of (chunk_type, chunk_start, chunk_end).\r\n """\r\n if any(isinstance(s, list) for s in seq):\r\n seq = [item for sublist in seq for item in sublist + [\'O\']]\r\n\r\n prev_tag = \'O\'\r\n prev_type = \'\'\r\n begin_offset = 0\r\n chunks = []\r\n for i, chunk in enumerate(seq + [\'O\']):\r\n if suffix:\r\n tag = chunk[-1]\r\n type_ = chunk.split(\'-\')[0]\r\n else:\r\n tag = chunk[0]\r\n type_ = chunk.split(\'-\')[-1]\r\n\r\n if end_of_chunk(prev_tag, tag, prev_type, type_):\r\n chunks.append((prev_type, begin_offset, i - 1))\r\n if start_of_chunk(prev_tag, tag, prev_type, type_):\r\n begin_offset = i\r\n prev_tag = tag\r\n prev_type = type_\r\n\r\n return chunks\r\n\r\n\r\ndef after_get_entities(seq, suffix=False):\r\n """Gets entities from sequence.\r\n Args:\r\n seq (list): sequence of labels.\r\n Returns:\r\n list: list of (chunk_type, chunk_start, chunk_end).\r\n """\r\n if any(isinstance(s, list) for s in seq):\r\n seq = [item for sublist in seq for item in sublist + [\'O\']]\r\n\r\n prev_tag = \'O\'\r\n prev_type = \'\'\r\n begin_offset = 0\r\n chunks = []\r\n for i, chunk in enumerate(seq + [\'O\']):\r\n if suffix:\r\n tag = chunk[-1]\r\n type_ = chunk[:-1].rsplit(\'-\', maxsplit=1)[0] or \'_\'\r\n else:\r\n tag = chunk[0]\r\n type_ = chunk[1:].split(\'-\', maxsplit=1)[-1] or \'_\'\r\n\r\n if end_of_chunk(prev_tag, tag, prev_type, type_):\r\n chunks.append((prev_type, begin_offset, i - 1))\r\n if start_of_chunk(prev_tag, tag, prev_type, type_):\r\n begin_offset = i\r\n prev_tag = tag\r\n prev_type = type_\r\n\r\n return chunks\r\n\r\n\r\ndef main():\r\n examples_1 = [\'B\', \'I\', \'I\', \'O\', \'B\', \'I\']\r\n print(before_get_entities(examples_1))\r\n print(after_get_entities(examples_1))\r\n examples_2 = [\'B-ARGM-LOC\', \'I-ARGM-LOC\', \'I-ARGM-LOC\', \'O\', \'B-ARGM-TIME\', \'I-ARGM-TIME\']\r\n print(before_get_entities(examples_2))\r\n print(after_get_entities(examples_2))\r\n\r\n\r\nif __name__ == \'__main__\':\r\n main()\r\n```'
"And we can get more examples not correct, such as:\r\n\r\ninput: `['B', 'I', 'I-I']`\r\nbefore: `[('B', 0, 0), ('I', 1, 2)]`\r\nafter: `[('_', 0, 1), ('I', 2, 2)]`\r\n\r\ninput: `['B-ARGM-TIME', 'I-ARGM-TIME', 'I-TIME']`\r\nbefore: `[('TIME', 0, 2)]`\r\nafter: `[('ARGM-TIME', 0, 1), ('TIME', 2, 2)]`"
"I think i didn't break any thing. Maybe the checks should be restart?"
'Could you please rebase from master @AlongWY ? This should fix the CI stuff'
'ok, i will do it'
"Indeed the official repo is quite stale. Let's merge it here, thanks @AlongWY "] | 2020-07-08 09:12:12+00:00 | 2020-07-16 08:26:46+00:00 | 2020-07-16 08:26:46+00:00 | CONTRIBUTOR | null | Fix seqeval process labels such as 'B', 'B-ARGM-LOC' | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/352/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/352/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/352.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/352', 'merged_at': '2020-07-16T08:26:46Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/352.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/352'} | true |
https://api.github.com/repos/huggingface/datasets/issues/351 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/351/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/351/comments | https://api.github.com/repos/huggingface/datasets/issues/351/events | https://github.com/huggingface/datasets/pull/351 | 652,424,048 | MDExOlB1bGxSZXF1ZXN0NDQ1NDk0NTE4 | 351 | add pandas dataset | {'avatar_url': 'https://avatars.githubusercontent.com/u/42851186?v=4', 'events_url': 'https://api.github.com/users/lhoestq/events{/privacy}', 'followers_url': 'https://api.github.com/users/lhoestq/followers', 'following_url': 'https://api.github.com/users/lhoestq/following{/other_user}', 'gists_url': 'https://api.github.com/users/lhoestq/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/lhoestq', 'id': 42851186, 'login': 'lhoestq', 'node_id': 'MDQ6VXNlcjQyODUxMTg2', 'organizations_url': 'https://api.github.com/users/lhoestq/orgs', 'received_events_url': 'https://api.github.com/users/lhoestq/received_events', 'repos_url': 'https://api.github.com/users/lhoestq/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/lhoestq/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/lhoestq/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/lhoestq'} | [] | closed | false | null | [] | null | [] | 2020-07-07 15:38:07+00:00 | 2020-07-08 14:15:16+00:00 | 2020-07-08 14:15:15+00:00 | MEMBER | null | Create a dataset from serialized pandas dataframes.
Usage:
```python
from nlp import load_dataset
dset = load_dataset("pandas", data_files="df.pkl")["train"]
``` | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 1, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 1, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/351/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/351/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/351.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/351', 'merged_at': '2020-07-08T14:15:15Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/351.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/351'} | true |
https://api.github.com/repos/huggingface/datasets/issues/350 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/350/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/350/comments | https://api.github.com/repos/huggingface/datasets/issues/350/events | https://github.com/huggingface/datasets/pull/350 | 652,398,691 | MDExOlB1bGxSZXF1ZXN0NDQ1NDczODYz | 350 | add from_pandas and from_dict | {'avatar_url': 'https://avatars.githubusercontent.com/u/42851186?v=4', 'events_url': 'https://api.github.com/users/lhoestq/events{/privacy}', 'followers_url': 'https://api.github.com/users/lhoestq/followers', 'following_url': 'https://api.github.com/users/lhoestq/following{/other_user}', 'gists_url': 'https://api.github.com/users/lhoestq/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/lhoestq', 'id': 42851186, 'login': 'lhoestq', 'node_id': 'MDQ6VXNlcjQyODUxMTg2', 'organizations_url': 'https://api.github.com/users/lhoestq/orgs', 'received_events_url': 'https://api.github.com/users/lhoestq/received_events', 'repos_url': 'https://api.github.com/users/lhoestq/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/lhoestq/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/lhoestq/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/lhoestq'} | [] | closed | false | null | [] | null | [] | 2020-07-07 15:03:53+00:00 | 2020-07-08 14:14:33+00:00 | 2020-07-08 14:14:32+00:00 | MEMBER | null | I added two new methods to the `Dataset` class:
- `from_pandas()` to create a dataset from a pandas dataframe
- `from_dict()` to create a dataset from a dictionary (keys = columns)
It uses the `pa.Table.from_pandas` and `pa.Table.from_pydict` funcitons to do so.
It is also possible to specify the features types via `features=...` if there are ambiguities (null/nan values), otherwise the arrow schema is infered from the data automatically by pyarrow.
One question that I have right now:
+ Should we also add a `save()` method that would write the dataset on the disk ? Right now if we create a `Dataset` using those two new methods, the data are kept in RAM. Then to reload it we can call the `from_file()` method. | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/350/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/350/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/350.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/350', 'merged_at': '2020-07-08T14:14:32Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/350.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/350'} | true |
https://api.github.com/repos/huggingface/datasets/issues/349 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/349/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/349/comments | https://api.github.com/repos/huggingface/datasets/issues/349/events | https://github.com/huggingface/datasets/pull/349 | 652,231,571 | MDExOlB1bGxSZXF1ZXN0NDQ1MzQwMTQ1 | 349 | Hyperpartisan news detection | {'avatar_url': 'https://avatars.githubusercontent.com/u/13795113?v=4', 'events_url': 'https://api.github.com/users/ghomasHudson/events{/privacy}', 'followers_url': 'https://api.github.com/users/ghomasHudson/followers', 'following_url': 'https://api.github.com/users/ghomasHudson/following{/other_user}', 'gists_url': 'https://api.github.com/users/ghomasHudson/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/ghomasHudson', 'id': 13795113, 'login': 'ghomasHudson', 'node_id': 'MDQ6VXNlcjEzNzk1MTEz', 'organizations_url': 'https://api.github.com/users/ghomasHudson/orgs', 'received_events_url': 'https://api.github.com/users/ghomasHudson/received_events', 'repos_url': 'https://api.github.com/users/ghomasHudson/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/ghomasHudson/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/ghomasHudson/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/ghomasHudson'} | [] | closed | false | null | [] | null | ['Thank you so much for working on this! This is awesome!\r\n\r\nHow much would it help you if we would remove the manual request?\r\n\r\nWe are naturally interested in getting some broad idea of how many people and who are using our dataset. But if you consider hosting the dataset yourself, I would rather remove this small barrier on our side (so that we then still get the download count from your library).'
"This is an interesting aspect indeed!\r\nDo you want to send me an email (see my homepage) and I'll invite you on our slack channel to talk about that?\r\n@ghomasHudson wanna reach out to me as well? I tried to find your email to invite you without success."] | 2020-07-07 11:06:37+00:00 | 2020-07-07 20:47:27+00:00 | 2020-07-07 14:57:11+00:00 | CONTRIBUTOR | null | Adding the hyperpartisan news detection dataset from PAN. This contains news article text, labelled with whether they're hyper-partisan and why kinds of biases they display.
Implementation notes:
- As with many PAN tasks, the data is hosted on [Zenodo](https://zenodo.org/record/1489920) and must be requested before use. I've used the manual download stuff for this, although the dataset is provided under a Creative Commons Attribution 4.0 International License, so we could host a version if we wanted to?
- The 'bias' attribute doesn't exist for the 'byarticle' configuration. I've added an empty string to the class labels to deal with this. Is there a more standard value for empty data?
- Should we always subclass `nlp.BuilderConfig`?
| {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/349/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/349/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/349.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/349', 'merged_at': '2020-07-07T14:57:11Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/349.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/349'} | true |
https://api.github.com/repos/huggingface/datasets/issues/348 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/348/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/348/comments | https://api.github.com/repos/huggingface/datasets/issues/348/events | https://github.com/huggingface/datasets/pull/348 | 652,158,308 | MDExOlB1bGxSZXF1ZXN0NDQ1MjgwNjk3 | 348 | Add OSCAR dataset | {'avatar_url': 'https://avatars.githubusercontent.com/u/635220?v=4', 'events_url': 'https://api.github.com/users/pjox/events{/privacy}', 'followers_url': 'https://api.github.com/users/pjox/followers', 'following_url': 'https://api.github.com/users/pjox/following{/other_user}', 'gists_url': 'https://api.github.com/users/pjox/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/pjox', 'id': 635220, 'login': 'pjox', 'node_id': 'MDQ6VXNlcjYzNTIyMA==', 'organizations_url': 'https://api.github.com/users/pjox/orgs', 'received_events_url': 'https://api.github.com/users/pjox/received_events', 'repos_url': 'https://api.github.com/users/pjox/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/pjox/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/pjox/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/pjox'} | [] | closed | false | null | [] | null | ["@pjox I think the tests don't pass because you haven't provided any dummy data (`dummy_data.zip`).\r\n\r\n "
"> @pjox I think the tests don't pass because you haven't provided any dummy data (`dummy_data.zip`).\r\n\r\nBut can I do the dummy data without running `python nlp-cli test datasets/<your-dataset-folder> --save_infos --all_configs` first? 🤔 "
'You make a good point! Do you know how big is it uncompressed?'
'Between 7T and 9T I think.'
"Hi ! I've been busy but I plan to compute the missing metadata soon !\r\nLooking forward to be able to load a memory mapped version of OSCAR :) "
"> Hi ! I've been busy but I plan to compute the missing metadata soon !\r\n> Looking forward to be able to load a memory mapped version of OSCAR :)\r\n\r\nAmazing! Thanks! 😄 "
"Hi there, are there any plans to complete this issue soon? I'm planning to use this dataset on a project. Let me know if there's anything I can do to help to finish this 🤗 "
'Yes it will be added soon :) \r\nRecently the OSCAR data files were moved to another host. We just need to update the script and compute the dataset_infos.json (it will probably take a few days).'
"@lhoestq I've seen in oscar.py that it isn't a dataset script with manual download way. Is that correct? \r\nSome time ago, @pjox had some troubles with his servers providing that dataset 'cause it's really huge. Providing it on an automatic download way seems to be a little bit dangerous for me 😄 "
"Now thanks to @pjox 's help OSCAR is hosted on HF's S3, which is probably more robust that the previous servers :)\r\n\r\nAlso small update on my side:\r\nI launched the computation of the dataset_infos.json file, it will take a few days."
'Now it seems to be a good plan for me 🤗 '
"But is there a plan to provide the OSCAR's unshuffled version too?"
'The one we have on S3 is currently the unshuffled version'
"I've thought that you won't provide the unshuffled version 'cause this comment on oscar.py:\r\n\r\n`# TODO(oscar): Implement unshuffled OSCAR`\r\n\r\n"
"That TODO is normal, I haven't touched the python script in months (I haven't had the time, sorry), but I guess @lhoestq fixed the paths if he's already working on the metadata. In any case from now on, only the unshuffled versions of OSCAR will be distributed through the hf/datasets library as in any case it is the version most people use to train language models.\r\n\r\nIf for any reason, you need the shuffled version it will always be available on the [OSCAR website](https://oscar-corpus.com).\r\n\r\nAlso future versions of OSCAR will be unshuffled only."
'Should we close this PR now that the other one was merged?'
'Sure.\r\nClosing since #1694 is merged'
'@lhoestq just a little detail, is the Oscar version that HF offers the same one that was available on INRIA? By that I mean, have you done any further filtering or removing of data inside it? Thanks a lot! '
'Hello @jchwenger, this is exactly the same (unshuffled) version that\'s available at Inria. Sadly no further filtering is provided, but after the latest OSCAR audit (https://arxiv.org/abs/2103.12028) we\'re already working on future versions of OSCAR that will be "filtered" and that will be available on the OSCAR website and hopefully here as well.'
'@pjox brilliant, in my case I was hoping it would be unfiltered, good news!'] | 2020-07-07 09:22:07+00:00 | 2021-05-03 22:07:08+00:00 | 2021-02-09 10:19:19+00:00 | CONTRIBUTOR | null | I don't know if tests pass, when I run them it tries to download the whole corpus which is around 3.5TB compressed and I don't have that kind of space. I'll really need some help with it 😅
Thanks! | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 4, 'laugh': 0, 'rocket': 0, 'total_count': 4, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/348/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/348/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/348.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/348', 'merged_at': None, 'patch_url': 'https://github.com/huggingface/datasets/pull/348.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/348'} | true |
https://api.github.com/repos/huggingface/datasets/issues/347 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/347/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/347/comments | https://api.github.com/repos/huggingface/datasets/issues/347/events | https://github.com/huggingface/datasets/issues/347 | 652,106,567 | MDU6SXNzdWU2NTIxMDY1Njc= | 347 | 'cp950' codec error from load_dataset('xtreme', 'tydiqa') | {'avatar_url': 'https://avatars.githubusercontent.com/u/50871412?v=4', 'events_url': 'https://api.github.com/users/jerryIsHere/events{/privacy}', 'followers_url': 'https://api.github.com/users/jerryIsHere/followers', 'following_url': 'https://api.github.com/users/jerryIsHere/following{/other_user}', 'gists_url': 'https://api.github.com/users/jerryIsHere/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/jerryIsHere', 'id': 50871412, 'login': 'jerryIsHere', 'node_id': 'MDQ6VXNlcjUwODcxNDEy', 'organizations_url': 'https://api.github.com/users/jerryIsHere/orgs', 'received_events_url': 'https://api.github.com/users/jerryIsHere/received_events', 'repos_url': 'https://api.github.com/users/jerryIsHere/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/jerryIsHere/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/jerryIsHere/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/jerryIsHere'} | [{'color': '2edb81', 'default': False, 'description': 'A bug in a dataset script provided in the library', 'id': 2067388877, 'name': 'dataset bug', 'node_id': 'MDU6TGFiZWwyMDY3Mzg4ODc3', 'url': 'https://api.github.com/repos/huggingface/datasets/labels/dataset%20bug'}] | closed | false | null | [] | null | ["This is probably a Windows issue, we need to specify the encoding when `load_dataset()` reads the original CSV file.\r\nTry to find the `open()` statement called by `load_dataset()` and add an `encoding='utf-8'` parameter.\r\nSee issues #242 and #307 "
'It should be in `xtreme.py:L755`:\r\n```python\r\n if self.config.name == "tydiqa" or self.config.name.startswith("MLQA") or self.config.name == "SQuAD":\r\n with open(filepath) as f:\r\n data = json.load(f)\r\n```\r\n\r\nCould you try to add the encoding parameter:\r\n```python\r\nopen(filepath, encoding=\'utf-8\')\r\n```'
'Hello @jerryIsHere :) Did it work ?\r\nIf so we may change the dataset script to force the utf-8 encoding'
'@lhoestq sorry for being that late, I found 4 copy of xtreme.py. I did the changes as what has been told to all of them.\r\nThe problem is not solved'
"Could you provide a better error message so that we can make sure it comes from the opening of the `tydiqa`'s json files ?\r\n"
'@lhoestq \r\nThe error message is same as before:\r\nException has occurred: UnicodeDecodeError\r\n\'cp950\' codec can\'t decode byte 0xe2 in position 111: illegal multibyte sequence\r\n File "D:\\python\\test\\test.py", line 3, in <module>\r\n dataset = load_dataset(\'xtreme\', \'tydiqa\')\r\n\r\n![image](https://user-images.githubusercontent.com/50871412/87748794-7c216880-c829-11ea-94f0-7caeacb4d865.png)\r\n\r\nI said that I found 4 copy of xtreme.py and add the 「, encoding=\'utf-8\'」 parameter to the open() function\r\nthese python script was found under this directory\r\nC:\\Users\\USER\\AppData\\Local\\Programs\\Python\\Python37\\Lib\\site-packages\\nlp\\datasets\\xtreme\r\n'
"Hi there !\r\nI encountered the same issue with the IMDB dataset on windows. It threw an error about charmap not being able to decode a symbol during the first time I tried to download it. I checked on a remote linux machine I have, and it can't be reproduced.\r\nI added ```encoding='UTF-8'``` to both lines that have ```open``` in ```imdb.py``` (108 and 114) and it worked for me.\r\nThank you !"
"> Hi there !\r\n> I encountered the same issue with the IMDB dataset on windows. It threw an error about charmap not being able to decode a symbol during the first time I tried to download it. I checked on a remote linux machine I have, and it can't be reproduced.\r\n> I added `encoding='UTF-8'` to both lines that have `open` in `imdb.py` (108 and 114) and it worked for me.\r\n> Thank you !\r\n\r\nHello !\r\nGlad you managed to fix this issue on your side.\r\nDo you mind opening a PR for IMDB ?"
"> This is probably a Windows issue, we need to specify the encoding when `load_dataset()` reads the original CSV file.\r\n> Try to find the `open()` statement called by `load_dataset()` and add an `encoding='utf-8'` parameter.\r\n> See issues #242 and #307\r\n\r\nSorry for not responding for about a month.\r\nI have just found that it is necessary to change / add the environment variable as what was told in #242.\r\nEverything works after I add the new environment variable and restart my PC.\r\n\r\nI think the encoding issue for windows isn't limited to the open() function call specific to few dataset, but actually in the entire library, depends on the machine / os you use."
'Since #481 we shouldn\'t have other issues with encodings as they need to be set to "utf-8" be default.\r\n\r\nClosing this one, but feel free to re-open if you gave other questions'] | 2020-07-07 08:14:23+00:00 | 2020-09-07 14:51:45+00:00 | 2020-09-07 14:51:45+00:00 | CONTRIBUTOR | null | ![image](https://user-images.githubusercontent.com/50871412/86744744-67481680-c06c-11ea-8612-b77eba92a392.png)
I guess the error is related to python source encoding issue that my PC is trying to decode the source code with wrong encoding-decoding tools, perhaps :
https://www.python.org/dev/peps/pep-0263/
I guess the error was triggered by the code " module = importlib.import_module(module_path)" at line 57 in the source code: nlp/src/nlp/load.py / (https://github.com/huggingface/nlp/blob/911d5596f9b500e39af8642fe3d1b891758999c7/src/nlp/load.py#L51)
Any ideas?
p.s. tried the same code on colab, that runs perfectly
| {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/347/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/347/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/346 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/346/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/346/comments | https://api.github.com/repos/huggingface/datasets/issues/346/events | https://github.com/huggingface/datasets/pull/346 | 652,044,151 | MDExOlB1bGxSZXF1ZXN0NDQ1MTg4MTUz | 346 | Add emotion dataset | {'avatar_url': 'https://avatars.githubusercontent.com/u/26859204?v=4', 'events_url': 'https://api.github.com/users/lewtun/events{/privacy}', 'followers_url': 'https://api.github.com/users/lewtun/followers', 'following_url': 'https://api.github.com/users/lewtun/following{/other_user}', 'gists_url': 'https://api.github.com/users/lewtun/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/lewtun', 'id': 26859204, 'login': 'lewtun', 'node_id': 'MDQ6VXNlcjI2ODU5MjA0', 'organizations_url': 'https://api.github.com/users/lewtun/orgs', 'received_events_url': 'https://api.github.com/users/lewtun/received_events', 'repos_url': 'https://api.github.com/users/lewtun/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/lewtun/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/lewtun/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/lewtun'} | [] | closed | false | null | [] | null | ["I've tried it and am getting the same error as you.\r\n\r\nYou could use the text files rather than the pickle:\r\n```\r\nhttps://www.dropbox.com/s/ikkqxfdbdec3fuj/test.txt\r\nhttps://www.dropbox.com/s/1pzkadrvffbqw6o/train.txt\r\nhttps://www.dropbox.com/s/2mzialpsgf9k5l3/val.txt\r\n```\r\n\r\nThen you would get all 3 splits rather than just the train split."
"Thanks a lot @ghomasHudson - silly me for not spotting that! \r\n\r\nI'll keep the PR open for now since I'm quite close to wrapping it up."
'Hi @ghomasHudson your suggestion worked like a charm - the PR is now ready for review 😎 '
'Hello, I probably have a silly question but the labels of the emotion dataset are in the form of numbers and not string, so I can not use the function classification_report because it mixes numbers and string (prediction). How can I access the label in the form of a string and not a number?\r\nThank you in advance.'
'Hi @juliette-sch! Yes, I believe that having the labels as integers is now the default for many classification datasets. You can access the string label via the `ClassLabel.int2str` function ([docs](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=int2str#datasets.ClassLabel.int2str)), so you could add a new column to the dataset as follows:\r\n\r\n```python\r\nfrom datasets import load_dataset \r\n\r\nemotions = load_dataset("emotion")\r\n\r\ndef label_int2str(row):\r\n return {"label_name": emotions["train"].features["label"].int2str(row["label"])}\r\n\r\n# adds a new column called `label_name`\r\nemotions = emotions.map(label_int2str)\r\n```'
'Great, thank you very much @lewtun !'
'Hi, @lewtun \r\nWhen I load "emotion"\r\n```\r\nfrom datasets import load_dataset\r\n\r\nemotions = load_dataset("emotion")\r\n```\r\n\r\nThere is an error:\r\n\r\n```\r\nConnectionError: Couldn\'t reach https://www.dropbox.com/s/1pzkadrvffbqw6o/train.txt?dl=1 (SSLError(MaxRetryError("HTTPSConnectionPool(host=\'www.dropbox.com\', port=443): Max retries exceeded with url: /s/1pzkadrvffbqw6o/train.txt?dl=1 (Caused by SSLError(SSLCertVerificationError(1, \'[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self signed certificate in certificate chain (_ssl.c:1091)\')))")))\r\n```\r\nCan you please tell me what is wrong?\r\n\r\nThanks a lot.\r\nDan'
"Hi ! I could't reproduce the error, can you try again ? You can also try updating the `datasets` library and see if it fixes the issue"
"Hi @lhoestq \r\n\r\nIt seems my company's internet blocked dropbox, I am sorry.\r\nThanks a lot.\r\n"] | 2020-07-07 06:35:41+00:00 | 2022-05-30 15:16:44+00:00 | 2020-07-13 14:39:38+00:00 | MEMBER | null | Hello 🤗 team!
I am trying to add an emotion classification dataset ([link](https://github.com/dair-ai/emotion_dataset)) to `nlp` but I am a bit stuck about what I should do when the URL for the dataset is not a ZIP file, but just a pickled `pandas.DataFrame` (see [here](https://www.dropbox.com/s/607ptdakxuh5i4s/merged_training.pkl)).
With the current implementation, running
```bash
python nlp-cli test datasets/emotion --save_infos --all_configs
```
throws a `_pickle.UnpicklingError: invalid load key, '<'.` error (full stack trace below). The strange thing is that the path to the file does not carry the `.pkl` extension and instead appears to be some md5 hash (see the `FILE PATH` print statement in the stack trace).
Note: I have checked that the `merged_training.pkl` file is not corrupted when I download it with `wget`.
Any pointers on what I'm doing wrong would be greatly appreciated!
**Stack trace**
```
INFO:nlp.load:Checking datasets/emotion/emotion.py for additional imports.
INFO:filelock:Lock 140330435928512 acquired on datasets/emotion/emotion.py.lock
INFO:nlp.load:Found main folder for dataset datasets/emotion/emotion.py at /Users/lewtun/git/nlp/src/nlp/datasets/emotion
INFO:nlp.load:Creating specific version folder for dataset datasets/emotion/emotion.py at /Users/lewtun/git/nlp/src/nlp/datasets/emotion/59666994754d1b369228a749b695e377643d141fa98c6972be00407659788c7b
INFO:nlp.load:Copying script file from datasets/emotion/emotion.py to /Users/lewtun/git/nlp/src/nlp/datasets/emotion/59666994754d1b369228a749b695e377643d141fa98c6972be00407659788c7b/emotion.py
INFO:nlp.load:Couldn't find dataset infos file at datasets/emotion/dataset_infos.json
INFO:nlp.load:Creating metadata file for dataset datasets/emotion/emotion.py at /Users/lewtun/git/nlp/src/nlp/datasets/emotion/59666994754d1b369228a749b695e377643d141fa98c6972be00407659788c7b/emotion.json
INFO:filelock:Lock 140330435928512 released on datasets/emotion/emotion.py.lock
INFO:nlp.builder:Generating dataset emotion (/Users/lewtun/.cache/huggingface/datasets/emotion/emotion/1.0.0)
INFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source
Downloading and preparing dataset emotion/emotion (download: Unknown size, generated: Unknown size, total: Unknown size) to /Users/lewtun/.cache/huggingface/datasets/emotion/emotion/1.0.0...
INFO:nlp.builder:Generating split train
0 examples [00:00, ? examples/s]FILE PATH /Users/lewtun/.cache/huggingface/datasets/3615dcb52b7ba052ef63e1571894c4b67e8e12a6ab1ef2f756ec3c380bf48490
Traceback (most recent call last):
File "nlp-cli", line 37, in <module>
service.run()
File "/Users/lewtun/git/nlp/src/nlp/commands/test.py", line 83, in run
builder.download_and_prepare(
File "/Users/lewtun/git/nlp/src/nlp/builder.py", line 431, in download_and_prepare
self._download_and_prepare(
File "/Users/lewtun/git/nlp/src/nlp/builder.py", line 483, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/Users/lewtun/git/nlp/src/nlp/builder.py", line 664, in _prepare_split
for key, record in utils.tqdm(generator, unit=" examples", total=split_info.num_examples, leave=False):
File "/Users/lewtun/miniconda3/envs/nlp/lib/python3.8/site-packages/tqdm/std.py", line 1129, in __iter__
for obj in iterable:
File "/Users/lewtun/git/nlp/src/nlp/datasets/emotion/59666994754d1b369228a749b695e377643d141fa98c6972be00407659788c7b/emotion.py", line 87, in _generate_examples
data = pickle.load(f)
_pickle.UnpicklingError: invalid load key, '<'.
``` | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/346/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/346/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/346.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/346', 'merged_at': '2020-07-13T14:39:38Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/346.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/346'} | true |
https://api.github.com/repos/huggingface/datasets/issues/345 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/345/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/345/comments | https://api.github.com/repos/huggingface/datasets/issues/345/events | https://github.com/huggingface/datasets/issues/345 | 651,761,201 | MDU6SXNzdWU2NTE3NjEyMDE= | 345 | Supporting documents in ELI5 | {'avatar_url': 'https://avatars.githubusercontent.com/u/29262273?v=4', 'events_url': 'https://api.github.com/users/saverymax/events{/privacy}', 'followers_url': 'https://api.github.com/users/saverymax/followers', 'following_url': 'https://api.github.com/users/saverymax/following{/other_user}', 'gists_url': 'https://api.github.com/users/saverymax/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/saverymax', 'id': 29262273, 'login': 'saverymax', 'node_id': 'MDQ6VXNlcjI5MjYyMjcz', 'organizations_url': 'https://api.github.com/users/saverymax/orgs', 'received_events_url': 'https://api.github.com/users/saverymax/received_events', 'repos_url': 'https://api.github.com/users/saverymax/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/saverymax/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/saverymax/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/saverymax'} | [] | closed | false | null | [] | null | ['Hi @saverymax ! For licensing reasons, the original team was unable to release pre-processed CommonCrawl documents. Instead, they provided a script to re-create them from a CommonCrawl dump, but it unfortunately requires access to a medium-large size cluster:\r\nhttps://github.com/facebookresearch/ELI5#downloading-support-documents-from-the-commoncrawl\r\n\r\nIn order to make the task accessible to people who may not have access to this kind of infrastructure, we suggest to use Wikipedia as a knowledge source rather than the full CommonCrawl. The following blog post shows how you can create Wikipedia support documents and get a performance that is on par with a system that uses CommonCrawl pages.\r\nhttps://yjernite.github.io/lfqa.html#task_description\r\n\r\nHope that helps, using ElasticSearch to index Wiki40b and create the documents should take about 4 hours. Let us know if you have any trouble with the blog post though!'
'Hi, thanks for the quick response. The blog post is quite an interesting working example, thanks for sharing it.\r\nTwo follow-up points/questions about my original question:\r\n\r\n1. Yes, I read that the facebook team could not share the CommonCrawl b/c of licensing reasons. They state "No, we are not allowed to host processed Reddit or CommonCrawl data," which indicates they could also not share the Reddit data for licensing reasons. But it seems that HuggingFace is able to share the Reddit data, so why not a subset of CommonCrawl?\r\n\r\n2. Thanks for the suggestion about ElasticSearch and Wiki40b. This is good to know about performance. I definitely could do the indexing and querying myself. What I like about the ELI5 dataset though, at least what is suggested by the paper, is that to create the dataset they had already selected the top 100 web sources and made a single support document from those. Though it doesn\'t appear to be too sophisticated an approach, having a single support document pre-computed (without having to run the facebook code or a replacement with another dataset) is super useful for my work, especially since I\'m not working on developing the latest and greatest retrieval model. Of course, I don\'t expect HF NLP datasets to be perfectly tailored to my use-case. I know there is overhead to any project, I\'m just illustrating a use-case of ELI5 which is not possible with the data provided as-is. If it\'s for licensing reasons, that is perfectly acceptable a reason, and I appreciate your response.'] | 2020-07-06 19:14:13+00:00 | 2020-10-27 15:38:45+00:00 | 2020-10-27 15:38:45+00:00 | NONE | null | I was attempting to use the ELI5 dataset, when I realized that huggingface does not provide the supporting documents (the source documents from the common crawl). Without the supporting documents, this makes the dataset about as useful for my project as a block of cheese, or some other more apt metaphor. According to facebook, the entire document collection is quite large. However, it would still be helpful to at least include a subset of the supporting documents i.e., having some data is better than having a block of cheese, in my case at least.
If you choose not to include them, it would be helpful to have documentation mentioning this specifically. It is especially confusing because the hf nlp ELI5 dataset has the key `'document'` but there are no documents to be found :( | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/345/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/345/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/344 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/344/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/344/comments | https://api.github.com/repos/huggingface/datasets/issues/344/events | https://github.com/huggingface/datasets/pull/344 | 651,495,246 | MDExOlB1bGxSZXF1ZXN0NDQ0NzQwMTIw | 344 | Search qa | {'avatar_url': 'https://avatars.githubusercontent.com/u/38249783?v=4', 'events_url': 'https://api.github.com/users/mariamabarham/events{/privacy}', 'followers_url': 'https://api.github.com/users/mariamabarham/followers', 'following_url': 'https://api.github.com/users/mariamabarham/following{/other_user}', 'gists_url': 'https://api.github.com/users/mariamabarham/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/mariamabarham', 'id': 38249783, 'login': 'mariamabarham', 'node_id': 'MDQ6VXNlcjM4MjQ5Nzgz', 'organizations_url': 'https://api.github.com/users/mariamabarham/orgs', 'received_events_url': 'https://api.github.com/users/mariamabarham/received_events', 'repos_url': 'https://api.github.com/users/mariamabarham/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/mariamabarham/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/mariamabarham/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/mariamabarham'} | [] | closed | false | null | [] | null | ["Could you rebase from master just to make sure we won't break anything for `fever` pls @mariamabarham ?"] | 2020-07-06 12:23:16+00:00 | 2020-07-16 08:58:16+00:00 | 2020-07-16 08:58:16+00:00 | CONTRIBUTOR | null | This PR adds the Search QA dataset used in **SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine**. The dataset has the following config name:
- raw_jeopardy: raw data
- train_test_val: which is the splitted version
#336 | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/344/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/344/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/344.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/344', 'merged_at': None, 'patch_url': 'https://github.com/huggingface/datasets/pull/344.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/344'} | true |
https://api.github.com/repos/huggingface/datasets/issues/343 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/343/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/343/comments | https://api.github.com/repos/huggingface/datasets/issues/343/events | https://github.com/huggingface/datasets/pull/343 | 651,419,630 | MDExOlB1bGxSZXF1ZXN0NDQ0Njc4NDEw | 343 | Fix nested tensorflow format | {'avatar_url': 'https://avatars.githubusercontent.com/u/42851186?v=4', 'events_url': 'https://api.github.com/users/lhoestq/events{/privacy}', 'followers_url': 'https://api.github.com/users/lhoestq/followers', 'following_url': 'https://api.github.com/users/lhoestq/following{/other_user}', 'gists_url': 'https://api.github.com/users/lhoestq/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/lhoestq', 'id': 42851186, 'login': 'lhoestq', 'node_id': 'MDQ6VXNlcjQyODUxMTg2', 'organizations_url': 'https://api.github.com/users/lhoestq/orgs', 'received_events_url': 'https://api.github.com/users/lhoestq/received_events', 'repos_url': 'https://api.github.com/users/lhoestq/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/lhoestq/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/lhoestq/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/lhoestq'} | [] | closed | false | null | [] | null | [] | 2020-07-06 10:13:45+00:00 | 2020-07-06 13:11:52+00:00 | 2020-07-06 13:11:51+00:00 | MEMBER | null | In #339 and #337 we are thinking about adding a way to export datasets to tfrecords.
However I noticed that it was not possible to do `dset.set_format("tensorflow")` on datasets with nested features like `squad`. I fixed that using a nested map operations to convert features to `tf.ragged.constant`.
I also added tests on the `set_format` function. | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/343/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/343/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/343.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/343', 'merged_at': '2020-07-06T13:11:51Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/343.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/343'} | true |
https://api.github.com/repos/huggingface/datasets/issues/342 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/342/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/342/comments | https://api.github.com/repos/huggingface/datasets/issues/342/events | https://github.com/huggingface/datasets/issues/342 | 651,333,194 | MDU6SXNzdWU2NTEzMzMxOTQ= | 342 | Features should be updated when `map()` changes schema | {'avatar_url': 'https://avatars.githubusercontent.com/u/7353373?v=4', 'events_url': 'https://api.github.com/users/thomwolf/events{/privacy}', 'followers_url': 'https://api.github.com/users/thomwolf/followers', 'following_url': 'https://api.github.com/users/thomwolf/following{/other_user}', 'gists_url': 'https://api.github.com/users/thomwolf/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/thomwolf', 'id': 7353373, 'login': 'thomwolf', 'node_id': 'MDQ6VXNlcjczNTMzNzM=', 'organizations_url': 'https://api.github.com/users/thomwolf/orgs', 'received_events_url': 'https://api.github.com/users/thomwolf/received_events', 'repos_url': 'https://api.github.com/users/thomwolf/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/thomwolf/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/thomwolf/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/thomwolf'} | [] | closed | false | null | [] | null | ["`dataset.column_names` are being updated but `dataset.features` aren't indeed..."] | 2020-07-06 08:03:23+00:00 | 2020-07-23 10:15:16+00:00 | 2020-07-23 10:15:16+00:00 | MEMBER | null | `dataset.map()` can change the schema and column names.
We should update the features in this case (with what is possible to infer). | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/342/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/342/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/341 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/341/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/341/comments | https://api.github.com/repos/huggingface/datasets/issues/341/events | https://github.com/huggingface/datasets/pull/341 | 650,611,969 | MDExOlB1bGxSZXF1ZXN0NDQ0MDcwMjEx | 341 | add fever dataset | {'avatar_url': 'https://avatars.githubusercontent.com/u/38249783?v=4', 'events_url': 'https://api.github.com/users/mariamabarham/events{/privacy}', 'followers_url': 'https://api.github.com/users/mariamabarham/followers', 'following_url': 'https://api.github.com/users/mariamabarham/following{/other_user}', 'gists_url': 'https://api.github.com/users/mariamabarham/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/mariamabarham', 'id': 38249783, 'login': 'mariamabarham', 'node_id': 'MDQ6VXNlcjM4MjQ5Nzgz', 'organizations_url': 'https://api.github.com/users/mariamabarham/orgs', 'received_events_url': 'https://api.github.com/users/mariamabarham/received_events', 'repos_url': 'https://api.github.com/users/mariamabarham/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/mariamabarham/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/mariamabarham/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/mariamabarham'} | [] | closed | false | null | [] | null | [] | 2020-07-03 13:53:07+00:00 | 2020-07-06 13:03:48+00:00 | 2020-07-06 13:03:47+00:00 | CONTRIBUTOR | null | This PR add the FEVER dataset https://fever.ai/ used in with the paper: FEVER: a large-scale dataset for Fact Extraction and VERification (https://arxiv.org/pdf/1803.05355.pdf).
#336 | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/341/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/341/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/341.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/341', 'merged_at': '2020-07-06T13:03:47Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/341.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/341'} | true |
https://api.github.com/repos/huggingface/datasets/issues/340 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/340/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/340/comments | https://api.github.com/repos/huggingface/datasets/issues/340/events | https://github.com/huggingface/datasets/pull/340 | 650,533,920 | MDExOlB1bGxSZXF1ZXN0NDQ0MDA2Nzcy | 340 | Update cfq.py | {'avatar_url': 'https://avatars.githubusercontent.com/u/4437290?v=4', 'events_url': 'https://api.github.com/users/brainshawn/events{/privacy}', 'followers_url': 'https://api.github.com/users/brainshawn/followers', 'following_url': 'https://api.github.com/users/brainshawn/following{/other_user}', 'gists_url': 'https://api.github.com/users/brainshawn/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/brainshawn', 'id': 4437290, 'login': 'brainshawn', 'node_id': 'MDQ6VXNlcjQ0MzcyOTA=', 'organizations_url': 'https://api.github.com/users/brainshawn/orgs', 'received_events_url': 'https://api.github.com/users/brainshawn/received_events', 'repos_url': 'https://api.github.com/users/brainshawn/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/brainshawn/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/brainshawn/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/brainshawn'} | [] | closed | false | null | [] | null | ['Thanks @brainshawn for this update'] | 2020-07-03 11:23:19+00:00 | 2020-07-03 12:33:50+00:00 | 2020-07-03 12:33:50+00:00 | CONTRIBUTOR | null | Make the dataset name consistent with in the paper: Compositional Freebase Question => Compositional Freebase Questions. | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/340/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/340/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/340.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/340', 'merged_at': '2020-07-03T12:33:50Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/340.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/340'} | true |
https://api.github.com/repos/huggingface/datasets/issues/339 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/339/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/339/comments | https://api.github.com/repos/huggingface/datasets/issues/339/events | https://github.com/huggingface/datasets/pull/339 | 650,156,468 | MDExOlB1bGxSZXF1ZXN0NDQzNzAyNTcw | 339 | Add dataset.export() to TFRecords | {'avatar_url': 'https://avatars.githubusercontent.com/u/4564897?v=4', 'events_url': 'https://api.github.com/users/jarednielsen/events{/privacy}', 'followers_url': 'https://api.github.com/users/jarednielsen/followers', 'following_url': 'https://api.github.com/users/jarednielsen/following{/other_user}', 'gists_url': 'https://api.github.com/users/jarednielsen/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/jarednielsen', 'id': 4564897, 'login': 'jarednielsen', 'node_id': 'MDQ6VXNlcjQ1NjQ4OTc=', 'organizations_url': 'https://api.github.com/users/jarednielsen/orgs', 'received_events_url': 'https://api.github.com/users/jarednielsen/received_events', 'repos_url': 'https://api.github.com/users/jarednielsen/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/jarednielsen/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/jarednielsen/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/jarednielsen'} | [] | closed | false | null | [] | null | ["Really cool @jarednielsen !\r\nDo you think we can make it work with dataset with nested features like `squad` ?\r\n\r\nI just did a PR to fix `.set_format` for datasets with nested features, but as soon as it's merged we could try to make the conversion work on a dataset like `squad`."
'For datasets with nested features we have two aspects to take into account:\r\n1) There can be nested dict of features. What is done in tensorflow_datasets to make things work is to flatten the dictionaries to end up with one single dictionary. A dict like `{"column1": {"subfeature": ...}}` is converted to `{"column1/subfeature":...}`\r\n2) There can be ragged tensors, i.e. lists of objects with non-fixed shapes. For example in squad there are often multiple possible answers per question. What is done in tensorflow_datasets to make things work is to concatenate everything and add ragged attributes (cf serialization code [here](https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/core/example_serializer.py))'
'Note that we have `flatten` method in `ArrowDataset`'
'I added support for nested dictionaries. A few more design decisions popped up:\r\n\r\n_Should we serialize from NumPy arrays or from tf.Tensors?_\r\n- The [tfds example serializer](url) works from NumPy arrays.\r\n- Calling `dset.set_format("tensorflow")` makes `__getitem__` return a tf.Tensor. So serializing from NumPy arrays would mean calling `dset.export()` before setting the format, which is confusing.\r\n- NumPy arrays can be serialized as their underlying datatype (int, float), while tf.Tensors must be converted to strings before serialization. This adds another step when serializing and deserializing, and removes the static-typing advantages of the TFRecord format.\r\n\r\nI think we should export directly from the underlying NumPy arrays into TFRecords, rather than using an intermediate step of tf.Tensor.\r\n\r\n_Should we serialize lists of dictionaries?_\r\n- The test_format_nested() test creates a list of dictionaries: https://github.com/huggingface/nlp/blob/911d5596f9b500e39af8642fe3d1b891758999c7/tests/test_arrow_dataset.py#L278-L288\r\n- This is difficult to serialize effectively, and I\'m not aware of any dataset that has this format. SQuAD has a dictionary of lists, such as the `answers` key. Is this necessary?'
'Thanks @thomwolf, used dset.flatten() to simplify. That handles the case of nested dictionaries, and then lists can be read into a tf.io.RaggedFeature in the case of something like squad answers.'
"@jarednielsen I just checked and indeed we don't have lists of dicts, we can just focus on the squad format as a reference then :) I'll change the test to remove this format that's not supposed to happen"
"Actually I realised that `flatten` also handles nested things like pyarrow's list<struct> so it's fine :D \r\nThis is so cool !\r\n\r\nCould you also add a test with a squad-like dataset ? As soon as we have that I think we'll be good to merge @jarednielsen :)\r\nGood job !"
'Great, done! I think this could be a great canonical way to generate a dataset.'
'I tried to match the format of Dataset.sort() and Dataset.shuffle() with the docstring. What difference are you referring to specifically?'
"Oh my bad they're fine actually (I was thinking of the backticks that we don't use in the docstrings of the transformers repo for argument names)"
'One final thing: now that we have a brand new documentation, could you just add `export` to the list of documented methods in [docs/source/package_reference/main_classes.rst](https://github.com/huggingface/nlp/blob/master/docs/source/package_reference/main_classes.rst) (so that it will appear in the docs [here](https://huggingface.co/nlp/package_reference/main_classes.html)) ?\r\n'
'Done' 'Cool thanks :)'
'Since #403 (it just got merged), we return python objects and not numpy arrays anymore (unless format="numpy" is specified).\r\nDo you think it can break the export method ? Could you try to rebase from master to run the CI to make sure it\'s fine ?'
'Good catch. I fixed it up so it works with the new format. By the way, when dset.format == "numpy", it now returns single items (like `0`) as a 0-dimensional NumPy array. Not sure if that is desired.'
"I played a little bit with the code and it works quite well :)\r\n\r\nI found two cases for which it doesn't work though:\r\n- if the features dict depth is > 2 (ex: wikisql), because `flatten` only flattens the first level of nesting (it can be fixed by calling `flatten` several times in a row, see [here](https://issues.apache.org/jira/browse/ARROW-4090))\r\n- Or if there are 2d features (ex: wikisql, `table.rows` is a sequence of sequences of strings), because tf.train.Features only support 1-d lists. That's why tensorflow-datasets flattens these 2-d features to 1-d and adds ragged features that are the shapes of the arrays, so that they can be reconstructed.\r\n\r\nI think we can ignore the 2d stuff right now (some work is being done in #363 ), but I'd like to see the `flatten` issue fixed soon\r\n"
'That seems like a bug in `pyarrow`, or at least in `flatten()`. Looks like it should be a separate PR.'
"I made `.flatten` work on our side (it calls pyarrow's flatten several times until it's really flat).\r\n\r\nThe only datasets that won't work are those with lists of lists of features, which is a rare case. Hopefully we can make this work with the multi-dimensional arrays changes we're also doing.\r\n\r\nI think we can merge now :) cc @thomwolf "] | 2020-07-02 19:26:27+00:00 | 2020-07-22 09:16:12+00:00 | 2020-07-22 09:16:12+00:00 | CONTRIBUTOR | null | Fixes https://github.com/huggingface/nlp/issues/337
Some design decisions:
- Simplified the function API to not handle sharding. It writes the entire dataset as a single TFRecord file. This simplifies the function logic and users can use other functions (`select`, `shard`, etc) to handle custom sharding or splitting.
- Use `from_generator()` instead of `from_tensor_slices()` to address the memory issues discussed in https://github.com/huggingface/nlp/issues/315 and https://github.com/huggingface/nlp/issues/193.
- Performs introspection using the values from `dataset.set_format()` to identify the TF datatypes. Currently it supports string, float, and int. If this should be extended for other datatypes, let me know.
- There are quite a few helper functions required within the `export()` method. If these are better placed in a utils file somewhere, let me know.
Also, I noticed that
```python
dataset = dataset.select(indices)
dataset.set_format("tensorflow")
# dataset._format_type is "tensorflow"
```
gives a different output than
```python
dataset.set_format("tensorflow")
dataset = dataset.select(indices)
# dataset._format_type is None
```
The latter loses the format of its parent dataset. Is there interest in making `set_format` a functional method that returns itself (can be chained), and that derived datasets maintain the format of their parent? | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 3, 'total_count': 3, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/339/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/339/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/339.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/339', 'merged_at': '2020-07-22T09:16:11Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/339.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/339'} | true |
https://api.github.com/repos/huggingface/datasets/issues/338 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/338/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/338/comments | https://api.github.com/repos/huggingface/datasets/issues/338/events | https://github.com/huggingface/datasets/pull/338 | 650,057,253 | MDExOlB1bGxSZXF1ZXN0NDQzNjIxMTEx | 338 | Run `make style` | {'avatar_url': 'https://avatars.githubusercontent.com/u/4564897?v=4', 'events_url': 'https://api.github.com/users/jarednielsen/events{/privacy}', 'followers_url': 'https://api.github.com/users/jarednielsen/followers', 'following_url': 'https://api.github.com/users/jarednielsen/following{/other_user}', 'gists_url': 'https://api.github.com/users/jarednielsen/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/jarednielsen', 'id': 4564897, 'login': 'jarednielsen', 'node_id': 'MDQ6VXNlcjQ1NjQ4OTc=', 'organizations_url': 'https://api.github.com/users/jarednielsen/orgs', 'received_events_url': 'https://api.github.com/users/jarednielsen/received_events', 'repos_url': 'https://api.github.com/users/jarednielsen/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/jarednielsen/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/jarednielsen/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/jarednielsen'} | [] | closed | false | null | [] | null | [] | 2020-07-02 16:19:47+00:00 | 2020-07-02 18:03:10+00:00 | 2020-07-02 18:03:10+00:00 | CONTRIBUTOR | null | These files get changed when I run `make style` on an unrelated PR. Upstreaming these changes so development on a different branch can be easier. | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/338/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/338/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/338.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/338', 'merged_at': '2020-07-02T18:03:10Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/338.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/338'} | true |
https://api.github.com/repos/huggingface/datasets/issues/337 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/337/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/337/comments | https://api.github.com/repos/huggingface/datasets/issues/337/events | https://github.com/huggingface/datasets/issues/337 | 650,035,887 | MDU6SXNzdWU2NTAwMzU4ODc= | 337 | [Feature request] Export Arrow dataset to TFRecords | {'avatar_url': 'https://avatars.githubusercontent.com/u/4564897?v=4', 'events_url': 'https://api.github.com/users/jarednielsen/events{/privacy}', 'followers_url': 'https://api.github.com/users/jarednielsen/followers', 'following_url': 'https://api.github.com/users/jarednielsen/following{/other_user}', 'gists_url': 'https://api.github.com/users/jarednielsen/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/jarednielsen', 'id': 4564897, 'login': 'jarednielsen', 'node_id': 'MDQ6VXNlcjQ1NjQ4OTc=', 'organizations_url': 'https://api.github.com/users/jarednielsen/orgs', 'received_events_url': 'https://api.github.com/users/jarednielsen/received_events', 'repos_url': 'https://api.github.com/users/jarednielsen/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/jarednielsen/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/jarednielsen/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/jarednielsen'} | [] | closed | false | null | [] | null | [] | 2020-07-02 15:47:12+00:00 | 2020-07-22 09:16:12+00:00 | 2020-07-22 09:16:12+00:00 | CONTRIBUTOR | null | The TFRecord generation process is error-prone and requires complex separate Python scripts to download and preprocess the data. I propose to combine the user-friendly features of `nlp` with the speed and efficiency of TFRecords. Sample API:
```python
# use these existing methods
ds = load_dataset("wikitext", "wikitext-2-raw-v1", split="train")
ds = ds.map(lambda ex: tokenizer(ex))
ds.set_format("tensorflow", columns=["input_ids", "token_type_ids", "attention_mask"])
# then add this method
ds.export(folder="/my/tfrecords", prefix="myrecord", num_shards=8, format="tfrecord")
```
which would create files like so:
```bash
/my/tfrecords/myrecord_1.tfrecord
/my/tfrecords/myrecord_2.tfrecord
...
```
I would be happy to contribute this method. We could use a similar approach for PyTorch. Thoughts? | {'+1': 3, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 3, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/337/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/337/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/336 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/336/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/336/comments | https://api.github.com/repos/huggingface/datasets/issues/336/events | https://github.com/huggingface/datasets/issues/336 | 649,914,203 | MDU6SXNzdWU2NDk5MTQyMDM= | 336 | [Dataset requests] New datasets for Open Question Answering | {'avatar_url': 'https://avatars.githubusercontent.com/u/7353373?v=4', 'events_url': 'https://api.github.com/users/thomwolf/events{/privacy}', 'followers_url': 'https://api.github.com/users/thomwolf/followers', 'following_url': 'https://api.github.com/users/thomwolf/following{/other_user}', 'gists_url': 'https://api.github.com/users/thomwolf/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/thomwolf', 'id': 7353373, 'login': 'thomwolf', 'node_id': 'MDQ6VXNlcjczNTMzNzM=', 'organizations_url': 'https://api.github.com/users/thomwolf/orgs', 'received_events_url': 'https://api.github.com/users/thomwolf/received_events', 'repos_url': 'https://api.github.com/users/thomwolf/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/thomwolf/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/thomwolf/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/thomwolf'} | [{'color': '008672', 'default': True, 'description': 'Extra attention is needed', 'id': 1935892884, 'name': 'help wanted', 'node_id': 'MDU6TGFiZWwxOTM1ODkyODg0', 'url': 'https://api.github.com/repos/huggingface/datasets/labels/help%20wanted'}
{'color': 'e99695', 'default': False, 'description': 'Requesting to add a new dataset', 'id': 2067376369, 'name': 'dataset request', 'node_id': 'MDU6TGFiZWwyMDY3Mzc2MzY5', 'url': 'https://api.github.com/repos/huggingface/datasets/labels/dataset%20request'}] | closed | false | {'avatar_url': 'https://avatars.githubusercontent.com/u/38249783?v=4', 'events_url': 'https://api.github.com/users/mariamabarham/events{/privacy}', 'followers_url': 'https://api.github.com/users/mariamabarham/followers', 'following_url': 'https://api.github.com/users/mariamabarham/following{/other_user}', 'gists_url': 'https://api.github.com/users/mariamabarham/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/mariamabarham', 'id': 38249783, 'login': 'mariamabarham', 'node_id': 'MDQ6VXNlcjM4MjQ5Nzgz', 'organizations_url': 'https://api.github.com/users/mariamabarham/orgs', 'received_events_url': 'https://api.github.com/users/mariamabarham/received_events', 'repos_url': 'https://api.github.com/users/mariamabarham/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/mariamabarham/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/mariamabarham/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/mariamabarham'} | [{'avatar_url': 'https://avatars.githubusercontent.com/u/38249783?v=4', 'events_url': 'https://api.github.com/users/mariamabarham/events{/privacy}', 'followers_url': 'https://api.github.com/users/mariamabarham/followers', 'following_url': 'https://api.github.com/users/mariamabarham/following{/other_user}', 'gists_url': 'https://api.github.com/users/mariamabarham/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/mariamabarham', 'id': 38249783, 'login': 'mariamabarham', 'node_id': 'MDQ6VXNlcjM4MjQ5Nzgz', 'organizations_url': 'https://api.github.com/users/mariamabarham/orgs', 'received_events_url': 'https://api.github.com/users/mariamabarham/received_events', 'repos_url': 'https://api.github.com/users/mariamabarham/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/mariamabarham/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/mariamabarham/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/mariamabarham'}] | null | [] | 2020-07-02 13:03:03+00:00 | 2020-07-16 09:04:22+00:00 | 2020-07-16 09:04:22+00:00 | MEMBER | null | We are still a few datasets missing for Open-Question Answering which is currently a field in strong development.
Namely, it would be really nice to add:
- WebQuestions (Berant et al., 2013) [done]
- CuratedTrec (Baudis et al. 2015) [not open-source]
- MS-MARCO (NGuyen et al. 2016) [done]
- SearchQA (Dunn et al. 2017) [done]
- FEVER (Thorne et al. 2018) - [ done]
All these datasets are cited in http://arxiv.org/abs/2005.11401 | {'+1': 1, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 1, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/336/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/336/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/335 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/335/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/335/comments | https://api.github.com/repos/huggingface/datasets/issues/335/events | https://github.com/huggingface/datasets/pull/335 | 649,765,179 | MDExOlB1bGxSZXF1ZXN0NDQzMzgwMjI1 | 335 | BioMRC Dataset presented in BioNLP 2020 ACL Workshop | {'avatar_url': 'https://avatars.githubusercontent.com/u/15162021?v=4', 'events_url': 'https://api.github.com/users/PetrosStav/events{/privacy}', 'followers_url': 'https://api.github.com/users/PetrosStav/followers', 'following_url': 'https://api.github.com/users/PetrosStav/following{/other_user}', 'gists_url': 'https://api.github.com/users/PetrosStav/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/PetrosStav', 'id': 15162021, 'login': 'PetrosStav', 'node_id': 'MDQ6VXNlcjE1MTYyMDIx', 'organizations_url': 'https://api.github.com/users/PetrosStav/orgs', 'received_events_url': 'https://api.github.com/users/PetrosStav/received_events', 'repos_url': 'https://api.github.com/users/PetrosStav/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/PetrosStav/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/PetrosStav/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/PetrosStav'} | [] | closed | false | null | [] | null | ['I fixed the issues that you pointed out, re-run all the test and pushed the fixed code :-)'
'```\r\n=================================== FAILURES ===================================\r\n___________________ AWSDatasetTest.test_load_dataset_pandas ____________________\r\n\r\nself = <tests.test_dataset_common.AWSDatasetTest testMethod=test_load_dataset_pandas>\r\ndataset_name = \'pandas\'\r\n\r\n def test_load_dataset(self, dataset_name):\r\n configs = self.dataset_tester.load_all_configs(dataset_name)[:1]\r\n> self.dataset_tester.check_load_dataset(dataset_name, configs)\r\n\r\ntests/test_dataset_common.py:231: \r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\ntests/test_dataset_common.py:125: in check_load_dataset\r\n dl_manager=mock_dl_manager, download_mode=GenerateMode.FORCE_REDOWNLOAD, ignore_verifications=True\r\n../.local/lib/python3.6/site-packages/nlp/builder.py:432: in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n../.local/lib/python3.6/site-packages/nlp/builder.py:466: in _download_and_prepare\r\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\n\r\nself = <nlp.datasets.pandas.91271af5018cf7184c27d5cd64802a1b234b3cf0e37dbca0d60f03179b13e926.pandas.Pandas object at 0x7f3b84f655c0>\r\ndl_manager = <nlp.utils.mock_download_manager.MockDownloadManager object at 0x7f3b84f3d320>\r\n\r\n def _split_generators(self, dl_manager):\r\n """ We handle string, list and dicts in datafiles\r\n """\r\n if isinstance(self.config.data_files, (str, list, tuple)):\r\n files = self.config.data_files\r\n if isinstance(files, str):\r\n files = [files]\r\n return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={"files": files})]\r\n splits = []\r\n for split_name in [nlp.Split.TRAIN, nlp.Split.VALIDATION, nlp.Split.TEST]:\r\n> if split_name in self.config.data_files:\r\nE TypeError: argument of type \'NoneType\' is not iterable\r\n\r\n../.local/lib/python3.6/site-packages/nlp/datasets/pandas/91271af5018cf7184c27d5cd64802a1b234b3cf0e37dbca0d60f03179b13e926/pandas.py:23: TypeError\r\n------------------------------ Captured log call -------------------------------\r\nINFO filelock:filelock.py:274 Lock 139893169180856 acquired on /home/circleci/.cache/huggingface/datasets/e5827d40e7a41d66bc5a2eded8dbc90694265f47d9e7cb0273ff6ff11ba426d9.aa556094028e27447a02bb38655ff97b3f4e06db1ac04c1bcdcf5b283b0f75b6.py.lock\r\nINFO nlp.utils.file_utils:file_utils.py:386 https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/pandas/pandas.py not found in cache or force_download set to True, downloading to /home/circleci/.cache/huggingface/datasets/tmpwmbk8e8d\r\nINFO nlp.utils.file_utils:file_utils.py:391 storing https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/pandas/pandas.py in cache at /home/circleci/.cache/huggingface/datasets/e5827d40e7a41d66bc5a2eded8dbc90694265f47d9e7cb0273ff6ff11ba426d9.aa556094028e27447a02bb38655ff97b3f4e06db1ac04c1bcdcf5b283b0f75b6.py\r\nINFO nlp.utils.file_utils:file_utils.py:394 creating metadata file for /home/circleci/.cache/huggingface/datasets/e5827d40e7a41d66bc5a2eded8dbc90694265f47d9e7cb0273ff6ff11ba426d9.aa556094028e27447a02bb38655ff97b3f4e06db1ac04c1bcdcf5b283b0f75b6.py\r\nINFO filelock:filelock.py:318 Lock 139893169180856 released on /home/circleci/.cache/huggingface/datasets/e5827d40e7a41d66bc5a2eded8dbc90694265f47d9e7cb0273ff6ff11ba426d9.aa556094028e27447a02bb38655ff97b3f4e06db1ac04c1bcdcf5b283b0f75b6.py.lock\r\nINFO nlp.load:load.py:157 Checking /home/circleci/.cache/huggingface/datasets/e5827d40e7a41d66bc5a2eded8dbc90694265f47d9e7cb0273ff6ff11ba426d9.aa556094028e27447a02bb38655ff97b3f4e06db1ac04c1bcdcf5b283b0f75b6.py for additional imports.\r\nINFO filelock:filelock.py:274 Lock 139893610536912 acquired on /home/circleci/.cache/huggingface/datasets/e5827d40e7a41d66bc5a2eded8dbc90694265f47d9e7cb0273ff6ff11ba426d9.aa556094028e27447a02bb38655ff97b3f4e06db1ac04c1bcdcf5b283b0f75b6.py.lock\r\nINFO nlp.load:load.py:320 Found main folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/pandas/pandas.py at /home/circleci/.local/lib/python3.6/site-packages/nlp/datasets/pandas\r\nINFO nlp.load:load.py:333 Found specific version folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/pandas/pandas.py at /home/circleci/.local/lib/python3.6/site-packages/nlp/datasets/pandas/91271af5018cf7184c27d5cd64802a1b234b3cf0e37dbca0d60f03179b13e926\r\nINFO nlp.load:load.py:346 Found script file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/pandas/pandas.py to /home/circleci/.local/lib/python3.6/site-packages/nlp/datasets/pandas/91271af5018cf7184c27d5cd64802a1b234b3cf0e37dbca0d60f03179b13e926/pandas.py\r\nINFO nlp.load:load.py:354 Couldn\'t find dataset infos file at https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/pandas/dataset_infos.json\r\nINFO nlp.load:load.py:371 Found metadata file for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/pandas/pandas.py at /home/circleci/.local/lib/python3.6/site-packages/nlp/datasets/pandas/91271af5018cf7184c27d5cd64802a1b234b3cf0e37dbca0d60f03179b13e926/pandas.json\r\nINFO filelock:filelock.py:318 Lock 139893610536912 released on /home/circleci/.cache/huggingface/datasets/e5827d40e7a41d66bc5a2eded8dbc90694265f47d9e7cb0273ff6ff11ba426d9.aa556094028e27447a02bb38655ff97b3f4e06db1ac04c1bcdcf5b283b0f75b6.py.lock\r\nINFO filelock:filelock.py:274 Lock 139893610533608 acquired on /home/circleci/.cache/huggingface/datasets/e5827d40e7a41d66bc5a2eded8dbc90694265f47d9e7cb0273ff6ff11ba426d9.aa556094028e27447a02bb38655ff97b3f4e06db1ac04c1bcdcf5b283b0f75b6.py.lock\r\nINFO nlp.utils.file_utils:file_utils.py:386 https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/pandas/pandas.py not found in cache or force_download set to True, downloading to /home/circleci/.cache/huggingface/datasets/tmp00hpyxrs\r\nINFO nlp.utils.file_utils:file_utils.py:391 storing https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/pandas/pandas.py in cache at /home/circleci/.cache/huggingface/datasets/e5827d40e7a41d66bc5a2eded8dbc90694265f47d9e7cb0273ff6ff11ba426d9.aa556094028e27447a02bb38655ff97b3f4e06db1ac04c1bcdcf5b283b0f75b6.py\r\nINFO nlp.utils.file_utils:file_utils.py:394 creating metadata file for /home/circleci/.cache/huggingface/datasets/e5827d40e7a41d66bc5a2eded8dbc90694265f47d9e7cb0273ff6ff11ba426d9.aa556094028e27447a02bb38655ff97b3f4e06db1ac04c1bcdcf5b283b0f75b6.py\r\nINFO filelock:filelock.py:318 Lock 139893610533608 released on /home/circleci/.cache/huggingface/datasets/e5827d40e7a41d66bc5a2eded8dbc90694265f47d9e7cb0273ff6ff11ba426d9.aa556094028e27447a02bb38655ff97b3f4e06db1ac04c1bcdcf5b283b0f75b6.py.lock\r\nINFO nlp.load:load.py:157 Checking /home/circleci/.cache/huggingface/datasets/e5827d40e7a41d66bc5a2eded8dbc90694265f47d9e7cb0273ff6ff11ba426d9.aa556094028e27447a02bb38655ff97b3f4e06db1ac04c1bcdcf5b283b0f75b6.py for additional imports.\r\nINFO filelock:filelock.py:274 Lock 139893610371224 acquired on /home/circleci/.cache/huggingface/datasets/e5827d40e7a41d66bc5a2eded8dbc90694265f47d9e7cb0273ff6ff11ba426d9.aa556094028e27447a02bb38655ff97b3f4e06db1ac04c1bcdcf5b283b0f75b6.py.lock\r\nINFO nlp.load:load.py:320 Found main folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/pandas/pandas.py at /home/circleci/.local/lib/python3.6/site-packages/nlp/datasets/pandas\r\nINFO nlp.load:load.py:333 Found specific version folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/pandas/pandas.py at /home/circleci/.local/lib/python3.6/site-packages/nlp/datasets/pandas/91271af5018cf7184c27d5cd64802a1b234b3cf0e37dbca0d60f03179b13e926\r\nINFO nlp.load:load.py:346 Found script file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/pandas/pandas.py to /home/circleci/.local/lib/python3.6/site-packages/nlp/datasets/pandas/91271af5018cf7184c27d5cd64802a1b234b3cf0e37dbca0d60f03179b13e926/pandas.py\r\nINFO nlp.load:load.py:354 Couldn\'t find dataset infos file at https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/pandas/dataset_infos.json\r\nINFO nlp.load:load.py:371 Found metadata file for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/pandas/pandas.py at /home/circleci/.local/lib/python3.6/site-packages/nlp/datasets/pandas/91271af5018cf7184c27d5cd64802a1b234b3cf0e37dbca0d60f03179b13e926/pandas.json\r\nINFO filelock:filelock.py:318 Lock 139893610371224 released on /home/circleci/.cache/huggingface/datasets/e5827d40e7a41d66bc5a2eded8dbc90694265f47d9e7cb0273ff6ff11ba426d9.aa556094028e27447a02bb38655ff97b3f4e06db1ac04c1bcdcf5b283b0f75b6.py.lock\r\nWARNING nlp.builder:builder.py:215 Using custom data configuration default\r\nINFO nlp.builder:builder.py:349 Generating dataset pandas (/tmp/tmp296h8eeg/pandas/default/0.0.0)\r\nINFO nlp.builder:builder.py:397 Dataset not on Hf google storage. Downloading and preparing it from source\r\n____________________ AWSDatasetTest.test_load_dataset_text _____________________\r\n\r\nself = <tests.test_dataset_common.AWSDatasetTest testMethod=test_load_dataset_text>\r\ndataset_name = \'text\'\r\n\r\n def test_load_dataset(self, dataset_name):\r\n configs = self.dataset_tester.load_all_configs(dataset_name)[:1]\r\n> self.dataset_tester.check_load_dataset(dataset_name, configs)\r\n\r\ntests/test_dataset_common.py:231: \r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\ntests/test_dataset_common.py:125: in check_load_dataset\r\n dl_manager=mock_dl_manager, download_mode=GenerateMode.FORCE_REDOWNLOAD, ignore_verifications=True\r\n../.local/lib/python3.6/site-packages/nlp/builder.py:432: in download_and_prepare\r\n dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n../.local/lib/python3.6/site-packages/nlp/builder.py:466: in _download_and_prepare\r\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\n\r\nself = <nlp.datasets.text.bf5568367c6707640e5601a44ed0af98f40a8483db81a7db99b85fab6606fc8b.text.Text object at 0x7f3b6a111550>\r\ndl_manager = <nlp.utils.mock_download_manager.MockDownloadManager object at 0x7f3b85582908>\r\n\r\n def _split_generators(self, dl_manager):\r\n """ The `datafiles` kwarg in load_dataset() can be a str, List[str], Dict[str,str], or Dict[str,List[str]].\r\n \r\n If str or List[str], then the dataset returns only the \'train\' split.\r\n If dict, then keys should be from the `nlp.Split` enum.\r\n """\r\n if isinstance(self.config.data_files, (str, list, tuple)):\r\n # Handle case with only one split\r\n files = self.config.data_files\r\n if isinstance(files, str):\r\n files = [files]\r\n return [nlp.SplitGenerator(name=nlp.Split.TRAIN, gen_kwargs={"files": files})]\r\n else:\r\n # Handle case with several splits and a dict mapping\r\n splits = []\r\n for split_name in [nlp.Split.TRAIN, nlp.Split.VALIDATION, nlp.Split.TEST]:\r\n> if split_name in self.config.data_files:\r\nE TypeError: argument of type \'NoneType\' is not iterable\r\n\r\n../.local/lib/python3.6/site-packages/nlp/datasets/text/bf5568367c6707640e5601a44ed0af98f40a8483db81a7db99b85fab6606fc8b/text.py:24: TypeError\r\n------------------------------ Captured log call -------------------------------\r\nINFO filelock:filelock.py:274 Lock 139893159303656 acquired on /home/circleci/.cache/huggingface/datasets/3e34209a2741375a1db1ff03bf1abba1a9bd0e6016912d3ead0114b9d1ca2685.88f858fae8ed77fdff99fe23b726fce01f73388251e0a09a226e6f82cd4ffe6c.py.lock\r\nINFO nlp.utils.file_utils:file_utils.py:386 https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/text/text.py not found in cache or force_download set to True, downloading to /home/circleci/.cache/huggingface/datasets/tmpk63omy4v\r\nINFO nlp.utils.file_utils:file_utils.py:391 storing https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/text/text.py in cache at /home/circleci/.cache/huggingface/datasets/3e34209a2741375a1db1ff03bf1abba1a9bd0e6016912d3ead0114b9d1ca2685.88f858fae8ed77fdff99fe23b726fce01f73388251e0a09a226e6f82cd4ffe6c.py\r\nINFO nlp.utils.file_utils:file_utils.py:394 creating metadata file for /home/circleci/.cache/huggingface/datasets/3e34209a2741375a1db1ff03bf1abba1a9bd0e6016912d3ead0114b9d1ca2685.88f858fae8ed77fdff99fe23b726fce01f73388251e0a09a226e6f82cd4ffe6c.py\r\nINFO filelock:filelock.py:318 Lock 139893159303656 released on /home/circleci/.cache/huggingface/datasets/3e34209a2741375a1db1ff03bf1abba1a9bd0e6016912d3ead0114b9d1ca2685.88f858fae8ed77fdff99fe23b726fce01f73388251e0a09a226e6f82cd4ffe6c.py.lock\r\nINFO nlp.load:load.py:157 Checking /home/circleci/.cache/huggingface/datasets/3e34209a2741375a1db1ff03bf1abba1a9bd0e6016912d3ead0114b9d1ca2685.88f858fae8ed77fdff99fe23b726fce01f73388251e0a09a226e6f82cd4ffe6c.py for additional imports.\r\nINFO filelock:filelock.py:274 Lock 139893159171352 acquired on /home/circleci/.cache/huggingface/datasets/3e34209a2741375a1db1ff03bf1abba1a9bd0e6016912d3ead0114b9d1ca2685.88f858fae8ed77fdff99fe23b726fce01f73388251e0a09a226e6f82cd4ffe6c.py.lock\r\nINFO nlp.load:load.py:320 Found main folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/text/text.py at /home/circleci/.local/lib/python3.6/site-packages/nlp/datasets/text\r\nINFO nlp.load:load.py:333 Found specific version folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/text/text.py at /home/circleci/.local/lib/python3.6/site-packages/nlp/datasets/text/bf5568367c6707640e5601a44ed0af98f40a8483db81a7db99b85fab6606fc8b\r\nINFO nlp.load:load.py:346 Found script file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/text/text.py to /home/circleci/.local/lib/python3.6/site-packages/nlp/datasets/text/bf5568367c6707640e5601a44ed0af98f40a8483db81a7db99b85fab6606fc8b/text.py\r\nINFO nlp.load:load.py:354 Couldn\'t find dataset infos file at https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/text/dataset_infos.json\r\nINFO nlp.load:load.py:371 Found metadata file for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/text/text.py at /home/circleci/.local/lib/python3.6/site-packages/nlp/datasets/text/bf5568367c6707640e5601a44ed0af98f40a8483db81a7db99b85fab6606fc8b/text.json\r\nINFO filelock:filelock.py:318 Lock 139893159171352 released on /home/circleci/.cache/huggingface/datasets/3e34209a2741375a1db1ff03bf1abba1a9bd0e6016912d3ead0114b9d1ca2685.88f858fae8ed77fdff99fe23b726fce01f73388251e0a09a226e6f82cd4ffe6c.py.lock\r\nINFO filelock:filelock.py:274 Lock 139893618479176 acquired on /home/circleci/.cache/huggingface/datasets/3e34209a2741375a1db1ff03bf1abba1a9bd0e6016912d3ead0114b9d1ca2685.88f858fae8ed77fdff99fe23b726fce01f73388251e0a09a226e6f82cd4ffe6c.py.lock\r\nINFO nlp.utils.file_utils:file_utils.py:386 https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/text/text.py not found in cache or force_download set to True, downloading to /home/circleci/.cache/huggingface/datasets/tmpkeykru_f\r\nINFO nlp.utils.file_utils:file_utils.py:391 storing https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/text/text.py in cache at /home/circleci/.cache/huggingface/datasets/3e34209a2741375a1db1ff03bf1abba1a9bd0e6016912d3ead0114b9d1ca2685.88f858fae8ed77fdff99fe23b726fce01f73388251e0a09a226e6f82cd4ffe6c.py\r\nINFO nlp.utils.file_utils:file_utils.py:394 creating metadata file for /home/circleci/.cache/huggingface/datasets/3e34209a2741375a1db1ff03bf1abba1a9bd0e6016912d3ead0114b9d1ca2685.88f858fae8ed77fdff99fe23b726fce01f73388251e0a09a226e6f82cd4ffe6c.py\r\nINFO filelock:filelock.py:318 Lock 139893618479176 released on /home/circleci/.cache/huggingface/datasets/3e34209a2741375a1db1ff03bf1abba1a9bd0e6016912d3ead0114b9d1ca2685.88f858fae8ed77fdff99fe23b726fce01f73388251e0a09a226e6f82cd4ffe6c.py.lock\r\nINFO nlp.load:load.py:157 Checking /home/circleci/.cache/huggingface/datasets/3e34209a2741375a1db1ff03bf1abba1a9bd0e6016912d3ead0114b9d1ca2685.88f858fae8ed77fdff99fe23b726fce01f73388251e0a09a226e6f82cd4ffe6c.py for additional imports.\r\nINFO filelock:filelock.py:274 Lock 139893618423848 acquired on /home/circleci/.cache/huggingface/datasets/3e34209a2741375a1db1ff03bf1abba1a9bd0e6016912d3ead0114b9d1ca2685.88f858fae8ed77fdff99fe23b726fce01f73388251e0a09a226e6f82cd4ffe6c.py.lock\r\nINFO nlp.load:load.py:320 Found main folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/text/text.py at /home/circleci/.local/lib/python3.6/site-packages/nlp/datasets/text\r\nINFO nlp.load:load.py:333 Found specific version folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/text/text.py at /home/circleci/.local/lib/python3.6/site-packages/nlp/datasets/text/bf5568367c6707640e5601a44ed0af98f40a8483db81a7db99b85fab6606fc8b\r\nINFO nlp.load:load.py:346 Found script file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/text/text.py to /home/circleci/.local/lib/python3.6/site-packages/nlp/datasets/text/bf5568367c6707640e5601a44ed0af98f40a8483db81a7db99b85fab6606fc8b/text.py\r\nINFO nlp.load:load.py:354 Couldn\'t find dataset infos file at https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/text/dataset_infos.json\r\nINFO nlp.load:load.py:371 Found metadata file for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/text/text.py at /home/circleci/.local/lib/python3.6/site-packages/nlp/datasets/text/bf5568367c6707640e5601a44ed0af98f40a8483db81a7db99b85fab6606fc8b/text.json\r\nINFO filelock:filelock.py:318 Lock 139893618423848 released on /home/circleci/.cache/huggingface/datasets/3e34209a2741375a1db1ff03bf1abba1a9bd0e6016912d3ead0114b9d1ca2685.88f858fae8ed77fdff99fe23b726fce01f73388251e0a09a226e6f82cd4ffe6c.py.lock\r\nWARNING nlp.builder:builder.py:215 Using custom data configuration default\r\nINFO nlp.builder:builder.py:349 Generating dataset text (/tmp/tmpbu67mvue/text/default/0.0.0)\r\nINFO nlp.builder:builder.py:397 Dataset not on Hf google storage. Downloading and preparing it from source\r\n=============================== warnings summary ===============================\r\n/home/circleci/.local/lib/python3.6/site-packages/tensorflow/python/pywrap_tensorflow_internal.py:15\r\n /home/circleci/.local/lib/python3.6/site-packages/tensorflow/python/pywrap_tensorflow_internal.py:15: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module\'s documentation for alternative uses\r\n import imp\r\n\r\ntests/test_dataset_common.py::LocalDatasetTest::test_builder_class_tydiqa\r\n /home/circleci/.local/lib/python3.6/site-packages/nlp/datasets/tydiqa/42d88245bde7c0db6c0d48c822dcaa26c7299e0b40cace7e8d6a9e3628135125/tydiqa.py:85: DeprecationWarning: invalid escape sequence \\G\r\n """\r\n\r\ntests/test_dataset_common.py::AWSDatasetTest::test_builder_class_mwsc\r\n /home/circleci/.local/lib/python3.6/site-packages/nlp/datasets/mwsc/53c0daac11b6794ff62b52a3a46c4f9da1bef68fd664a2f97b8918917aead715/mwsc.py:70: DeprecationWarning: invalid escape sequence \\[\r\n pattern = "\\[.*\\]"\r\n\r\ntests/test_dataset_common.py::AWSDatasetTest::test_builder_class_squadshifts\r\n /home/circleci/.local/lib/python3.6/site-packages/nlp/datasets/squadshifts/15536d7296a785325b99f6d84dfdceafa427419dd6caad110eabb5e5b4156cc2/squadshifts.py:47: DeprecationWarning: invalid escape sequence \\ \r\n """\r\n\r\n-- Docs: https://docs.pytest.org/en/latest/warnings.html\r\n=========================== short test summary info ============================\r\nFAILED tests/test_dataset_common.py::AWSDatasetTest::test_load_dataset_pandas\r\nFAILED tests/test_dataset_common.py::AWSDatasetTest::test_load_dataset_text\r\n===== 2 failed, 934 passed, 516 skipped, 4 warnings in 1562.46s (0:26:02) ======\r\n\r\nExited with code exit status 1\r\nCircleCI received exit code 1\r\n```\r\nI get this failed test on CircleCI , but all the tests that I run locally where successful. The error also seems not to have any, obvious at least, connection with my code.\r\n\r\nAny suggestions? Thanks! :-) '] | 2020-07-02 09:03:41+00:00 | 2020-07-15 08:02:07+00:00 | 2020-07-15 08:02:07+00:00 | CONTRIBUTOR | null | null | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/335/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/335/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/335.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/335', 'merged_at': '2020-07-15T08:02:07Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/335.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/335'} | true |
https://api.github.com/repos/huggingface/datasets/issues/334 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/334/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/334/comments | https://api.github.com/repos/huggingface/datasets/issues/334/events | https://github.com/huggingface/datasets/pull/334 | 649,661,791 | MDExOlB1bGxSZXF1ZXN0NDQzMjk1NjQ0 | 334 | Add dataset.shard() method | {'avatar_url': 'https://avatars.githubusercontent.com/u/4564897?v=4', 'events_url': 'https://api.github.com/users/jarednielsen/events{/privacy}', 'followers_url': 'https://api.github.com/users/jarednielsen/followers', 'following_url': 'https://api.github.com/users/jarednielsen/following{/other_user}', 'gists_url': 'https://api.github.com/users/jarednielsen/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/jarednielsen', 'id': 4564897, 'login': 'jarednielsen', 'node_id': 'MDQ6VXNlcjQ1NjQ4OTc=', 'organizations_url': 'https://api.github.com/users/jarednielsen/orgs', 'received_events_url': 'https://api.github.com/users/jarednielsen/received_events', 'repos_url': 'https://api.github.com/users/jarednielsen/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/jarednielsen/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/jarednielsen/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/jarednielsen'} | [] | closed | false | null | [] | null | ['Great, done!'] | 2020-07-02 06:05:19+00:00 | 2020-07-06 12:35:36+00:00 | 2020-07-06 12:35:36+00:00 | CONTRIBUTOR | null | Fixes https://github.com/huggingface/nlp/issues/312 | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/334/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/334/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/334.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/334', 'merged_at': '2020-07-06T12:35:36Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/334.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/334'} | true |
https://api.github.com/repos/huggingface/datasets/issues/333 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/333/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/333/comments | https://api.github.com/repos/huggingface/datasets/issues/333/events | https://github.com/huggingface/datasets/pull/333 | 649,236,516 | MDExOlB1bGxSZXF1ZXN0NDQyOTE1NDQ0 | 333 | fix variable name typo | {'avatar_url': 'https://avatars.githubusercontent.com/u/10676103?v=4', 'events_url': 'https://api.github.com/users/stas00/events{/privacy}', 'followers_url': 'https://api.github.com/users/stas00/followers', 'following_url': 'https://api.github.com/users/stas00/following{/other_user}', 'gists_url': 'https://api.github.com/users/stas00/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/stas00', 'id': 10676103, 'login': 'stas00', 'node_id': 'MDQ6VXNlcjEwNjc2MTAz', 'organizations_url': 'https://api.github.com/users/stas00/orgs', 'received_events_url': 'https://api.github.com/users/stas00/received_events', 'repos_url': 'https://api.github.com/users/stas00/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/stas00/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/stas00/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/stas00'} | [] | closed | false | null | [] | null | ['Good catch :)\r\nI think there is another occurence that needs to be fixed in the second gist (line 4924 of the notebook file):\r\n```python\r\nbleu = nlp.load_metric(...)\r\n```'
'Was fixed in e16f79b5f7fc12a6a30c777722be46897a272e6f\r\nClosing it.'] | 2020-07-01 19:13:50+00:00 | 2020-07-24 15:43:31+00:00 | 2020-07-24 08:32:16+00:00 | CONTRIBUTOR | null | null | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/333/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/333/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/333.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/333', 'merged_at': None, 'patch_url': 'https://github.com/huggingface/datasets/pull/333.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/333'} | true |
https://api.github.com/repos/huggingface/datasets/issues/332 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/332/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/332/comments | https://api.github.com/repos/huggingface/datasets/issues/332/events | https://github.com/huggingface/datasets/pull/332 | 649,140,135 | MDExOlB1bGxSZXF1ZXN0NDQyODMwMzMz | 332 | Add wiki_dpr | {'avatar_url': 'https://avatars.githubusercontent.com/u/42851186?v=4', 'events_url': 'https://api.github.com/users/lhoestq/events{/privacy}', 'followers_url': 'https://api.github.com/users/lhoestq/followers', 'following_url': 'https://api.github.com/users/lhoestq/following{/other_user}', 'gists_url': 'https://api.github.com/users/lhoestq/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/lhoestq', 'id': 42851186, 'login': 'lhoestq', 'node_id': 'MDQ6VXNlcjQyODUxMTg2', 'organizations_url': 'https://api.github.com/users/lhoestq/orgs', 'received_events_url': 'https://api.github.com/users/lhoestq/received_events', 'repos_url': 'https://api.github.com/users/lhoestq/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/lhoestq/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/lhoestq/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/lhoestq'} | [] | closed | false | null | [] | null | ["The two configurations don't have the same sizes, I may change that so that they both have 21015300 examples for convenience, even though it's supposed to have 21015324 examples in total.\r\n\r\nOne configuration only has 21015300 examples because it seems that the embeddings of the last 24 examples are missing."
"It's ok to merge now imo. I'll make another PR if we find a way to have the missing embeddings"] | 2020-07-01 17:12:00+00:00 | 2020-07-06 12:21:17+00:00 | 2020-07-06 12:21:16+00:00 | MEMBER | null | Presented in the [Dense Passage Retrieval paper](https://arxiv.org/pdf/2004.04906.pdf), this dataset consists in 21M passages from the english wikipedia along with their 768-dim embeddings computed using DPR's context encoder.
Note on the implementation:
- There are two configs: with and without the embeddings (73GB vs 14GB)
- I used a non-fixed-size sequence of floats to describe the feature format of the embeddings. I wanted to use fixed-size sequences but I had issues with reading the arrow file afterwards (for example `dataset[0]` was crashing)
- I added the case for lists of urls as input of the download_manager | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/332/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/332/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/332.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/332', 'merged_at': '2020-07-06T12:21:16Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/332.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/332'} | true |
https://api.github.com/repos/huggingface/datasets/issues/331 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/331/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/331/comments | https://api.github.com/repos/huggingface/datasets/issues/331/events | https://github.com/huggingface/datasets/issues/331 | 648,533,199 | MDU6SXNzdWU2NDg1MzMxOTk= | 331 | Loading CNN/Daily Mail dataset produces `nlp.utils.info_utils.NonMatchingSplitsSizesError` | {'avatar_url': 'https://avatars.githubusercontent.com/u/13238952?v=4', 'events_url': 'https://api.github.com/users/jxmorris12/events{/privacy}', 'followers_url': 'https://api.github.com/users/jxmorris12/followers', 'following_url': 'https://api.github.com/users/jxmorris12/following{/other_user}', 'gists_url': 'https://api.github.com/users/jxmorris12/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/jxmorris12', 'id': 13238952, 'login': 'jxmorris12', 'node_id': 'MDQ6VXNlcjEzMjM4OTUy', 'organizations_url': 'https://api.github.com/users/jxmorris12/orgs', 'received_events_url': 'https://api.github.com/users/jxmorris12/received_events', 'repos_url': 'https://api.github.com/users/jxmorris12/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/jxmorris12/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/jxmorris12/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/jxmorris12'} | [{'color': '2edb81', 'default': False, 'description': 'A bug in a dataset script provided in the library', 'id': 2067388877, 'name': 'dataset bug', 'node_id': 'MDU6TGFiZWwyMDY3Mzg4ODc3', 'url': 'https://api.github.com/repos/huggingface/datasets/labels/dataset%20bug'}] | closed | false | null | [] | null | ["I couldn't reproduce on my side.\r\nIt looks like you were not able to generate all the examples, and you have the problem for each split train-test-validation.\r\nCould you try to enable logging, try again and send the logs ?\r\n```python\r\nimport logging\r\nlogging.basicConfig(level=logging.INFO)\r\n```"
'here\'s the log\r\n```\r\n>>> import nlp\r\nimport logging\r\nlogging.basicConfig(level=logging.INFO)\r\nnlp.load_dataset(\'cnn_dailymail\', \'3.0.0\')\r\n>>> import logging\r\n>>> logging.basicConfig(level=logging.INFO)\r\n>>> nlp.load_dataset(\'cnn_dailymail\', \'3.0.0\')\r\nINFO:nlp.load:Checking /u/jm8wx/.cache/huggingface/datasets/720d2e20d8dc6d98f21195a39cc934bb41dd0a40b57ea3d323661a7c5d70522c.d44c2417f4e0fe938ede0a684dcbb1fa9b4789de22e8a99c43103d4b4c374b3b.py for additional imports.\r\nINFO:filelock:Lock 140443095301136 acquired on /u/jm8wx/.cache/huggingface/datasets/720d2e20d8dc6d98f21195a39cc934bb41dd0a40b57ea3d323661a7c5d70522c.d44c2417f4e0fe938ede0a684dcbb1fa9b4789de22e8a99c43103d4b4c374b3b.py.lock\r\nINFO:nlp.load:Found main folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/cnn_dailymail/cnn_dailymail.py at /p/qdata/jm8wx/datasets/nlp/src/nlp/datasets/cnn_dailymail\r\nINFO:nlp.load:Found specific version folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/cnn_dailymail/cnn_dailymail.py at /p/qdata/jm8wx/datasets/nlp/src/nlp/datasets/cnn_dailymail/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad\r\nINFO:nlp.load:Found script file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/cnn_dailymail/cnn_dailymail.py to /p/qdata/jm8wx/datasets/nlp/src/nlp/datasets/cnn_dailymail/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad/cnn_dailymail.py\r\nINFO:nlp.load:Updating dataset infos file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/cnn_dailymail/dataset_infos.json to /p/qdata/jm8wx/datasets/nlp/src/nlp/datasets/cnn_dailymail/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad/dataset_infos.json\r\nINFO:nlp.load:Found metadata file for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/cnn_dailymail/cnn_dailymail.py at /p/qdata/jm8wx/datasets/nlp/src/nlp/datasets/cnn_dailymail/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad/cnn_dailymail.json\r\nINFO:filelock:Lock 140443095301136 released on /u/jm8wx/.cache/huggingface/datasets/720d2e20d8dc6d98f21195a39cc934bb41dd0a40b57ea3d323661a7c5d70522c.d44c2417f4e0fe938ede0a684dcbb1fa9b4789de22e8a99c43103d4b4c374b3b.py.lock\r\nINFO:nlp.info:Loading Dataset Infos from /p/qdata/jm8wx/datasets/nlp/src/nlp/datasets/cnn_dailymail/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad\r\nINFO:nlp.builder:Generating dataset cnn_dailymail (/u/jm8wx/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0)\r\nINFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source\r\nDownloading and preparing dataset cnn_dailymail/3.0.0 (download: 558.32 MiB, generated: 1.26 GiB, total: 1.81 GiB) to /u/jm8wx/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0...\r\nINFO:nlp.utils.info_utils:All the checksums matched successfully.\r\nINFO:nlp.builder:Generating split train\r\nINFO:nlp.arrow_writer:Done writing 285161 examples in 1240618482 bytes /u/jm8wx/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0.incomplete/cnn_dailymail-train.arrow.\r\nINFO:nlp.builder:Generating split validation\r\nINFO:nlp.arrow_writer:Done writing 13255 examples in 56637485 bytes /u/jm8wx/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0.incomplete/cnn_dailymail-validation.arrow.\r\nINFO:nlp.builder:Generating split test\r\nINFO:nlp.arrow_writer:Done writing 11379 examples in 48931393 bytes /u/jm8wx/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0.incomplete/cnn_dailymail-test.arrow.\r\nTraceback (most recent call last):\r\n File "<stdin>", line 1, in <module>\r\n File "/p/qdata/jm8wx/datasets/nlp/src/nlp/load.py", line 520, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File "/p/qdata/jm8wx/datasets/nlp/src/nlp/builder.py", line 431, in download_and_prepare\r\n self._download_and_prepare(\r\n File "/p/qdata/jm8wx/datasets/nlp/src/nlp/builder.py", line 488, in _download_and_prepare\r\n verify_splits(self.info.splits, split_dict)\r\n File "/p/qdata/jm8wx/datasets/nlp/src/nlp/utils/info_utils.py", line 70, in verify_splits\r\n raise NonMatchingSplitsSizesError(str(bad_splits))\r\nnlp.utils.info_utils.NonMatchingSplitsSizesError: [{\'expected\': SplitInfo(name=\'test\', num_bytes=49424491, num_examples=11490, dataset_name=\'cnn_dailymail\'), \'recorded\': SplitInfo(name=\'test\', num_bytes=48931393, num_examples=11379, dataset_name=\'cnn_dailymail\')}, {\'expected\': SplitInfo(name=\'train\', num_bytes=1249178681, num_examples=287113, dataset_name=\'cnn_dailymail\'), \'recorded\': SplitInfo(name=\'train\', num_bytes=1240618482, num_examples=285161, dataset_name=\'cnn_dailymail\')}, {\'expected\': SplitInfo(name=\'validation\', num_bytes=57149241, num_examples=13368, dataset_name=\'cnn_dailymail\'), \'recorded\': SplitInfo(name=\'validation\', num_bytes=56637485, num_examples=13255, dataset_name=\'cnn_dailymail\')}]\r\n```'
'> here\'s the log\r\n> \r\n> ```\r\n> >>> import nlp\r\n> import logging\r\n> logging.basicConfig(level=logging.INFO)\r\n> nlp.load_dataset(\'cnn_dailymail\', \'3.0.0\')\r\n> >>> import logging\r\n> >>> logging.basicConfig(level=logging.INFO)\r\n> >>> nlp.load_dataset(\'cnn_dailymail\', \'3.0.0\')\r\n> INFO:nlp.load:Checking /u/jm8wx/.cache/huggingface/datasets/720d2e20d8dc6d98f21195a39cc934bb41dd0a40b57ea3d323661a7c5d70522c.d44c2417f4e0fe938ede0a684dcbb1fa9b4789de22e8a99c43103d4b4c374b3b.py for additional imports.\r\n> INFO:filelock:Lock 140443095301136 acquired on /u/jm8wx/.cache/huggingface/datasets/720d2e20d8dc6d98f21195a39cc934bb41dd0a40b57ea3d323661a7c5d70522c.d44c2417f4e0fe938ede0a684dcbb1fa9b4789de22e8a99c43103d4b4c374b3b.py.lock\r\n> INFO:nlp.load:Found main folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/cnn_dailymail/cnn_dailymail.py at /p/qdata/jm8wx/datasets/nlp/src/nlp/datasets/cnn_dailymail\r\n> INFO:nlp.load:Found specific version folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/cnn_dailymail/cnn_dailymail.py at /p/qdata/jm8wx/datasets/nlp/src/nlp/datasets/cnn_dailymail/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad\r\n> INFO:nlp.load:Found script file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/cnn_dailymail/cnn_dailymail.py to /p/qdata/jm8wx/datasets/nlp/src/nlp/datasets/cnn_dailymail/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad/cnn_dailymail.py\r\n> INFO:nlp.load:Updating dataset infos file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/cnn_dailymail/dataset_infos.json to /p/qdata/jm8wx/datasets/nlp/src/nlp/datasets/cnn_dailymail/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad/dataset_infos.json\r\n> INFO:nlp.load:Found metadata file for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/cnn_dailymail/cnn_dailymail.py at /p/qdata/jm8wx/datasets/nlp/src/nlp/datasets/cnn_dailymail/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad/cnn_dailymail.json\r\n> INFO:filelock:Lock 140443095301136 released on /u/jm8wx/.cache/huggingface/datasets/720d2e20d8dc6d98f21195a39cc934bb41dd0a40b57ea3d323661a7c5d70522c.d44c2417f4e0fe938ede0a684dcbb1fa9b4789de22e8a99c43103d4b4c374b3b.py.lock\r\n> INFO:nlp.info:Loading Dataset Infos from /p/qdata/jm8wx/datasets/nlp/src/nlp/datasets/cnn_dailymail/9645e0bc96f647decf46541f6f4bef6936ee82ace653ac362bab03309a46d4ad\r\n> INFO:nlp.builder:Generating dataset cnn_dailymail (/u/jm8wx/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0)\r\n> INFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source\r\n> Downloading and preparing dataset cnn_dailymail/3.0.0 (download: 558.32 MiB, generated: 1.26 GiB, total: 1.81 GiB) to /u/jm8wx/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0...\r\n> INFO:nlp.utils.info_utils:All the checksums matched successfully.\r\n> INFO:nlp.builder:Generating split train\r\n> INFO:nlp.arrow_writer:Done writing 285161 examples in 1240618482 bytes /u/jm8wx/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0.incomplete/cnn_dailymail-train.arrow.\r\n> INFO:nlp.builder:Generating split validation\r\n> INFO:nlp.arrow_writer:Done writing 13255 examples in 56637485 bytes /u/jm8wx/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0.incomplete/cnn_dailymail-validation.arrow.\r\n> INFO:nlp.builder:Generating split test\r\n> INFO:nlp.arrow_writer:Done writing 11379 examples in 48931393 bytes /u/jm8wx/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0.incomplete/cnn_dailymail-test.arrow.\r\n> Traceback (most recent call last):\r\n> File "<stdin>", line 1, in <module>\r\n> File "/p/qdata/jm8wx/datasets/nlp/src/nlp/load.py", line 520, in load_dataset\r\n> builder_instance.download_and_prepare(\r\n> File "/p/qdata/jm8wx/datasets/nlp/src/nlp/builder.py", line 431, in download_and_prepare\r\n> self._download_and_prepare(\r\n> File "/p/qdata/jm8wx/datasets/nlp/src/nlp/builder.py", line 488, in _download_and_prepare\r\n> verify_splits(self.info.splits, split_dict)\r\n> File "/p/qdata/jm8wx/datasets/nlp/src/nlp/utils/info_utils.py", line 70, in verify_splits\r\n> raise NonMatchingSplitsSizesError(str(bad_splits))\r\n> nlp.utils.info_utils.NonMatchingSplitsSizesError: [{\'expected\': SplitInfo(name=\'test\', num_bytes=49424491, num_examples=11490, dataset_name=\'cnn_dailymail\'), \'recorded\': SplitInfo(name=\'test\', num_bytes=48931393, num_examples=11379, dataset_name=\'cnn_dailymail\')}, {\'expected\': SplitInfo(name=\'train\', num_bytes=1249178681, num_examples=287113, dataset_name=\'cnn_dailymail\'), \'recorded\': SplitInfo(name=\'train\', num_bytes=1240618482, num_examples=285161, dataset_name=\'cnn_dailymail\')}, {\'expected\': SplitInfo(name=\'validation\', num_bytes=57149241, num_examples=13368, dataset_name=\'cnn_dailymail\'), \'recorded\': SplitInfo(name=\'validation\', num_bytes=56637485, num_examples=13255, dataset_name=\'cnn_dailymail\')}]\r\n> ```\r\n\r\nWith `nlp == 0.3.0` version, I\'m not able to reproduce this error on my side.\r\nWhich version are you using for reproducing your bug?\r\n\r\n```\r\n>> nlp.load_dataset(\'cnn_dailymail\', \'3.0.0\')\r\n\r\n8.90k/8.90k [00:18<00:00, 486B/s]\r\n\r\nDownloading: 100%\r\n9.37k/9.37k [00:00<00:00, 234kB/s]\r\n\r\nDownloading and preparing dataset cnn_dailymail/3.0.0 (download: 558.32 MiB, generated: 1.26 GiB, total: 1.81 GiB) to /root/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0...\r\nDownloading:\r\n159M/? [00:09<00:00, 16.7MB/s]\r\n\r\nDownloading:\r\n376M/? [00:06<00:00, 62.6MB/s]\r\n\r\nDownloading:\r\n2.11M/? [00:06<00:00, 333kB/s]\r\n\r\nDownloading:\r\n46.4M/? [00:02<00:00, 18.4MB/s]\r\n\r\nDownloading:\r\n2.43M/? [00:00<00:00, 2.62MB/s]\r\n\r\nDataset cnn_dailymail downloaded and prepared to /root/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0. Subsequent calls will reuse this data.\r\n{\'test\': Dataset(schema: {\'article\': \'string\', \'highlights\': \'string\'}, num_rows: 11490),\r\n \'train\': Dataset(schema: {\'article\': \'string\', \'highlights\': \'string\'}, num_rows: 287113),\r\n \'validation\': Dataset(schema: {\'article\': \'string\', \'highlights\': \'string\'}, num_rows: 13368)}\r\n\r\n>> ...\r\n\r\n```'
"In general if some examples are missing after processing (hence causing the `NonMatchingSplitsSizesError `), it is often due to either\r\n1) corrupted cached files\r\n2) decoding errors\r\n\r\nI just checked the dataset script for code that could lead to decoding errors but I couldn't find any. Before we try to dive more into the processing of the dataset, could you try to clear your cache ? Just to make sure that it isn't 1)"
'Yes thanks for the support! I cleared out my cache folder and everything works fine now'] | 2020-06-30 22:21:33+00:00 | 2020-07-09 13:03:40+00:00 | 2020-07-09 13:03:40+00:00 | CONTRIBUTOR | null | ```
>>> import nlp
>>> nlp.load_dataset('cnn_dailymail', '3.0.0')
Downloading and preparing dataset cnn_dailymail/3.0.0 (download: 558.32 MiB, generated: 1.26 GiB, total: 1.81 GiB) to /u/jm8wx/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/p/qdata/jm8wx/datasets/nlp/src/nlp/load.py", line 520, in load_dataset
builder_instance.download_and_prepare(
File "/p/qdata/jm8wx/datasets/nlp/src/nlp/builder.py", line 431, in download_and_prepare
self._download_and_prepare(
File "/p/qdata/jm8wx/datasets/nlp/src/nlp/builder.py", line 488, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/p/qdata/jm8wx/datasets/nlp/src/nlp/utils/info_utils.py", line 70, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
nlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='test', num_bytes=49424491, num_examples=11490, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='test', num_bytes=48931393, num_examples=11379, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='train', num_bytes=1249178681, num_examples=287113, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='train', num_bytes=1240618482, num_examples=285161, dataset_name='cnn_dailymail')}, {'expected': SplitInfo(name='validation', num_bytes=57149241, num_examples=13368, dataset_name='cnn_dailymail'), 'recorded': SplitInfo(name='validation', num_bytes=56637485, num_examples=13255, dataset_name='cnn_dailymail')}]
``` | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/331/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/331/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/330 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/330/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/330/comments | https://api.github.com/repos/huggingface/datasets/issues/330/events | https://github.com/huggingface/datasets/pull/330 | 648,525,720 | MDExOlB1bGxSZXF1ZXN0NDQyMzIxMjEw | 330 | Doc red | {'avatar_url': 'https://avatars.githubusercontent.com/u/13795113?v=4', 'events_url': 'https://api.github.com/users/ghomasHudson/events{/privacy}', 'followers_url': 'https://api.github.com/users/ghomasHudson/followers', 'following_url': 'https://api.github.com/users/ghomasHudson/following{/other_user}', 'gists_url': 'https://api.github.com/users/ghomasHudson/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/ghomasHudson', 'id': 13795113, 'login': 'ghomasHudson', 'node_id': 'MDQ6VXNlcjEzNzk1MTEz', 'organizations_url': 'https://api.github.com/users/ghomasHudson/orgs', 'received_events_url': 'https://api.github.com/users/ghomasHudson/received_events', 'repos_url': 'https://api.github.com/users/ghomasHudson/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/ghomasHudson/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/ghomasHudson/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/ghomasHudson'} | [] | closed | false | null | [] | null | [] | 2020-06-30 22:05:31+00:00 | 2020-07-06 12:10:39+00:00 | 2020-07-05 12:27:29+00:00 | CONTRIBUTOR | null | Adding [DocRED](https://github.com/thunlp/DocRED) - a relation extraction dataset which tests document-level RE. A few implementation notes:
- There are 2 separate versions of the training set - *annotated* and *distant*. Instead of `nlp.Split.Train` I've used the splits `"train_annotated"` and `"train_distant"` to reflect this.
- As well as the relation id, the full relation name is mapped from `rel_info.json`
- I renamed the 'h', 'r', 't' keys to 'head', 'relation' and 'tail' to make them more readable.
- Used the fix from #319 to allow nested sequences of dicts. | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/330/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/330/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/330.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/330', 'merged_at': '2020-07-05T12:27:29Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/330.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/330'} | true |
https://api.github.com/repos/huggingface/datasets/issues/329 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/329/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/329/comments | https://api.github.com/repos/huggingface/datasets/issues/329/events | https://github.com/huggingface/datasets/issues/329 | 648,446,979 | MDU6SXNzdWU2NDg0NDY5Nzk= | 329 | [Bug] FileLock dependency incompatible with filesystem | {'avatar_url': 'https://avatars.githubusercontent.com/u/4564897?v=4', 'events_url': 'https://api.github.com/users/jarednielsen/events{/privacy}', 'followers_url': 'https://api.github.com/users/jarednielsen/followers', 'following_url': 'https://api.github.com/users/jarednielsen/following{/other_user}', 'gists_url': 'https://api.github.com/users/jarednielsen/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/jarednielsen', 'id': 4564897, 'login': 'jarednielsen', 'node_id': 'MDQ6VXNlcjQ1NjQ4OTc=', 'organizations_url': 'https://api.github.com/users/jarednielsen/orgs', 'received_events_url': 'https://api.github.com/users/jarednielsen/received_events', 'repos_url': 'https://api.github.com/users/jarednielsen/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/jarednielsen/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/jarednielsen/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/jarednielsen'} | [] | closed | false | null | [] | null | ['Hi, can you give details on your environment/os/packages versions/etc?'
'Environment is Ubuntu 18.04, Python 3.7.5, nlp==0.3.0, filelock=3.0.12.\r\n\r\nThe external volume is Amazon FSx for Lustre, and it by default creates files with limited permissions. My working theory is that FileLock creates a lockfile that isn\'t writable, and thus there\'s no way to acquire it by removing the .lock file. But Python is able to create new files and write to them outside of the FileLock package.\r\n\r\nWhen I attempt to use FileLock within a Docker container by writing to `/root/.cache/hello.txt`, it succeeds. So there\'s some permissions issue. But it\'s not a Docker configuration issue; I\'ve replicated it without Docker.\r\n```bash\r\necho "hello world" >> hello.txt\r\nls -l\r\n\r\n-rw-rw-r-- 1 ubuntu ubuntu 10 Jun 30 19:52 hello.txt\r\n```'
'Looks like the `flock` syscall does not work on Lustre filesystems by default: https://github.com/benediktschmitt/py-filelock/issues/67.\r\n\r\nI added the `-o flock` option when mounting the filesystem, as [described here](https://docs.aws.amazon.com/fsx/latest/LustreGuide/getting-started-step2.html), which fixed the issue.'
'Awesome, thanks a lot for sharing your fix!'
"I'm wondering if this can be revisited. In some managed environments the same person using HF cannot change the file-system mount flags, (and the organization may be unwilling to change these flags due to other concerns) but can ensure that there won't be concurrent writes, for example because HF is offline and the models/datasets were downloaded earlier. \r\n\r\nThe real fix would be to FileLock itself, which does not seem very active and seems to not deal with failed system flock calls , which would be one way to fix this, as they mention in the issue below also raised by @jarednielsen \r\n\r\nhttps://github.com/tox-dev/py-filelock/issues/67"
"> I'm wondering if this can be revisited. In some managed environments the same person using HF cannot change the file-system mount flags, (and the organization may be unwilling to change these flags due to other concerns) but can ensure that there won't be concurrent writes, for example because HF is offline and the models/datasets were downloaded earlier.\r\n\r\nI am one of those users. Is there a work around for this?\r\n"
'The machines I use have a shared FS which has the filelock problem as well as a local one that does not. Using some env vars (HF_HOME, which controls both models and datasets, and HF_DATASETS_OFFLINE) for both transformers and datasets library one can influence where these downloads happen, and whether the locks get taken. I think some of the relevant documentation is here https://huggingface.co/docs/transformers/installation#cache-setup. I do end up using different settings when I download the models and when I use them, and have to rsync the models to the local file system using a separate script. '
"Thanks @orm011 . These filesystems are such a pain. I'll dig around, looks like setting `cache_dir` to a non-lustre filesystem works for `transformers` but not `datasets`."
'Note I `export HF_HOME=` in the shell prior to running python (I do not use the `cache_dir` argument, I think I ran into similar issues with it, nor `HF_DATASETS_CACHE` , though maybe that works, or maybe you can set it in python prior to importing the library ), and I change no other variables. Then `datasets.load_dataset()` works without any additional flags, and they go into `HF_HOME/datasets/` and the models go into `HF_HOME/transformers/` (and the lock files are all there as well). '
"I am using a shared cluster with a lustre system that I can't change. I am unable to download or load datsets onto the filesystem because of file lock. @thomwolf can this issue be reopened? "] | 2020-06-30 19:45:31+00:00 | 2023-10-07 17:07:53+00:00 | 2020-06-30 21:33:06+00:00 | CONTRIBUTOR | null | I'm downloading a dataset successfully with
`load_dataset("wikitext", "wikitext-2-raw-v1")`
But when I attempt to cache it on an external volume, it hangs indefinitely:
`load_dataset("wikitext", "wikitext-2-raw-v1", cache_dir="/fsx") # /fsx is an external volume mount`
The filesystem when hanging looks like this:
```bash
/fsx
----downloads
----94be...73.lock
----wikitext
----wikitext-2-raw
----wikitext-2-raw-1.0.0.incomplete
```
It appears that on this filesystem, the FileLock object is forever stuck in its "acquire" stage. I have verified that the issue lies specifically with the `filelock` dependency:
```python
open("/fsx/hello.txt").write("hello") # succeeds
from filelock import FileLock
with FileLock("/fsx/hello.lock"):
open("/fsx/hello.txt").write("hello") # hangs indefinitely
```
Has anyone else run into this issue? I'd raise it directly on the FileLock repo, but that project appears abandoned with the last update over a year ago. Or if there's a solution that would remove the FileLock dependency from the project, I would appreciate that. | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/329/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/329/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/328 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/328/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/328/comments | https://api.github.com/repos/huggingface/datasets/issues/328/events | https://github.com/huggingface/datasets/issues/328 | 648,326,841 | MDU6SXNzdWU2NDgzMjY4NDE= | 328 | Fork dataset | {'avatar_url': 'https://avatars.githubusercontent.com/u/2000204?v=4', 'events_url': 'https://api.github.com/users/timothyjlaurent/events{/privacy}', 'followers_url': 'https://api.github.com/users/timothyjlaurent/followers', 'following_url': 'https://api.github.com/users/timothyjlaurent/following{/other_user}', 'gists_url': 'https://api.github.com/users/timothyjlaurent/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/timothyjlaurent', 'id': 2000204, 'login': 'timothyjlaurent', 'node_id': 'MDQ6VXNlcjIwMDAyMDQ=', 'organizations_url': 'https://api.github.com/users/timothyjlaurent/orgs', 'received_events_url': 'https://api.github.com/users/timothyjlaurent/received_events', 'repos_url': 'https://api.github.com/users/timothyjlaurent/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/timothyjlaurent/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/timothyjlaurent/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/timothyjlaurent'} | [] | closed | false | null | [] | null | ['To be able to generate the Arrow dataset you need to either use our csv or json utilities `load_dataset("json", data_files=my_json_files)` OR write your own custom dataset script (you can find some inspiration from the [squad](https://github.com/huggingface/nlp/blob/master/datasets/squad/squad.py) script for example). Custom dataset scripts can be called locally with `nlp.load_dataset(path_to_my_script_directory)`.\r\n\r\nThis should help you get what you call "Dataset1".\r\n\r\nThen using some dataset transforms like `.map` for example you can get to "DatasetNER" and "DatasetREL".\r\n'
"Thanks for the helpful advice, @lhoestq -- I wasn't quite able to get the json recipe working - \r\n\r\n```\r\n~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/pyarrow/ipc.py in __init__(self, source)\r\n 60 \r\n 61 def __init__(self, source):\r\n---> 62 self._open(source)\r\n 63 \r\n 64 \r\n~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/pyarrow/ipc.pxi in pyarrow.lib._RecordBatchStreamReader._open()\r\n~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()\r\nArrowInvalid: Tried reading schema message, was null or length 0\r\n```\r\n\r\nBut I'm going to give the generator_dataset_builder a try.\r\n\r\n1 more quick question -- can .map be used to output different length mappings -- could I skip one, or yield 2, can you map_batch "
'You can use `.map(my_func, batched=True)` and return less examples, or more examples if you want'
'Thanks this answers my question. I think the issue I was having using the json loader were due to using gzipped jsonl files.\r\n\r\nThe error I get now is :\r\n\r\n```\r\n\r\nUsing custom data configuration test\r\n---------------------------------------------------------------------------\r\n\r\nValueError Traceback (most recent call last)\r\n\r\n<ipython-input-38-29082a31e5b2> in <module>\r\n 5 print(ner_datafiles)\r\n 6 \r\n----> 7 ds = nlp.load_dataset("json", "test", data_files=ner_datafiles[0])\r\n 8 \r\n\r\n~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)\r\n 522 download_mode=download_mode,\r\n 523 ignore_verifications=ignore_verifications,\r\n--> 524 save_infos=save_infos,\r\n 525 )\r\n 526 \r\n\r\n~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)\r\n 430 verify_infos = not save_infos and not ignore_verifications\r\n 431 self._download_and_prepare(\r\n--> 432 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs\r\n 433 )\r\n 434 # Sync info\r\n\r\n~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)\r\n 481 try:\r\n 482 # Prepare split will record examples associated to the split\r\n--> 483 self._prepare_split(split_generator, **prepare_split_kwargs)\r\n 484 except OSError:\r\n 485 raise OSError("Cannot find data file. " + (self.manual_download_instructions or ""))\r\n\r\n~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/builder.py in _prepare_split(self, split_generator)\r\n 736 schema_dict[field.name] = Value(str(field.type))\r\n 737 \r\n--> 738 parse_schema(writer.schema, features)\r\n 739 self.info.features = Features(features)\r\n 740 \r\n\r\n~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/builder.py in parse_schema(schema, schema_dict)\r\n 734 parse_schema(field.type.value_type, schema_dict[field.name])\r\n 735 else:\r\n--> 736 schema_dict[field.name] = Value(str(field.type))\r\n 737 \r\n 738 parse_schema(writer.schema, features)\r\n\r\n<string> in __init__(self, dtype, id, _type)\r\n\r\n~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/features.py in __post_init__(self)\r\n 55 \r\n 56 def __post_init__(self):\r\n---> 57 self.pa_type = string_to_arrow(self.dtype)\r\n 58 \r\n 59 def __call__(self):\r\n\r\n~/.virtualenvs/inv-text2struct/lib/python3.6/site-packages/nlp/features.py in string_to_arrow(type_str)\r\n 32 if str(type_str + "_") not in pa.__dict__:\r\n 33 raise ValueError(\r\n---> 34 f"Neither {type_str} nor {type_str + \'_\'} seems to be a pyarrow data type. "\r\n 35 f"Please make sure to use a correct data type, see: "\r\n 36 f"https://arrow.apache.org/docs/python/api/datatypes.html#factory-functions"\r\n\r\nValueError: Neither list<item: int64> nor list<item: int64>_ seems to be a pyarrow data type. Please make sure to use a correct data type, see: https://arrow.apache.org/docs/python/api/datatypes.html#factory-functions.\r\n```\r\n\r\nIf I just create a pa- table manually like is done in the jsonloader -- it seems to work fine. Ths JSON I\'m trying to load isn\'t overly complex - 1 integer field, the rest text fields with a nested list of objects with text fields .'
"I'll close this -- It's still unclear how to go about troubleshooting the json example as I mentioned above. If I decide it's worth the trouble, I'll create another issue, or wait for a better support for using nlp for making custom data-loaders."] | 2020-06-30 16:42:53+00:00 | 2020-07-06 21:43:59+00:00 | 2020-07-06 21:43:59+00:00 | NONE | null | We have a multi-task learning model training I'm trying to convert to using the Arrow-based nlp dataset.
We're currently training a custom TensorFlow model but the nlp paradigm should be a bridge for us to be able to use the wealth of pre-trained models in Transformers.
Our preprocessing flow parses raw text and json with Entity and Relations annotations and creates 2 datasets for training a NER and Relations prediction heads.
Is there some good way to "fork" dataset-
EG
1. text + json -> Dataset1
1. Dataset1 -> DatasetNER
1. Dataset1 -> DatasetREL
or
1. text + json -> Dataset1
1. Dataset1 -> DatasetNER
1. Dataset1 + DatasetNER -> DatasetREL
| {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/328/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/328/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/327 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/327/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/327/comments | https://api.github.com/repos/huggingface/datasets/issues/327/events | https://github.com/huggingface/datasets/pull/327 | 648,312,858 | MDExOlB1bGxSZXF1ZXN0NDQyMTQyOTQw | 327 | set seed for suffling tests | {'avatar_url': 'https://avatars.githubusercontent.com/u/42851186?v=4', 'events_url': 'https://api.github.com/users/lhoestq/events{/privacy}', 'followers_url': 'https://api.github.com/users/lhoestq/followers', 'following_url': 'https://api.github.com/users/lhoestq/following{/other_user}', 'gists_url': 'https://api.github.com/users/lhoestq/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/lhoestq', 'id': 42851186, 'login': 'lhoestq', 'node_id': 'MDQ6VXNlcjQyODUxMTg2', 'organizations_url': 'https://api.github.com/users/lhoestq/orgs', 'received_events_url': 'https://api.github.com/users/lhoestq/received_events', 'repos_url': 'https://api.github.com/users/lhoestq/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/lhoestq/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/lhoestq/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/lhoestq'} | [] | closed | false | null | [] | null | [] | 2020-06-30 16:21:34+00:00 | 2020-07-02 08:34:05+00:00 | 2020-07-02 08:34:04+00:00 | MEMBER | null | Some tests were randomly failing because of a missing seed in a test for `train_test_split(shuffle=True)` | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/327/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/327/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/327.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/327', 'merged_at': '2020-07-02T08:34:04Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/327.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/327'} | true |
https://api.github.com/repos/huggingface/datasets/issues/326 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/326/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/326/comments | https://api.github.com/repos/huggingface/datasets/issues/326/events | https://github.com/huggingface/datasets/issues/326 | 648,126,103 | MDU6SXNzdWU2NDgxMjYxMDM= | 326 | Large dataset in Squad2-format | {'avatar_url': 'https://avatars.githubusercontent.com/u/47894090?v=4', 'events_url': 'https://api.github.com/users/flozi00/events{/privacy}', 'followers_url': 'https://api.github.com/users/flozi00/followers', 'following_url': 'https://api.github.com/users/flozi00/following{/other_user}', 'gists_url': 'https://api.github.com/users/flozi00/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/flozi00', 'id': 47894090, 'login': 'flozi00', 'node_id': 'MDQ6VXNlcjQ3ODk0MDkw', 'organizations_url': 'https://api.github.com/users/flozi00/orgs', 'received_events_url': 'https://api.github.com/users/flozi00/received_events', 'repos_url': 'https://api.github.com/users/flozi00/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/flozi00/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/flozi00/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/flozi00'} | [] | closed | false | null | [] | null | ["I'm pretty sure you can get some inspiration from the squad_v2 script. It looks like the dataset is quite big so it will take some time for the users to generate it, but it should be reasonable.\r\n\r\nAlso you are saying that you are still making the dataset grow in size right ?\r\nIt's probably good practice to let the users do their training/evaluations with the exact same version of the dataset.\r\nWe allow for each dataset to specify a version (ex: 1.0.0) and increment this number every time there are new samples in the dataset for example. Does it look like a good solution for you ? Or would you rather have one final version with the full dataset ?"
'It would also be good if there is any possibility for versioning, I think this way is much better than the dynamic way.\nIf you mean that part to put the tiles into one is the generation it would take up to 15-20 minutes on home computer hardware.\nAre there any compression or optimization algorithms while generating the dataset ?\nOtherwise the hardware limit is around 32 GB ram at the moment.\nIf everything works well we will add some more gigabytes of data in future what would make it pretty memory costly.'
"15-20 minutes is fine !\r\nAlso there's no RAM limitations as we save to disk every 1000 elements while generating the dataset by default.\r\nAfter generation, the dataset is ready to use with (again) no RAM limitations as we do memory-mapping."
"Wow, that sounds pretty cool.\nActually I have the problem of running out of memory while tokenization on our local machine.\nThat wouldn't happen again, would it ?"
'You can do the tokenization step using `my_tokenized_dataset = my_dataset.map(my_tokenize_function)` that writes the tokenized texts on disk as well. And then `my_tokenized_dataset` will be a memory-mapped dataset too, so you should be fine :)'
'Does it have an affect to the trainings speed ?'
"In your training loop, loading the tokenized texts is going to be fast and pretty much negligible compared to a forward pass. You shouldn't expect any slow down."
'Closing this one. Feel free to re-open if you have other questions'] | 2020-06-30 12:18:59+00:00 | 2020-07-09 09:01:50+00:00 | 2020-07-09 09:01:50+00:00 | CONTRIBUTOR | null | At the moment we are building an large question answering dataset and think about sharing it with the huggingface community.
Caused the computing power we splitted it into multiple tiles, but they are all in the same format.
Right now the most important facts about are this:
- Contexts: 1.047.671
- questions: 1.677.732
- Answers: 6.742.406
- unanswerable: 377.398
It is already cleaned
<pre><code>
train_data = [
{
'context': "this is the context",
'qas': [
{
'id': "00002",
'is_impossible': False,
'question': "whats is this",
'answers': [
{
'text': "answer",
'answer_start': 0
}
]
},
{
'id': "00003",
'is_impossible': False,
'question': "question2",
'answers': [
{
'text': "answer2",
'answer_start': 1
}
]
}
]
}
]
</code></pre>
Cause it is growing every day we are thinking about an structure like this:
We host an Json file, containing all the download links and the script can load it dynamically.
At the moment it is around ~20GB
Any advice how to handle this, or an ready to use template ? | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/326/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/326/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/325 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/325/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/325/comments | https://api.github.com/repos/huggingface/datasets/issues/325/events | https://github.com/huggingface/datasets/pull/325 | 647,601,592 | MDExOlB1bGxSZXF1ZXN0NDQxNTk3NTgw | 325 | Add SQuADShifts dataset | {'avatar_url': 'https://avatars.githubusercontent.com/u/8953195?v=4', 'events_url': 'https://api.github.com/users/millerjohnp/events{/privacy}', 'followers_url': 'https://api.github.com/users/millerjohnp/followers', 'following_url': 'https://api.github.com/users/millerjohnp/following{/other_user}', 'gists_url': 'https://api.github.com/users/millerjohnp/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/millerjohnp', 'id': 8953195, 'login': 'millerjohnp', 'node_id': 'MDQ6VXNlcjg5NTMxOTU=', 'organizations_url': 'https://api.github.com/users/millerjohnp/orgs', 'received_events_url': 'https://api.github.com/users/millerjohnp/received_events', 'repos_url': 'https://api.github.com/users/millerjohnp/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/millerjohnp/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/millerjohnp/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/millerjohnp'} | [] | closed | false | null | [] | null | ['Very cool to have this dataset, thank you for adding it :)'] | 2020-06-29 19:11:16+00:00 | 2020-06-30 17:07:31+00:00 | 2020-06-30 17:07:31+00:00 | CONTRIBUTOR | null | This PR adds the four new variants of the SQuAD dataset used in [The Effect of Natural Distribution Shift on Question Answering Models](https://arxiv.org/abs/2004.14444) to facilitate evaluating model robustness to distribution shift. | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/325/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/325/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/325.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/325', 'merged_at': '2020-06-30T17:07:31Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/325.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/325'} | true |
https://api.github.com/repos/huggingface/datasets/issues/324 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/324/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/324/comments | https://api.github.com/repos/huggingface/datasets/issues/324/events | https://github.com/huggingface/datasets/issues/324 | 647,525,725 | MDU6SXNzdWU2NDc1MjU3MjU= | 324 | Error when calculating glue score | {'avatar_url': 'https://avatars.githubusercontent.com/u/47185867?v=4', 'events_url': 'https://api.github.com/users/D-i-l-r-u-k-s-h-i/events{/privacy}', 'followers_url': 'https://api.github.com/users/D-i-l-r-u-k-s-h-i/followers', 'following_url': 'https://api.github.com/users/D-i-l-r-u-k-s-h-i/following{/other_user}', 'gists_url': 'https://api.github.com/users/D-i-l-r-u-k-s-h-i/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/D-i-l-r-u-k-s-h-i', 'id': 47185867, 'login': 'D-i-l-r-u-k-s-h-i', 'node_id': 'MDQ6VXNlcjQ3MTg1ODY3', 'organizations_url': 'https://api.github.com/users/D-i-l-r-u-k-s-h-i/orgs', 'received_events_url': 'https://api.github.com/users/D-i-l-r-u-k-s-h-i/received_events', 'repos_url': 'https://api.github.com/users/D-i-l-r-u-k-s-h-i/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/D-i-l-r-u-k-s-h-i/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/D-i-l-r-u-k-s-h-i/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/D-i-l-r-u-k-s-h-i'} | [] | closed | false | null | [] | null | ['The glue metric for cola is a metric for classification. It expects label ids as integers as inputs.'
'I want to evaluate a sentence pair whether they are semantically equivalent, so I used MRPC and it gives the same error, does that mean we have to encode the sentences and parse as input?\r\n\r\nusing BertTokenizer;\r\n```\r\nencoded_reference=tokenizer.encode(reference, add_special_tokens=False)\r\nencoded_prediction=tokenizer.encode(prediction, add_special_tokens=False)\r\n```\r\n\r\n`glue_score = glue_metric.compute(encoded_prediction, encoded_reference)`\r\n```\r\n\r\nValueError Traceback (most recent call last)\r\n<ipython-input-9-4c3a3ce7b583> in <module>()\r\n----> 1 glue_score = glue_metric.compute(encoded_prediction, encoded_reference)\r\n\r\n6 frames\r\n/usr/local/lib/python3.6/dist-packages/nlp/metric.py in compute(self, predictions, references, timeout, **metrics_kwargs)\r\n 198 predictions = self.data["predictions"]\r\n 199 references = self.data["references"]\r\n--> 200 output = self._compute(predictions=predictions, references=references, **metrics_kwargs)\r\n 201 return output\r\n 202 \r\n\r\n/usr/local/lib/python3.6/dist-packages/nlp/metrics/glue/27b1bc63e520833054bd0d7a8d0bc7f6aab84cc9eed1b576e98c806f9466d302/glue.py in _compute(self, predictions, references)\r\n 101 return pearson_and_spearman(predictions, references)\r\n 102 elif self.config_name in ["mrpc", "qqp"]:\r\n--> 103 return acc_and_f1(predictions, references)\r\n 104 elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:\r\n 105 return {"accuracy": simple_accuracy(predictions, references)}\r\n\r\n/usr/local/lib/python3.6/dist-packages/nlp/metrics/glue/27b1bc63e520833054bd0d7a8d0bc7f6aab84cc9eed1b576e98c806f9466d302/glue.py in acc_and_f1(preds, labels)\r\n 60 def acc_and_f1(preds, labels):\r\n 61 acc = simple_accuracy(preds, labels)\r\n---> 62 f1 = f1_score(y_true=labels, y_pred=preds)\r\n 63 return {\r\n 64 "accuracy": acc,\r\n\r\n/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in f1_score(y_true, y_pred, labels, pos_label, average, sample_weight, zero_division)\r\n 1097 pos_label=pos_label, average=average,\r\n 1098 sample_weight=sample_weight,\r\n-> 1099 zero_division=zero_division)\r\n 1100 \r\n 1101 \r\n\r\n/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in fbeta_score(y_true, y_pred, beta, labels, pos_label, average, sample_weight, zero_division)\r\n 1224 warn_for=(\'f-score\',),\r\n 1225 sample_weight=sample_weight,\r\n-> 1226 zero_division=zero_division)\r\n 1227 return f\r\n 1228 \r\n\r\n/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in precision_recall_fscore_support(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight, zero_division)\r\n 1482 raise ValueError("beta should be >=0 in the F-beta score")\r\n 1483 labels = _check_set_wise_labels(y_true, y_pred, average, labels,\r\n-> 1484 pos_label)\r\n 1485 \r\n 1486 # Calculate tp_sum, pred_sum, true_sum ###\r\n\r\n/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py in _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)\r\n 1314 raise ValueError("Target is %s but average=\'binary\'. Please "\r\n 1315 "choose another average setting, one of %r."\r\n-> 1316 % (y_type, average_options))\r\n 1317 elif pos_label not in (None, 1):\r\n 1318 warnings.warn("Note that pos_label (set to %r) is ignored when "\r\n\r\nValueError: Target is multiclass but average=\'binary\'. Please choose another average setting, one of [None, \'micro\', \'macro\', \'weighted\'].\r\n\r\n```'
'MRPC is also a binary classification task, so its metric is a binary classification metric.\r\n\r\nTo evaluate if pairs of sentences are semantically equivalent, maybe you could take a look at models that compute if one sentence entails the other or not (typically the kinds of model that could work well on the MRPC task).'
'Closing this one. Feel free to re-open if you have other questions :)'] | 2020-06-29 16:53:48+00:00 | 2020-07-09 09:13:34+00:00 | 2020-07-09 09:13:34+00:00 | NONE | null | I was trying glue score along with other metrics here. But glue gives me this error;
```
import nlp
glue_metric = nlp.load_metric('glue',name="cola")
glue_score = glue_metric.compute(predictions, references)
```
```
---------------------------------------------------------------------------
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-8-b9210a524504> in <module>()
----> 1 glue_score = glue_metric.compute(predictions, references)
6 frames
/usr/local/lib/python3.6/dist-packages/nlp/metric.py in compute(self, predictions, references, timeout, **metrics_kwargs)
191 """
192 if predictions is not None:
--> 193 self.add_batch(predictions=predictions, references=references)
194 self.finalize(timeout=timeout)
195
/usr/local/lib/python3.6/dist-packages/nlp/metric.py in add_batch(self, predictions, references, **kwargs)
207 if self.writer is None:
208 self._init_writer()
--> 209 self.writer.write_batch(batch)
210
211 def add(self, prediction=None, reference=None, **kwargs):
/usr/local/lib/python3.6/dist-packages/nlp/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)
155 if self.pa_writer is None:
156 self._build_writer(pa_table=pa.Table.from_pydict(batch_examples))
--> 157 pa_table: pa.Table = pa.Table.from_pydict(batch_examples, schema=self._schema)
158 if writer_batch_size is None:
159 writer_batch_size = self.writer_batch_size
/usr/local/lib/python3.6/dist-packages/pyarrow/types.pxi in __iter__()
/usr/local/lib/python3.6/dist-packages/pyarrow/array.pxi in pyarrow.lib.asarray()
/usr/local/lib/python3.6/dist-packages/pyarrow/array.pxi in pyarrow.lib.array()
/usr/local/lib/python3.6/dist-packages/pyarrow/array.pxi in pyarrow.lib._sequence_to_array()
TypeError: an integer is required (got type str)
```
I'm not sure whether I'm doing this wrong or whether it's an issue. I would like to know a workaround. Thank you. | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/324/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/324/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/323 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/323/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/323/comments | https://api.github.com/repos/huggingface/datasets/issues/323/events | https://github.com/huggingface/datasets/pull/323 | 647,521,308 | MDExOlB1bGxSZXF1ZXN0NDQxNTMxOTY3 | 323 | Add package path to sys when downloading package as github archive | {'avatar_url': 'https://avatars.githubusercontent.com/u/10469459?v=4', 'events_url': 'https://api.github.com/users/yjernite/events{/privacy}', 'followers_url': 'https://api.github.com/users/yjernite/followers', 'following_url': 'https://api.github.com/users/yjernite/following{/other_user}', 'gists_url': 'https://api.github.com/users/yjernite/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/yjernite', 'id': 10469459, 'login': 'yjernite', 'node_id': 'MDQ6VXNlcjEwNDY5NDU5', 'organizations_url': 'https://api.github.com/users/yjernite/orgs', 'received_events_url': 'https://api.github.com/users/yjernite/received_events', 'repos_url': 'https://api.github.com/users/yjernite/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/yjernite/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/yjernite/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/yjernite'} | [] | closed | false | null | [] | null | ['Sorry for the long diff, everything after the imports comes from `black` for code quality :/ '
" I think it's fine and I can't think of another way to make the import work anyways.\r\n\r\nMaybe we can have the `sys.path` behavior inside `prepare_module` instead ? Currently it seems to come out of nowhere in the code ^^'\r\nWe could check if external imports have a `__init__.py` and if it is the case then we can add to directory to the `PYTHONPATH`"] | 2020-06-29 16:46:01+00:00 | 2020-07-30 14:00:23+00:00 | 2020-07-30 14:00:23+00:00 | MEMBER | null | This fixes the `coval.py` metric so that imports within the downloaded module work correctly. We can use a similar trick to add the BLEURT metric (@ankparikh)
@thomwolf not sure how you feel about adding to the `PYTHONPATH` from the script. This is the only way I could make it work with my understanding of `importlib` but there might be a more elegant method.
This PR fixes https://github.com/huggingface/nlp/issues/305 | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/323/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/323/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/323.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/323', 'merged_at': None, 'patch_url': 'https://github.com/huggingface/datasets/pull/323.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/323'} | true |
https://api.github.com/repos/huggingface/datasets/issues/322 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/322/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/322/comments | https://api.github.com/repos/huggingface/datasets/issues/322/events | https://github.com/huggingface/datasets/pull/322 | 647,483,850 | MDExOlB1bGxSZXF1ZXN0NDQxNTAyMjc2 | 322 | output nested dict in get_nearest_examples | {'avatar_url': 'https://avatars.githubusercontent.com/u/42851186?v=4', 'events_url': 'https://api.github.com/users/lhoestq/events{/privacy}', 'followers_url': 'https://api.github.com/users/lhoestq/followers', 'following_url': 'https://api.github.com/users/lhoestq/following{/other_user}', 'gists_url': 'https://api.github.com/users/lhoestq/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/lhoestq', 'id': 42851186, 'login': 'lhoestq', 'node_id': 'MDQ6VXNlcjQyODUxMTg2', 'organizations_url': 'https://api.github.com/users/lhoestq/orgs', 'received_events_url': 'https://api.github.com/users/lhoestq/received_events', 'repos_url': 'https://api.github.com/users/lhoestq/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/lhoestq/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/lhoestq/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/lhoestq'} | [] | closed | false | null | [] | null | [] | 2020-06-29 15:47:47+00:00 | 2020-07-02 08:33:33+00:00 | 2020-07-02 08:33:32+00:00 | MEMBER | null | As we are using a columnar format like arrow as the backend for datasets, we expect to have a dictionary of columns when we slice a dataset like in this example:
```python
my_examples = dataset[0:10]
print(type(my_examples))
# >>> dict
print(my_examples["my_column"][0]
# >>> this is the first element of the column 'my_column'
```
Therefore I wanted to keep this logic when calling `get_nearest_examples` that returns the top 10 nearest examples:
```python
dataset.add_faiss_index(column="embeddings")
scores, examples = dataset.get_nearest_examples("embeddings", query=my_numpy_embedding)
print(type(examples))
# >>> dict
```
Previously it was returning a list[dict]. It was the only place that was using this output format.
To make it work I had to implement `__getitem__(key)` where `key` is a list.
This is different from `.select` because `.select` is a dataset transform (it returns a new dataset object) while `__getitem__` is an extraction method (it returns python dictionaries). | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/322/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/322/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/322.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/322', 'merged_at': '2020-07-02T08:33:32Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/322.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/322'} | true |
https://api.github.com/repos/huggingface/datasets/issues/321 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/321/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/321/comments | https://api.github.com/repos/huggingface/datasets/issues/321/events | https://github.com/huggingface/datasets/issues/321 | 647,271,526 | MDU6SXNzdWU2NDcyNzE1MjY= | 321 | ERROR:root:mwparserfromhell | {'avatar_url': 'https://avatars.githubusercontent.com/u/26505641?v=4', 'events_url': 'https://api.github.com/users/Shiro-LK/events{/privacy}', 'followers_url': 'https://api.github.com/users/Shiro-LK/followers', 'following_url': 'https://api.github.com/users/Shiro-LK/following{/other_user}', 'gists_url': 'https://api.github.com/users/Shiro-LK/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/Shiro-LK', 'id': 26505641, 'login': 'Shiro-LK', 'node_id': 'MDQ6VXNlcjI2NTA1NjQx', 'organizations_url': 'https://api.github.com/users/Shiro-LK/orgs', 'received_events_url': 'https://api.github.com/users/Shiro-LK/received_events', 'repos_url': 'https://api.github.com/users/Shiro-LK/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/Shiro-LK/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/Shiro-LK/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/Shiro-LK'} | [{'color': '2edb81', 'default': False, 'description': 'A bug in a dataset script provided in the library', 'id': 2067388877, 'name': 'dataset bug', 'node_id': 'MDU6TGFiZWwyMDY3Mzg4ODc3', 'url': 'https://api.github.com/repos/huggingface/datasets/labels/dataset%20bug'}] | closed | false | null | [] | null | ['It looks like it comes from `mwparserfromhell`.\r\n\r\nWould it be possible to get the bad `section` that causes this issue ? The `section` string is from `datasets/wikipedia.py:L548` ? You could just add a `try` statement and print the section if the line `section_text.append(section.strip_code().strip())` crashes.\r\n\r\nIt will help us know if we have to fix it on our side or if it is a `mwparserfromhell` issue.'
"Hi, \r\n\r\nThank you for you answer.\r\nI have try to print the bad section using `try` and `except`, but it is a bit weird as the error seems to appear 3 times for instance, but the two first error does not print anything (as if the function did not go in the `except` part).\r\nFor the third one, I got that (I haven't display the entire text) :\r\n\r\n> error : ==== Parque nacional Cajas ====\r\n> {{AP|Parque nacional Cajas}}\r\n> [[Archivo:Ecuador cajas national park.jpg|thumb|left|300px|Laguna del Cajas]]\r\n> El parque nacional Cajas está situado en los [[Cordillera de los Andes|Andes]], al sur del [[Ecuador]], en la provincia de [[Provincia de Azuay|Azuay]], a 33\r\n> [[km]] al noroccidente de la ciudad de [[Cuenca (Ecuador)|Cuenca]]. Los accesos más comunes al parque inician todos en Cuenca: Desde allí, la vía Cuenca-Mol\r\n> leturo atraviesa en Control de [[Surocucho]] en poco más de 30 minutos de viaje; más adelante, esta misma carretera pasa a orillas de la laguna La Toreadora donde están el Centro Administrativo y de Información del parque. Siguiendo de largo hacia [[Molleturo]], por esta vía se conoce el sector norte del Cajas y se serpentea entre varias lagunas mayores y menores.\r\n> Para acceder al parque desde la costa, la vía Molleturo-Cuenca es también la mejor opción.\r\n\r\nHow can I display the link instead of the text ? I suppose it will help you more "
'The error appears several times as Apache Beam retries to process examples up to 4 times irc.\r\n\r\nI just tried to run this text into `mwparserfromhell` but it worked without the issue.\r\n\r\nI used this code (from the `wikipedia.py` script):\r\n```python\r\nimport mwparserfromhell as parser\r\nimport re\r\nimport six\r\n\r\nraw_content = r"""==== Parque nacional Cajas ====\r\n{{AP|Parque nacional Cajas}}\r\n[[Archivo:Ecuador cajas national park.jpg|thumb|left|300px|Laguna del Cajas]]\r\nEl parque nacional Cajas está situado en los [[Cordillera de los Andes|Andes]], al sur del [[Ecuador]], en la provincia de [[Provincia de Azuay|Azuay]], a 33\r\n[[km]] al noroccidente de la ciudad de [[Cuenca (Ecuador)|Cuenca]]. Los accesos más comunes al parque inician todos en Cuenca: Desde allí, la vía Cuenca-Mol\r\nleturo atraviesa en Control de [[Surocucho]] en poco más de 30 minutos de viaje; más adelante, esta misma carretera pasa a orillas de la laguna La Toreadora donde están el Centro Administrativo y de Información del parque. Siguiendo de largo hacia [[Molleturo]], por esta vía se conoce el sector norte del Cajas y se serpentea entre varias lagunas mayores y menores.\r\n"""\r\n\r\nwikicode = parser.parse(raw_content)\r\n\r\n# Filters for references, tables, and file/image links.\r\nre_rm_wikilink = re.compile("^(?:File|Image|Media):", flags=re.IGNORECASE | re.UNICODE)\r\n\r\ndef rm_wikilink(obj):\r\n return bool(re_rm_wikilink.match(six.text_type(obj.title)))\r\n\r\ndef rm_tag(obj):\r\n return six.text_type(obj.tag) in {"ref", "table"}\r\n\r\ndef rm_template(obj):\r\n return obj.name.lower() in {"reflist", "notelist", "notelist-ua", "notelist-lr", "notelist-ur", "notelist-lg"}\r\n\r\ndef try_remove_obj(obj, section):\r\n try:\r\n section.remove(obj)\r\n except ValueError:\r\n # For unknown reasons, objects are sometimes not found.\r\n pass\r\n\r\nsection_text = []\r\nfor section in wikicode.get_sections(flat=True, include_lead=True, include_headings=True):\r\n for obj in section.ifilter_wikilinks(matches=rm_wikilink, recursive=True):\r\n try_remove_obj(obj, section)\r\n for obj in section.ifilter_templates(matches=rm_template, recursive=True):\r\n try_remove_obj(obj, section)\r\n for obj in section.ifilter_tags(matches=rm_tag, recursive=True):\r\n try_remove_obj(obj, section)\r\n\r\n section_text.append(section.strip_code().strip())\r\n```'
"Not sure why we're having this issue. Maybe could you get also the file that's causing that ?"
'thanks for your answer.\r\nHow can I know which file is causing the issue ? \r\nI am trying to load the spanish wikipedia data. '
"Because of the way Apache Beam works we indeed don't have access to the file name at this point in the code.\r\nWe'll have to use some tricks I think :p \r\n\r\nYou can append `filepath` to `title` in `wikipedia.py:L512` for example. [[EDIT: it's L494 my bad]]\r\nThen just do `try:...except:` on the call of `_parse_and_clean_wikicode` L500 I guess.\r\n\r\nThanks for diving into this ! I tried it myself but I run out of memory on my laptop\r\nAs soon as we have the name of the file it should be easier to find what's wrong."
'Thanks for your help.\r\n\r\nI tried to print the "title" of the document inside the` except (mwparserfromhell.parser.ParserError) as e`,the title displayed was : "Campeonato Mundial de futsal de la AMF 2015". (Wikipedia ES) Is it what you were looking for ?'
'Thanks a lot @Shiro-LK !\r\n\r\nI was able to reproduce the issue. It comes from [this table on wikipedia](https://es.wikipedia.org/wiki/Campeonato_Mundial_de_futsal_de_la_AMF_2015#Clasificados) that can\'t be parsed.\r\n\r\nThe file in which the problem occurs comes from the wikipedia dumps, and it can be downloaded [here](https://dumps.wikimedia.org/eswiki/20200501/eswiki-20200501-pages-articles-multistream6.xml-p6424816p7924815.bz2)\r\n\r\nParsing the file this way raises the parsing issue:\r\n\r\n```python\r\nimport mwparserfromhell as parser\r\nfrom tqdm.auto import tqdm\r\nimport bz2\r\nimport six\r\nimport logging\r\nimport codecs\r\nimport xml.etree.cElementTree as etree\r\n\r\nfilepath = "path/to/eswiki-20200501-pages-articles-multistream6.xml-p6424816p7924815.bz2"\r\n\r\ndef _extract_content(filepath):\r\n """Extracts article content from a single WikiMedia XML file."""\r\n logging.info("generating examples from = %s", filepath)\r\n with open(filepath, "rb") as f:\r\n f = bz2.BZ2File(filename=f)\r\n if six.PY3:\r\n # Workaround due to:\r\n # https://github.com/tensorflow/tensorflow/issues/33563\r\n utf_f = codecs.getreader("utf-8")(f)\r\n else:\r\n utf_f = f\r\n # To clear root, to free-up more memory than just `elem.clear()`.\r\n context = etree.iterparse(utf_f, events=("end",))\r\n context = iter(context)\r\n unused_event, root = next(context)\r\n for unused_event, elem in tqdm(context, total=949087):\r\n if not elem.tag.endswith("page"):\r\n continue\r\n namespace = elem.tag[:-4]\r\n title = elem.find("./{0}title".format(namespace)).text\r\n ns = elem.find("./{0}ns".format(namespace)).text\r\n id_ = elem.find("./{0}id".format(namespace)).text\r\n # Filter pages that are not in the "main" namespace.\r\n if ns != "0":\r\n root.clear()\r\n continue\r\n raw_content = elem.find("./{0}revision/{0}text".format(namespace)).text\r\n root.clear()\r\n\r\n if "Campeonato Mundial de futsal de la AMF 2015" in title:\r\n yield (id_, title, raw_content)\r\n\r\nfor id_, title, raw_content in _extract_content(filepath):\r\n wikicode = parser.parse(raw_content)\r\n```\r\n\r\nThe copied the raw content that can\'t be parsed [here](https://pastebin.com/raw/ZbmevLyH).\r\n\r\nThe minimal code to reproduce is:\r\n```python\r\nimport mwparserfromhell as parser\r\nimport requests\r\n\r\nraw_content = requests.get("https://pastebin.com/raw/ZbmevLyH").content.decode("utf-8")\r\nwikicode = parser.parse(raw_content)\r\n\r\n```\r\n\r\nI will create an issue on mwparserfromhell\'s repo to see if we can fix that\r\n'
'This going to be fixed in the next `mwparserfromhell` release :)'
'Fixed in `mwparserfromhell` version 0.6.'] | 2020-06-29 11:10:43+00:00 | 2022-02-14 15:21:46+00:00 | 2022-02-14 15:21:46+00:00 | NONE | null | Hi,
I am trying to download some wikipedia data but I got this error for spanish "es" (but there are maybe some others languages which have the same error I haven't tried all of them ).
`ERROR:root:mwparserfromhell ParseError: This is a bug and should be reported. Info: C tokenizer exited with non-empty token stack.`
The code I have use was :
`dataset = load_dataset('wikipedia', '20200501.es', beam_runner='DirectRunner')`
| {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/321/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/321/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/320 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/320/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/320/comments | https://api.github.com/repos/huggingface/datasets/issues/320/events | https://github.com/huggingface/datasets/issues/320 | 647,188,167 | MDU6SXNzdWU2NDcxODgxNjc= | 320 | Blog Authorship Corpus, Non Matching Splits Sizes Error, nlp viewer | {'avatar_url': 'https://avatars.githubusercontent.com/u/38249783?v=4', 'events_url': 'https://api.github.com/users/mariamabarham/events{/privacy}', 'followers_url': 'https://api.github.com/users/mariamabarham/followers', 'following_url': 'https://api.github.com/users/mariamabarham/following{/other_user}', 'gists_url': 'https://api.github.com/users/mariamabarham/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/mariamabarham', 'id': 38249783, 'login': 'mariamabarham', 'node_id': 'MDQ6VXNlcjM4MjQ5Nzgz', 'organizations_url': 'https://api.github.com/users/mariamabarham/orgs', 'received_events_url': 'https://api.github.com/users/mariamabarham/received_events', 'repos_url': 'https://api.github.com/users/mariamabarham/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/mariamabarham/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/mariamabarham/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/mariamabarham'} | [{'color': '94203D', 'default': False, 'description': '', 'id': 2107841032, 'name': 'nlp-viewer', 'node_id': 'MDU6TGFiZWwyMTA3ODQxMDMy', 'url': 'https://api.github.com/repos/huggingface/datasets/labels/nlp-viewer'}] | closed | false | null | [] | null | ['I wonder if this means downloading failed? That corpus has a really slow server.'
"This dataset seems to have a decoding problem that results in inconsistencies in the number of generated examples.\r\nSee #215.\r\nThat's why we end up with a `NonMatchingSplitsSizesError `."] | 2020-06-29 07:36:35+00:00 | 2020-06-29 14:44:42+00:00 | 2020-06-29 14:44:42+00:00 | CONTRIBUTOR | null | Selecting `blog_authorship_corpus` in the nlp viewer throws the following error:
```
NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=610252351, num_examples=532812, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='train', num_bytes=614706451, num_examples=535568, dataset_name='blog_authorship_corpus')}, {'expected': SplitInfo(name='validation', num_bytes=37500394, num_examples=31277, dataset_name='blog_authorship_corpus'), 'recorded': SplitInfo(name='validation', num_bytes=32553710, num_examples=28521, dataset_name='blog_authorship_corpus')}]
Traceback:
File "/home/sasha/streamlit/lib/streamlit/ScriptRunner.py", line 322, in _run_script
exec(code, module.__dict__)
File "/home/sasha/nlp-viewer/run.py", line 172, in <module>
dts, fail = get(str(option.id), str(conf_option.name) if conf_option else None)
File "/home/sasha/streamlit/lib/streamlit/caching.py", line 591, in wrapped_func
return get_or_create_cached_value()
File "/home/sasha/streamlit/lib/streamlit/caching.py", line 575, in get_or_create_cached_value
return_value = func(*args, **kwargs)
File "/home/sasha/nlp-viewer/run.py", line 132, in get
builder_instance.download_and_prepare()
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/nlp/builder.py", line 432, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/nlp/builder.py", line 488, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/home/sasha/.local/share/virtualenvs/lib-ogGKnCK_/lib/python3.7/site-packages/nlp/utils/info_utils.py", line 70, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
```
@srush @lhoestq | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/320/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/320/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/319 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/319/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/319/comments | https://api.github.com/repos/huggingface/datasets/issues/319/events | https://github.com/huggingface/datasets/issues/319 | 646,792,487 | MDU6SXNzdWU2NDY3OTI0ODc= | 319 | Nested sequences with dicts | {'avatar_url': 'https://avatars.githubusercontent.com/u/13795113?v=4', 'events_url': 'https://api.github.com/users/ghomasHudson/events{/privacy}', 'followers_url': 'https://api.github.com/users/ghomasHudson/followers', 'following_url': 'https://api.github.com/users/ghomasHudson/following{/other_user}', 'gists_url': 'https://api.github.com/users/ghomasHudson/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/ghomasHudson', 'id': 13795113, 'login': 'ghomasHudson', 'node_id': 'MDQ6VXNlcjEzNzk1MTEz', 'organizations_url': 'https://api.github.com/users/ghomasHudson/orgs', 'received_events_url': 'https://api.github.com/users/ghomasHudson/received_events', 'repos_url': 'https://api.github.com/users/ghomasHudson/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/ghomasHudson/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/ghomasHudson/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/ghomasHudson'} | [] | closed | false | null | [] | null | ['Oh yes, this is a backward compatibility feature with tensorflow_dataset in which a `Sequence` or `dict` is converted in a `dict` of `lists`, unfortunately it is not very intuitive, see here: https://github.com/huggingface/nlp/blob/master/src/nlp/features.py#L409\r\n\r\nTo avoid this behavior, you can just define the list in the feature with a simple list or a tuple (which is also simpler to write).\r\nIn your case, the features could be as follow:\r\n``` python\r\n...\r\nfeatures=nlp.Features({\r\n "title": nlp.Value("string"),\r\n "vertexSet": [[{\r\n "name": nlp.Value("string"),\r\n "sent_id": nlp.Value("int32"),\r\n "pos": nlp.features.Sequence(nlp.Value("int32")),\r\n "type": nlp.Value("string"),\r\n }]],\r\n ...\r\n }),\r\n...\r\n```'] | 2020-06-27 23:45:17+00:00 | 2020-07-03 10:22:00+00:00 | 2020-07-03 10:22:00+00:00 | CONTRIBUTOR | null | Am pretty much finished [adding a dataset](https://github.com/ghomasHudson/nlp/blob/DocRED/datasets/docred/docred.py) for [DocRED](https://github.com/thunlp/DocRED), but am getting an error when trying to add a nested `nlp.features.sequence(nlp.features.sequence({key:value,...}))`.
The original data is in this format:
```python
{
'title': "Title of wiki page",
'vertexSet': [
[
{ 'name': "mention_name",
'sent_id': "mention in which sentence",
'pos': ["postion of mention in a sentence"],
'type': "NER_type"},
{another mention}
],
[another entity]
]
...
}
```
So to represent this I've attempted to write:
```
...
features=nlp.Features({
"title": nlp.Value("string"),
"vertexSet": nlp.features.Sequence(nlp.features.Sequence({
"name": nlp.Value("string"),
"sent_id": nlp.Value("int32"),
"pos": nlp.features.Sequence(nlp.Value("int32")),
"type": nlp.Value("string"),
})),
...
}),
...
```
This is giving me the error:
```
pyarrow.lib.ArrowTypeError: Could not convert [{'pos': [[0,2], [2,4], [3,5]], "type": ["ORG", "ORG", "ORG"], "name": ["Lark Force", "Lark Force", "Lark Force", "sent_id": [0, 3, 4]}..... with type list: was not a dict, tuple, or recognized null value for conversion to struct type
```
Do we expect the pyarrow stuff to break when doing this deeper nesting? I've checked that it still works when you do `nlp.features.Sequence(nlp.features.Sequence(nlp.Value("string"))` or `nlp.features.Sequence({key:value,...})` just not nested sequences with a dict.
If it's not possible, I can always convert it to a shallower structure. I'd rather not change the DocRED authors' structure if I don't have to though. | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/319/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/319/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/318 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/318/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/318/comments | https://api.github.com/repos/huggingface/datasets/issues/318/events | https://github.com/huggingface/datasets/pull/318 | 646,682,840 | MDExOlB1bGxSZXF1ZXN0NDQwOTExOTYy | 318 | Multitask | {'avatar_url': 'https://avatars.githubusercontent.com/u/13795113?v=4', 'events_url': 'https://api.github.com/users/ghomasHudson/events{/privacy}', 'followers_url': 'https://api.github.com/users/ghomasHudson/followers', 'following_url': 'https://api.github.com/users/ghomasHudson/following{/other_user}', 'gists_url': 'https://api.github.com/users/ghomasHudson/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/ghomasHudson', 'id': 13795113, 'login': 'ghomasHudson', 'node_id': 'MDQ6VXNlcjEzNzk1MTEz', 'organizations_url': 'https://api.github.com/users/ghomasHudson/orgs', 'received_events_url': 'https://api.github.com/users/ghomasHudson/received_events', 'repos_url': 'https://api.github.com/users/ghomasHudson/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/ghomasHudson/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/ghomasHudson/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/ghomasHudson'} | [] | closed | false | null | [] | null | ["It's definitely going in the right direction ! Thanks for giving it a try\r\n\r\nI really like the API.\r\nIMO it's fine right now if we don't have all the dataset transforms (map, filter, etc.) as it can be done before building the multitask dataset, but it will be important to have them in the end.\r\nAll the formatting methods could easily be added though.\r\n\r\nI think there are some parts that will require some work with apache arrow like slicing. I can find a way to do it using pyarrow tables concatenation (I did something similar when implementing `__getitem__` with an input that is a list of indices [here](https://github.com/huggingface/nlp/pull/322/files#diff-73270df8d7f08c62a27e40806e1a5fb0R463-R469)). It is very fast and it allows to have the same output format as a normal Dataset.\r\n\r\nAlso maybe we should check that not only the columns but also the schemas match ?\r\nAnd maybe add the `seed` of the shuffling step as an argument ?\r\n\r\n"
'Maybe we should remove the methods that are not implemented for now, WDYT @thomwolf ?'
"That's an interesting first draft, thanks a lot for that and the user facing API is really nice.\r\n\r\nI think we should dive more into this and the questions of #217 before merging the first version though.\r\n\r\nIn particular, the typical way to do multi-tasking is usually to sample a task and then sample a batch within the selected task. I think we should probably stay be closer to this traditional approach, or at least make it very easy to do, rather than go to close to the T5 approach which is very specific to this paper.\r\n\r\nIn this regard, it seems important to find some way to address the remarks of @zphang. I'm still wondering if we should not adopt more of a sampling approach rather than an iteration approach."
"@thomwolf Thanks! I mainly wanted to get something working quickly for my own MTL research. I agree with a lot of the points you made so I'll convert this pull request back to a draft.\r\n\r\nFor your specific point about 'batch-level' multitask mixing, it would be a pretty trivial change to add a `batch_size` parameter and ensure every `batch_size` examples are from the same task. This would certainly work, but would add a notion of 'batches' to a Dataset, which does feel like a 'Sampler-level' concept and not a Dataset one. There's also the possibility of wanting some specific task-level sampling functionality (e.g. applying `SortishSampler` to each task) which would only work with this kind of 2 step sampling approach. My first proposal in the transformers repo was actually a Sampler https://github.com/huggingface/transformers/issues/4340. I wonder whether functionality at the sampler-level has a place in the vision for the `nlp` repo?\r\n\r\nI imagine following a sampling approach you'd have to abandon maintaining the same user-facing API as a standard dataset (A shame because replacing a single dataset seamlessly with a multitask one is a really nice user-experience).\r\n\r\nRandom half-Idea: You could have a class which accepts a list of any iterables (either a Dataset or a DataLoader which already is doing the batching). Not sure what interface you'd present though. hmmm. \r\n\r\nThere's definitely more discussion to have. \r\n"
"Are there any updates on making multi-task learning more officially supported in the datasets/transformers libraries? \r\nGiven that many papers use more than one task, it would be great to have multi-task learning more officially supported and easier to use. There are a few notebooks/blogs about using HF Transformers for this, but they all mention that it's more of a hack and not really officially supported (e.g. [this notebook](https://colab.research.google.com/github/zphang/zphang.github.io/blob/master/files/notebooks/Multi_task_Training_with_Transformers_NLP.ipynb#scrollTo=xW8bnTgCsx5c), or [this blog](https://medium.com/@shahrukhx01/multi-task-learning-with-transformers-part-1-multi-prediction-heads-b7001cf014bf)). \r\n\r\n[jiant](https://github.com/nyu-mll/jiant) was a framework built on transformers that made multi-task learning a first class feature of the library until recently, but they stopped maintaining their library a month ago ([see here](https://github.com/nyu-mll/jiant)). \r\nThis could be a good reason to increase support from the HF team? @lhoestq @thomwolf \r\n\r\nI'm not advanced enough to contribute on this, but an up-to-date notebook showing how to train a model e.g. on both MLM and next-sentence-prediction would already be very useful!"
"> Are there any updates on making multi-task learning more officially supported in the datasets/transformers libraries? Given that many papers use more than one task, it would be great to have multi-task learning more officially supported and easier to use. There are a few notebooks/blogs about using HF Transformers for this, but they all mention that it's more of a hack and not officially supported (e.g. [this notebook](https://colab.research.google.com/github/zphang/zphang.github.io/blob/master/files/notebooks/Multi_task_Training_with_Transformers_NLP.ipynb#scrollTo=xW8bnTgCsx5c), or [this blog](https://medium.com/@shahrukhx01/multi-task-learning-with-transformers-part-1-multi-prediction-heads-b7001cf014bf)).\r\n> \r\n> [jiant](https://github.com/nyu-mll/jiant) was a framework built on transformers that made multi-task learning a first class feature of the library until recently, but they stopped maintaining their library a month ago. This could be a good reason to increase support from the HF team? @lhoestq\r\n> \r\n> I'm not advanced enough to contribute on this, but an up-to-date notebook showing how to train a model e.g. on both MLM and NSP would already be very useful!\r\n\r\nI kinda stopped working on this as I didn't really get any response on an actual workable solution.\r\n\r\nThe problem that I came up against after initially being redirected here after [proposing this in the transformers repo](https://github.com/huggingface/transformers/issues/4340) ([among](https://github.com/huggingface/transformers/issues/6872) [others](https://github.com/huggingface/transformers/issues/1856)) , was the request be able to do the multitask mixing at the batch level as well as at the level of individual examples. As this repo doesn't really have the concept of 'batches' it would need to be implemented in the transformers repo, rather than here. You could then pick which level to do your multitask learning on.\r\n\r\nWork on T5 and as of last week, on [exT5](https://arxiv.org/pdf/2111.10952.pdf), have shown that multitask mixing on the example level works incredibly well (with a big enough batch size), so if you're ok doing that, then this pull request works.\r\n\r\nI completely agree that multitask learning is a vital part of modern NLP, nearly every piece of research code I write has at least some aspect of multitask learning (currently using this patch). Many of the top GLUE and SuperGLUE submissions are using some aspect of mutlitask learning. We need to support it."
"Fully agree. Batching and data loading is one important thing. The part I'm struggling with right now is the classification head (which is more part of the Transformers repo, but also essential for multi-task learning). @ghomasHudson, how do you tune two classification heads simultaneously? Say, when I want to fine-tune an existing base-model on some classification task (like NLI, or next-sentence-prediction) and at the same time add some MLM for regularisation & domain adaptation. In this case I need two classification heads, but I don't know how to switch them between the batches. "
"> Fully agree. Batching and data loading is one important thing. The part I'm struggling with right now is the classification head (which is more part of the Transformers repo, but also essential for multi-task learning). @ghomasHudson, how do you tune two classification heads simultaneously? Say, when I want to fine-tune an existing base-model on some classification task (like NLI, or next-sentence-prediction) and at the same time add some MLM for regularisation & domain adaptation. In this case I need two classification heads, but I don't know how to switch them between the batches.\r\n\r\nThis pull request is mainly focused on getting the data in the right format, but you're right that there's no easy way to pick between the heads without something like jiant. You could of course replicate this functionality yourself - probably by making a class that implements the functionality of both `ModelNameForSequenceClassification` or `ModelNameForMaskedLM` picking between them depending on some task parameter you add to the forward pass. \r\n\r\njiant make this approach model agnostic by [ignoring the custom per-model head implementations of huggingface](https://github.com/nyu-mll/jiant/blob/386d4e726a27becda1b03c241f064eb13c54860f/jiant/proj/main/modeling/heads.py#L17-L18), instead making generic versions. Then the jiant code [passes a `task` parameter](https://github.com/nyu-mll/jiant/blob/386d4e726a27becda1b03c241f064eb13c54860f/jiant/proj/main/modeling/primary.py#L107-L109) into their [JiantModel](https://github.com/nyu-mll/jiant/blob/386d4e726a27becda1b03c241f064eb13c54860f/jiant/proj/main/modeling/primary.py#L36-L79) wrapper. To implement this in huggingface transformers would require quite a few modifications to the current approach (potentially interfering with some other project aims e.g. code readability), so you might find it tricky to get a change like that accepted. It would be super cool though.\r\n\r\nAnd there's of course the exT5 way of doing things too where you sidestep this issue entirely by treating both tasks as text-to-text problems so you can end up with 100% shared parameters, e.g.\r\nMLM: `Lorem <mask_0> amet, consectetur <mask_1> do eiusmod tempor incididunt ut labore <mask_2>`\r\nNLI: `Premise: The Old One always comforted Ca'daan, except today. hypothesis: Ca'daan knew the Old One very well.`\r\nThis also allows you to do mixed batches of both tasks.\r\n\r\nPersonally, my research mainly focuses on this last approach, using the structure of the data itself to indicate the task rather than swapping in and out different parts of the network."
"Hi! `jiant` maintainer here, don't have much to add to the conversation yet but I'm happy to share my experience/thoughts on working with Multitask models if people have questions."
'Hi ! I think it could be easier to simply share as examples in `transformers` some code that uses `jiant` and/or subclass/reimplement some part of `transformers` for multitask ?'
'> Hi ! I think it could be easier to simply share as examples in `transformers` some code that uses `jiant` and/or subclass/reimplement some part of `transformers` for multitask ?\r\n\r\nWell since `jiant` requires new huggingface models to be explicitly added (as there are ["subtle differences in the models that jiant must abstract"](https://github.com/nyu-mll/jiant/blob/master/guides/models/adding_models.md)), and isn\'t being maintained anymore, then the first option might be out of date quickly.\r\n\r\nIf `transformers` could move towards making the task-specific heads more generic and as well as [creating a new base model in the `__init__` method](https://github.com/huggingface/transformers/blob/43f953cc2eec804eba04e2a9ae164d1a33fd97a8/src/transformers/models/bert/modeling_bert.py#L1502), allowing it to be passed as an argument (along with other little tweaks to standardize the approach), then this functionality could be moved into `transformers` itself.\r\n\r\nIt does seem a little redundant to have `jiant` as a library abstracting all the idiosyncrasies of each model type, where this could be done directly in the `transformers` repo in a single place alongside the model.\r\n\r\nIt\'s not an easy problem to solve though, especially balanced with the desire to expose models with minimal abstraction. @zphang probably knows more about this than me though.'
"As mentioned, one of the main obstacles is that HF/T doesn't support generic heads. At first glance, this should be easy, since the interface is quite simple: models output both a token-wise and a sequence representation (e.g. `[CLS]`), and heads use either one and output the corresponding predictions/losses.\r\n\r\nHowever, there are a number of cases where this doesn't work. One of them is multiple-choice tasks like HellaSwag, which is a multiple choice task with 4 text options. The way this is normally formatted is that you encode `context + question + option_X` for X=1..4, and then score all four options based on a scoring head and pick the highest scoring option as the prediction. This requires you to run the encoder on 4 separate inputs, which breaks the above abstraction (the task-specific model might need to call the encoder multiple times).\r\n\r\nAnother thing is batching. You can imagine with the above that you might want a different batch size for multiple-choice tasks compared to simpler classification tasks. This means you need task-specific batching as well. In addition, [it's been shown](https://arxiv.org/abs/2101.11038) that you really want to mix tasks within a single batch. This also leads into issues like how you want to sample different task examples, early stopping on them, how to mix the validation scores, etc. (`jiant` addressed these, through probably more-complicated-than-necessary configurations.)\r\n\r\nNone of these are insurmountable problems, but it requires some tweaking of the current code layout to get it to work. I would guess that it wouldn't take much work to get a 90% implementation."
"> Another thing is batching. You can imagine with the above that you might want a different batch size for multiple-choice tasks compared to simpler classification tasks. This means you need task-specific batching as well. In addition, [it's been shown](https://arxiv.org/abs/2101.11038) that you really want to mix tasks within a single batch. This also leads into issues like how you want to sample different task examples, early stopping on them, how to mix the validation scores, etc. (`jiant` addressed these, through probably more-complicated-than-necessary configurations.)\r\n\r\nThat's reassuring. exT5 find the same thing - that mixing tasks together in a batch gives better performance (provided the batch size is big enough that each batch contains a mix of different tasks). Assuming this, we can ignore doing things at the batch-level and just do this at the individual example level - in which case this pull request already does the data mixing part of the problem! Balancing different tasks could easily be added here by implementing temperature-scaled mixing, custom weights, etc...\r\n\r\nTo make a generic implementation of this using different heads would be hard (impossible?) without doing the sub-batching that Muppet do - in which case we're back at dealing with the 'batch' (sub-batch) level which would need an implementation in `transformers` not here.\r\n\r\n"
'Mixing at example should work fine. One issue though is that, as mentioned above, different tasks maybe actually require different amounts of memory, so downstream the user would have to find some way to handle that. But this might be one of those "the last/edge-case 10% is the hardest" to handle kind of deals.'
"Very true - there's always going to be those cases. I also feel that the way things are going, if we just leave this for a few years no one will be wanting to use task-specific heads anymore - it'll all be task prompts included in the input a-la GPT, T5, etc... which will make this substantially simpler to implement.\r\n\r\nIt's quite tricky to make a suitably non-opinionated generic version of this at the moment."
'> Is there an advantage to varying the proportions of each task in each batch\r\n\r\nSome tasks have much less data than others. E.g. SNLI vs. CoLA is almost a 100x difference, so people often sample differently-sized tasks differently.'
"As a short-term solution, I like @lhoestq's suggestion to create a notebook that shows how to implement multi-task learning by subclassing some transformer & dataset classes in a general way. I've been trying to get @zphang's [great but old notebook](https://colab.research.google.com/github/zphang/zphang.github.io/blob/master/files/notebooks/Multi_task_Training_with_Transformers_NLP.ipynb#scrollTo=CQ39AbTAPAUi) on multi-task learning running today and I didn't get it to work, probably because it was implemented a long time ago with `transformers==2.11`, `torch==1.2`~ etc and installing older versions still caused errors.\r\nThere is also this [interesting new repo](https://github.com/shahrukhx01/multitask-learning-transformers), which has a cool way of enabling you to save and load a model with two classification heads ([see model here](https://huggingface.co/shahrukhx01/bert-multitask-query-classifiers) and blog post [here](https://medium.com/@shahrukhx01/multi-task-learning-with-transformers-part-1-multi-prediction-heads-b7001cf014bf)). Haven't tried it yet, but it only uses `BertForSequenceClassification` instead of the more general AutoModelForXYZ\r\n\r\n@zphang, would you maybe be up for contributing an updated version of your older notebook with the latest version of `transformers` and `datasets` which runs in today's colabs? I feel like this would be very helpful for the community and if you keep the classes/functions somewhat general, people can easily adapt it to their use cases! 🙏 :) \r\nWould be a great addition to the [HF notebooks](https://huggingface.co/docs/transformers/notebooks).\r\n\r\nIn the medium-term, I agree that it would be great to have more native support for this via the HF libraries. I feels weird that you can neither train the old BERT (trained on two tasks) nor any of the newer models, without some hacks. "
'@zphang would love to see the newer notebook as suggested by @MoritzLaurer '] | 2020-06-27 13:27:29+00:00 | 2022-07-06 15:19:57+00:00 | 2022-07-06 15:19:57+00:00 | CONTRIBUTOR | null | Following our discussion in #217, I've implemented a first working version of `MultiDataset`.
There's a function `build_multitask()` which takes either individual `nlp.Dataset`s or `dicts` of splits and constructs `MultiDataset`(s). I've added a notebook with example usage.
I've implemented many of the `nlp.Dataset` methods (cache_files, columns, nbytes, num_columns, num_rows, column_names, schema, shape). Some of the other methods are complicated as they change the number of examples. These raise `NotImplementedError`s at the moment.
This will need some tests which I haven't written yet.
There's definitely room for improvements but I think the general approach is sound. | {'+1': 1, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 1, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/318/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/318/timeline | null | null | 1 | {'diff_url': 'https://github.com/huggingface/datasets/pull/318.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/318', 'merged_at': None, 'patch_url': 'https://github.com/huggingface/datasets/pull/318.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/318'} | true |
https://api.github.com/repos/huggingface/datasets/issues/317 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/317/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/317/comments | https://api.github.com/repos/huggingface/datasets/issues/317/events | https://github.com/huggingface/datasets/issues/317 | 646,555,384 | MDU6SXNzdWU2NDY1NTUzODQ= | 317 | Adding a dataset with multiple subtasks | {'avatar_url': 'https://avatars.githubusercontent.com/u/294483?v=4', 'events_url': 'https://api.github.com/users/erickrf/events{/privacy}', 'followers_url': 'https://api.github.com/users/erickrf/followers', 'following_url': 'https://api.github.com/users/erickrf/following{/other_user}', 'gists_url': 'https://api.github.com/users/erickrf/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/erickrf', 'id': 294483, 'login': 'erickrf', 'node_id': 'MDQ6VXNlcjI5NDQ4Mw==', 'organizations_url': 'https://api.github.com/users/erickrf/orgs', 'received_events_url': 'https://api.github.com/users/erickrf/received_events', 'repos_url': 'https://api.github.com/users/erickrf/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/erickrf/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/erickrf/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/erickrf'} | [] | closed | false | null | [] | null | ['For one dataset you can have different configurations that each have their own `nlp.Features`.\r\nWe imagine having one configuration per subtask for example.\r\nThey are loaded with `nlp.load_dataset("my_dataset", "my_config")`.\r\n\r\nFor example the `glue` dataset has many configurations. It is a bit different from your case though because each configuration is a dataset by itself (sst2, mnli).\r\nAnother example is `wikipedia` that has one configuration per language.'] | 2020-06-26 23:14:19+00:00 | 2020-10-27 15:36:52+00:00 | 2020-10-27 15:36:52+00:00 | NONE | null | I intent to add the datasets of the MT Quality Estimation shared tasks to `nlp`. However, they have different subtasks -- such as word-level, sentence-level and document-level quality estimation, each of which having different language pairs, and some of the data reused in different subtasks.
For example, in [QE 2019,](http://www.statmt.org/wmt19/qe-task.html) we had the same English-Russian and English-German data for word-level and sentence-level QE.
I suppose these datasets could have both their word and sentence-level labels inside `nlp.Features`; but what about other subtasks? Should they be considered a different dataset altogether?
I read the discussion on #217 but the case of QE seems a lot simpler. | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/317/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/317/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/316 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/316/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/316/comments | https://api.github.com/repos/huggingface/datasets/issues/316/events | https://github.com/huggingface/datasets/pull/316 | 646,366,450 | MDExOlB1bGxSZXF1ZXN0NDQwNjY5NzY5 | 316 | add AG News dataset | {'avatar_url': 'https://avatars.githubusercontent.com/u/13238952?v=4', 'events_url': 'https://api.github.com/users/jxmorris12/events{/privacy}', 'followers_url': 'https://api.github.com/users/jxmorris12/followers', 'following_url': 'https://api.github.com/users/jxmorris12/following{/other_user}', 'gists_url': 'https://api.github.com/users/jxmorris12/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/jxmorris12', 'id': 13238952, 'login': 'jxmorris12', 'node_id': 'MDQ6VXNlcjEzMjM4OTUy', 'organizations_url': 'https://api.github.com/users/jxmorris12/orgs', 'received_events_url': 'https://api.github.com/users/jxmorris12/received_events', 'repos_url': 'https://api.github.com/users/jxmorris12/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/jxmorris12/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/jxmorris12/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/jxmorris12'} | [] | closed | false | null | [] | null | ['Thanks @jxmorris12 for adding this adding. \r\nCan you please add a small description of the PR?'] | 2020-06-26 16:11:58+00:00 | 2020-06-30 09:58:08+00:00 | 2020-06-30 08:31:55+00:00 | CONTRIBUTOR | null | adds support for the AG-News topic classification dataset | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/316/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/316/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/316.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/316', 'merged_at': '2020-06-30T08:31:55Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/316.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/316'} | true |
https://api.github.com/repos/huggingface/datasets/issues/315 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/315/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/315/comments | https://api.github.com/repos/huggingface/datasets/issues/315/events | https://github.com/huggingface/datasets/issues/315 | 645,888,943 | MDU6SXNzdWU2NDU4ODg5NDM= | 315 | [Question] Best way to batch a large dataset? | {'avatar_url': 'https://avatars.githubusercontent.com/u/4564897?v=4', 'events_url': 'https://api.github.com/users/jarednielsen/events{/privacy}', 'followers_url': 'https://api.github.com/users/jarednielsen/followers', 'following_url': 'https://api.github.com/users/jarednielsen/following{/other_user}', 'gists_url': 'https://api.github.com/users/jarednielsen/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/jarednielsen', 'id': 4564897, 'login': 'jarednielsen', 'node_id': 'MDQ6VXNlcjQ1NjQ4OTc=', 'organizations_url': 'https://api.github.com/users/jarednielsen/orgs', 'received_events_url': 'https://api.github.com/users/jarednielsen/received_events', 'repos_url': 'https://api.github.com/users/jarednielsen/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/jarednielsen/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/jarednielsen/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/jarednielsen'} | [{'color': 'c5def5', 'default': False, 'description': 'Generic discussion on the library', 'id': 2067400324, 'name': 'generic discussion', 'node_id': 'MDU6TGFiZWwyMDY3NDAwMzI0', 'url': 'https://api.github.com/repos/huggingface/datasets/labels/generic%20discussion'}] | open | false | null | [] | null | ['Update: I think I\'ve found a solution.\r\n\r\n```python\r\noutput_types = {"input_ids": tf.int64, "token_type_ids": tf.int64, "attention_mask": tf.int64}\r\ndef train_dataset_gen():\r\n for i in range(len(train_dataset)):\r\n yield train_dataset[i]\r\ntf_dataset = tf.data.Dataset.from_generator(train_dataset_gen, output_types=output_types)\r\n```\r\n\r\nloads WikiText-2 in 20 ms, and WikiText-103 in 20 ms. It appears to be lazily loading via indexing train_dataset.'
"Yes this is the current best solution. We should probably show it in the tutorial notebook.\r\n\r\nNote that this solution unfortunately doesn't allow to train on TPUs (yet). See #193 "
'This approach still seems quite slow. When using TFRecords with a similar training loop, I get ~3.0-3.5 it/s on multi-node, multi-GPU training. I notice a pretty severe performance regression when scaling, with observed performance numbers. Since the allreduce step takes less than 100ms/it and I\'ve achieved 80% scaling efficiency up to 64 GPUs, it must be the data pipeline.\r\n\r\n| Nodes | GPUs | Iterations/Second |\r\n| --- | --- | --- |\r\n| 1 | 2 | 2.01 |\r\n| 1 | 8 | 0.81 |\r\n| 2 | 16 | 0.37 |\r\n\r\nHere are performance metrics over 10k steps. The iteration speed appears to follow some sort of caching pattern. I would love to use `nlp` in my project, but a slowdown from 3.0 it/s to 0.3 it/s is too great to stomach.\r\n\r\n<img width="1361" alt="Screen Shot 2020-07-02 at 8 29 22 AM" src="https://user-images.githubusercontent.com/4564897/86378156-2f8d3900-bc3e-11ea-918b-c395c3df5377.png">\r\n'
'An interesting alternative to investigate here would be to use the tf.io library which has some support for Arrow to TF conversion: https://www.tensorflow.org/io/api_docs/python/tfio/arrow/ArrowDataset\r\n\r\nThere are quite a few types supported, including lists so if the unsupported columns are dropped then we could maybe have a zero-copy mapping from Arrow to TensorFlow, including tokenized inputs and 1D tensors like the ones we mostly use in NLP: https://github.com/tensorflow/io/blob/322b3170c43ecac5c6af9e39dbd18fd747913e5a/tensorflow_io/arrow/python/ops/arrow_dataset_ops.py#L44-L72\r\n\r\nHere is an introduction on Arrow to TF using tf.io: https://medium.com/tensorflow/tensorflow-with-apache-arrow-datasets-cdbcfe80a59f'
'Interesting. There\'s no support for strings, but it does enable int and floats so that would work for tokenized inputs. \r\n\r\nArrowStreamDataset requires loading from a "record batch iterator", which can be instantiated from in-memory arrays as described here: https://arrow.apache.org/docs/python/ipc.html. \r\n\r\nBut the nlp.Dataset stores its data as a `pyarrow.lib.Table`, and the underlying features are `pyarrow.lib.ChunkedArray`. I can\'t find any documentation about lazily creating a record batch iterator from a ChunkedArray or a Table. Have you had any success?\r\n\r\nI can\'t find [any uses](https://grep.app/search?q=ArrowDataset&filter[lang][0]=Python) of tfio.arrow.ArrowDataset on GitHub.'
'You can use `to_batches` maybe?\r\nhttps://arrow.apache.org/docs/python/generated/pyarrow.Table.html#pyarrow.Table.to_batches'
"Also note that since #322 it is now possible to do\r\n```python\r\nids = [1, 10, 42, 100]\r\nbatch = dataset[ids]\r\n```\r\nFrom my experience it is quite fast but it can take lots of memory for large batches (haven't played that much with it).\r\nLet me know if you think there could be a better way to implement it. (current code is [here](https://github.com/huggingface/nlp/blob/78628649962671b4aaa31a6b24e7275533416845/src/nlp/arrow_dataset.py#L463))"
'Thanks @lhoestq! That format is much better to work with.\r\n\r\nI put together a benchmarking script. This doesn\'t measure the CPU-to-GPU efficiency, nor how it scales with multi-GPU multi-node training where many processes are making the same demands on the same dataset. But it does show some interesting results:\r\n\r\n```python\r\nimport nlp\r\nimport numpy as np\r\nimport tensorflow as tf\r\nimport time\r\n\r\ndset = nlp.load_dataset("wikitext", "wikitext-2-raw-v1", split="train")\r\ndset = dset.filter(lambda ex: len(ex["text"]) > 0)\r\nbsz = 1024\r\nn_batches = 100\r\n\r\ndef single_item_gen():\r\n for i in range(len(dset)):\r\n yield dset[i]\r\n\r\ndef sequential_batch_gen():\r\n for i in range(0, len(dset), bsz):\r\n yield dset[i:i+bsz]\r\n\r\ndef random_batch_gen():\r\n for i in range(len(dset)):\r\n indices = list(np.random.randint(len(dset), size=(bsz,)))\r\n yield dset[indices]\r\n\r\noutput_types = {"text": tf.string}\r\nsingle_item = tf.data.Dataset.from_generator(single_item_gen, output_types=output_types).batch(bsz)\r\ninterleaved = tf.data.Dataset.range(10).interleave(\r\n lambda idx: tf.data.Dataset.from_generator(single_item_gen, output_types=output_types),\r\n cycle_length=10,\r\n)\r\nsequential_batch = tf.data.Dataset.from_generator(sequential_batch_gen, output_types=output_types)\r\nrandom_batch = tf.data.Dataset.from_generator(random_batch_gen, output_types=output_types)\r\n\r\ndef iterate(tf_dset):\r\n start = time.perf_counter()\r\n for i, batch in enumerate(tf_dset.take(n_batches)):\r\n pass\r\n elapsed = time.perf_counter() - start\r\n print(f"{tf_dset} took {elapsed:.3f} secs")\r\n\r\niterate(single_item)\r\niterate(interleaved)\r\niterate(sequential_batch)\r\niterate(random_batch)\r\n```\r\n\r\nResults:\r\n```\r\n<BatchDataset shapes: {text: <unknown>}, types: {text: tf.string}> took 23.005 secs\r\n<InterleaveDataset shapes: {text: <unknown>}, types: {text: tf.string}> took 0.135 secs\r\n<FlatMapDataset shapes: {text: <unknown>}, types: {text: tf.string}> took 0.074 secs\r\n<FlatMapDataset shapes: {text: <unknown>}, types: {text: tf.string}> took 0.550 secs\r\n```\r\n\r\n- Batching a generator which fetches a single item is terrible.\r\n- Interleaving performs well on a single process, but doesn\'t scale well to multi-GPU training. I believe the bottleneck here is in Arrow dataset locking or something similar. The numbers from the table above are with interleaving.\r\n- The sequential access dominates the random access (7x faster). Is there any way to bring random access times closer to sequential access? Maybe re-indexing the dataset after shuffling each pass over the data.'
'Hey @jarednielsen \r\n\r\nThanks for this very interesting analysis!! IMHO to read text data one should use `tf.data.TextLineDataset`. It would be interesting to compare what you have done with simply load with a `TextLineDataset` and see if there is a difference.\r\n\r\nA good example can be found here https://www.tensorflow.org/tutorials/load_data/text'
"Thanks! I'm not actually loading in raw text data, that was just the synthetic data I created for this benchmark. A more realistic use case would be a dataset of tokenized examples, which would be a dict of lists of integers. TensorFlow's TextLineDataset greedily loads the dataset into the graph itself, which can lead to out-of-memory errors - one of the main reason I'm so drawn to the `nlp` library is its zero-copy no-RAM approach to dataset loading and mapping. \r\n\r\nIt's quite helpful for running a preprocessing pipeline - a sample ELECTRA pipeline I've built is here: https://github.com/jarednielsen/deep-learning-models/blob/nlp/models/nlp/common/preprocess.py."
'Sorry, I think I badly expressed myself, my bad. What I suggested is to compare with the usual loading textual data in pure TF with `TextLineDataset` with `nlp`. I know it is not recommended with very large datasets to use it, but I was curious to see how it behaves compared to a processing with `nlp` on smaller datasets.\r\n\r\nBTW your script looks very interesting, thanks for sharing!!'] | 2020-06-25 22:30:20+00:00 | 2020-10-27 15:38:17+00:00 | null | CONTRIBUTOR | null | I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb), I see the following recommended for TensorFlow:
```python
train_tf_dataset = train_tf_dataset.filter(remove_none_values, load_from_cache_file=False)
columns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']
train_tf_dataset.set_format(type='tensorflow', columns=columns)
features = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]}
labels = {"output_1": train_tf_dataset["start_positions"].to_tensor(default_value=0, shape=[None, 1])}
labels["output_2"] = train_tf_dataset["end_positions"].to_tensor(default_value=0, shape=[None, 1])
### Question about this last line ###
tfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)
```
This code works for something like WikiText-2. However, scaling up to WikiText-103, the last line takes 5-10 minutes to run. I assume it is because tf.data.Dataset.from_tensor_slices() is pulling everything into memory, not lazily loading. This approach won't scale up to datasets 25x larger such as Wikipedia.
So I tried manual batching using `dataset.select()`:
```python
idxs = np.random.randint(len(dataset), size=bsz)
batch = dataset.select(idxs).map(lambda example: {"input_ids": tokenizer(example["text"])})
tf_batch = tf.constant(batch["ids"], dtype=tf.int64)
```
This appears to create a new Apache Arrow dataset with every batch I grab, and then tries to cache it. The runtime of `dataset.select([0, 1])` appears to be much worse than `dataset[:2]`. So using `select()` doesn't seem to be performant enough for a training loop.
Is there a performant scalable way to lazily load batches of nlp Datasets? | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 1, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 1, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/315/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/315/timeline | null | null | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/314 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/314/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/314/comments | https://api.github.com/repos/huggingface/datasets/issues/314/events | https://github.com/huggingface/datasets/pull/314 | 645,461,174 | MDExOlB1bGxSZXF1ZXN0NDM5OTM4MTMw | 314 | Fixed singlular very minor spelling error | {'avatar_url': 'https://avatars.githubusercontent.com/u/40696362?v=4', 'events_url': 'https://api.github.com/users/SchizoidBat/events{/privacy}', 'followers_url': 'https://api.github.com/users/SchizoidBat/followers', 'following_url': 'https://api.github.com/users/SchizoidBat/following{/other_user}', 'gists_url': 'https://api.github.com/users/SchizoidBat/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/SchizoidBat', 'id': 40696362, 'login': 'SchizoidBat', 'node_id': 'MDQ6VXNlcjQwNjk2MzYy', 'organizations_url': 'https://api.github.com/users/SchizoidBat/orgs', 'received_events_url': 'https://api.github.com/users/SchizoidBat/received_events', 'repos_url': 'https://api.github.com/users/SchizoidBat/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/SchizoidBat/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/SchizoidBat/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/SchizoidBat'} | [] | closed | false | null | [] | null | ['Thank you BatJeti! The storm-joker, aka the typo, finally got caught!'] | 2020-06-25 10:45:59+00:00 | 2020-06-26 08:46:41+00:00 | 2020-06-25 12:43:59+00:00 | CONTRIBUTOR | null | An instance of "independantly" was changed to "independently". That's all. | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/314/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/314/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/314.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/314', 'merged_at': '2020-06-25T12:43:59Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/314.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/314'} | true |
https://api.github.com/repos/huggingface/datasets/issues/313 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/313/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/313/comments | https://api.github.com/repos/huggingface/datasets/issues/313/events | https://github.com/huggingface/datasets/pull/313 | 645,390,088 | MDExOlB1bGxSZXF1ZXN0NDM5ODc4MDg5 | 313 | Add MWSC | {'avatar_url': 'https://avatars.githubusercontent.com/u/13795113?v=4', 'events_url': 'https://api.github.com/users/ghomasHudson/events{/privacy}', 'followers_url': 'https://api.github.com/users/ghomasHudson/followers', 'following_url': 'https://api.github.com/users/ghomasHudson/following{/other_user}', 'gists_url': 'https://api.github.com/users/ghomasHudson/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/ghomasHudson', 'id': 13795113, 'login': 'ghomasHudson', 'node_id': 'MDQ6VXNlcjEzNzk1MTEz', 'organizations_url': 'https://api.github.com/users/ghomasHudson/orgs', 'received_events_url': 'https://api.github.com/users/ghomasHudson/received_events', 'repos_url': 'https://api.github.com/users/ghomasHudson/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/ghomasHudson/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/ghomasHudson/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/ghomasHudson'} | [] | closed | false | {'avatar_url': 'https://avatars.githubusercontent.com/u/23423619?v=4', 'events_url': 'https://api.github.com/users/patrickvonplaten/events{/privacy}', 'followers_url': 'https://api.github.com/users/patrickvonplaten/followers', 'following_url': 'https://api.github.com/users/patrickvonplaten/following{/other_user}', 'gists_url': 'https://api.github.com/users/patrickvonplaten/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/patrickvonplaten', 'id': 23423619, 'login': 'patrickvonplaten', 'node_id': 'MDQ6VXNlcjIzNDIzNjE5', 'organizations_url': 'https://api.github.com/users/patrickvonplaten/orgs', 'received_events_url': 'https://api.github.com/users/patrickvonplaten/received_events', 'repos_url': 'https://api.github.com/users/patrickvonplaten/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/patrickvonplaten/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/patrickvonplaten/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/patrickvonplaten'} | [{'avatar_url': 'https://avatars.githubusercontent.com/u/23423619?v=4', 'events_url': 'https://api.github.com/users/patrickvonplaten/events{/privacy}', 'followers_url': 'https://api.github.com/users/patrickvonplaten/followers', 'following_url': 'https://api.github.com/users/patrickvonplaten/following{/other_user}', 'gists_url': 'https://api.github.com/users/patrickvonplaten/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/patrickvonplaten', 'id': 23423619, 'login': 'patrickvonplaten', 'node_id': 'MDQ6VXNlcjIzNDIzNjE5', 'organizations_url': 'https://api.github.com/users/patrickvonplaten/orgs', 'received_events_url': 'https://api.github.com/users/patrickvonplaten/received_events', 'repos_url': 'https://api.github.com/users/patrickvonplaten/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/patrickvonplaten/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/patrickvonplaten/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/patrickvonplaten'}
{'avatar_url': 'https://avatars.githubusercontent.com/u/38249783?v=4', 'events_url': 'https://api.github.com/users/mariamabarham/events{/privacy}', 'followers_url': 'https://api.github.com/users/mariamabarham/followers', 'following_url': 'https://api.github.com/users/mariamabarham/following{/other_user}', 'gists_url': 'https://api.github.com/users/mariamabarham/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/mariamabarham', 'id': 38249783, 'login': 'mariamabarham', 'node_id': 'MDQ6VXNlcjM4MjQ5Nzgz', 'organizations_url': 'https://api.github.com/users/mariamabarham/orgs', 'received_events_url': 'https://api.github.com/users/mariamabarham/received_events', 'repos_url': 'https://api.github.com/users/mariamabarham/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/mariamabarham/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/mariamabarham/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/mariamabarham'}
{'avatar_url': 'https://avatars.githubusercontent.com/u/42851186?v=4', 'events_url': 'https://api.github.com/users/lhoestq/events{/privacy}', 'followers_url': 'https://api.github.com/users/lhoestq/followers', 'following_url': 'https://api.github.com/users/lhoestq/following{/other_user}', 'gists_url': 'https://api.github.com/users/lhoestq/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/lhoestq', 'id': 42851186, 'login': 'lhoestq', 'node_id': 'MDQ6VXNlcjQyODUxMTg2', 'organizations_url': 'https://api.github.com/users/lhoestq/orgs', 'received_events_url': 'https://api.github.com/users/lhoestq/received_events', 'repos_url': 'https://api.github.com/users/lhoestq/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/lhoestq/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/lhoestq/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/lhoestq'}] | null | ['Looks good to me'] | 2020-06-25 09:22:02+00:00 | 2020-06-30 08:28:11+00:00 | 2020-06-30 08:28:11+00:00 | CONTRIBUTOR | null | Adding the [Modified Winograd Schema Challenge](https://github.com/salesforce/decaNLP/blob/master/local_data/schema.txt) dataset which formed part of the [decaNLP](http://decanlp.com/) benchmark. Not sure how much use people would find for it it outside of the benchmark, but it is general purpose.
Code is heavily borrowed from the [decaNLP repo](https://github.com/salesforce/decaNLP/blob/1e9605f246b9e05199b28bde2a2093bc49feeeaa/text/torchtext/datasets/generic.py#L773-L877).
There's a few (possibly overly opinionated) design choices I made:
- I used the train/test/dev split [buried in the decaNLP code](https://github.com/salesforce/decaNLP/blob/1e9605f246b9e05199b28bde2a2093bc49feeeaa/text/torchtext/datasets/generic.py#L852-L855)
- I split out each example into the 2 alternatives. Originally the data uses the format:
```
The city councilmen refused the demonstrators a permit because they [feared/advocated] violence.
Who [feared/advocated] violence?
councilmen/demonstrators
```
I split into the 2 variants:
```
The city councilmen refused the demonstrators a permit because they feared violence.
Who feared violence?
councilmen/demonstrators
The city councilmen refused the demonstrators a permit because they advocated violence.
Who advocated violence?
councilmen/demonstrators
```
I can't see any use for having the options combined into a single example (splitting them is [the way decaNLP processes](https://github.com/salesforce/decaNLP/blob/1e9605f246b9e05199b28bde2a2093bc49feeeaa/text/torchtext/datasets/generic.py#L846-L850)) them. You can't train on both versions with them combined, and splitting the examples later would be a pain to do. I think [winogrande.py](https://github.com/huggingface/nlp/blob/master/datasets/winogrande/winogrande.py) presents the data in this way?
- I've not used the decaNLP framing (appending the options to the question e.g. `Who feared violence?
-- councilmen or demonstrators?`) but left it more generic by adding the options as a new key: `"options":["councilmen","demonstrators"]` This should be an easy thing to change using `map` if needed by a specific application.
Dataset is working as-is but if anyone has any thoughts/preferences on the design decisions here I'm definitely open to different choices. | {'+1': 3, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 3, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/313/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/313/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/313.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/313', 'merged_at': '2020-06-30T08:28:10Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/313.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/313'} | true |
https://api.github.com/repos/huggingface/datasets/issues/312 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/312/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/312/comments | https://api.github.com/repos/huggingface/datasets/issues/312/events | https://github.com/huggingface/datasets/issues/312 | 645,025,561 | MDU6SXNzdWU2NDUwMjU1NjE= | 312 | [Feature request] Add `shard()` method to dataset | {'avatar_url': 'https://avatars.githubusercontent.com/u/4564897?v=4', 'events_url': 'https://api.github.com/users/jarednielsen/events{/privacy}', 'followers_url': 'https://api.github.com/users/jarednielsen/followers', 'following_url': 'https://api.github.com/users/jarednielsen/following{/other_user}', 'gists_url': 'https://api.github.com/users/jarednielsen/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/jarednielsen', 'id': 4564897, 'login': 'jarednielsen', 'node_id': 'MDQ6VXNlcjQ1NjQ4OTc=', 'organizations_url': 'https://api.github.com/users/jarednielsen/orgs', 'received_events_url': 'https://api.github.com/users/jarednielsen/received_events', 'repos_url': 'https://api.github.com/users/jarednielsen/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/jarednielsen/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/jarednielsen/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/jarednielsen'} | [] | closed | false | null | [] | null | ['Hi Jared,\r\nInteresting, thanks for raising this question. You can also do that after loading with `dataset.select()` or `dataset.filter()` which let you keep only a specific subset of rows in a dataset.\r\nWhat is your use-case for sharding?'
"Thanks for the pointer to those functions! It's still a little more verbose since you have to manually calculate which ids each rank would keep, but definitely works.\r\n\r\nMy use case is multi-node, multi-GPU training and avoiding global batches of duplicate elements. I'm using horovod. You can shuffle indices, or set random seeds, but explicitly sharding the dataset up front is the safest and clearest way I've found to do so."] | 2020-06-24 22:48:33+00:00 | 2020-07-06 12:35:36+00:00 | 2020-07-06 12:35:36+00:00 | CONTRIBUTOR | null | Currently, to shard a dataset into 10 pieces on different ranks, you can run
```python
rank = 3 # for example
size = 10
dataset = nlp.load_dataset('wikitext', 'wikitext-2-raw-v1', split=f"train[{rank*10}%:{(rank+1)*10}%]")
```
However, this breaks down if you have a number of ranks that doesn't divide cleanly into 100, such as 64 ranks. Is there interest in adding a method shard() that looks like this?
```python
rank = 3
size = 64
dataset = nlp.load_dataset("wikitext", "wikitext-2-raw-v1", split="train").shard(rank=rank, size=size)
```
TensorFlow has a similar API: https://www.tensorflow.org/api_docs/python/tf/data/Dataset#shard. I'd be happy to contribute this code. | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/312/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/312/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/311 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/311/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/311/comments | https://api.github.com/repos/huggingface/datasets/issues/311/events | https://github.com/huggingface/datasets/pull/311 | 645,013,131 | MDExOlB1bGxSZXF1ZXN0NDM5NTQ3OTg0 | 311 | Add qa_zre | {'avatar_url': 'https://avatars.githubusercontent.com/u/13795113?v=4', 'events_url': 'https://api.github.com/users/ghomasHudson/events{/privacy}', 'followers_url': 'https://api.github.com/users/ghomasHudson/followers', 'following_url': 'https://api.github.com/users/ghomasHudson/following{/other_user}', 'gists_url': 'https://api.github.com/users/ghomasHudson/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/ghomasHudson', 'id': 13795113, 'login': 'ghomasHudson', 'node_id': 'MDQ6VXNlcjEzNzk1MTEz', 'organizations_url': 'https://api.github.com/users/ghomasHudson/orgs', 'received_events_url': 'https://api.github.com/users/ghomasHudson/received_events', 'repos_url': 'https://api.github.com/users/ghomasHudson/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/ghomasHudson/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/ghomasHudson/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/ghomasHudson'} | [] | closed | false | null | [] | null | [] | 2020-06-24 22:17:22+00:00 | 2020-06-29 16:37:38+00:00 | 2020-06-29 16:37:38+00:00 | CONTRIBUTOR | null | Adding the QA-ZRE dataset from ["Zero-Shot Relation Extraction via Reading Comprehension"](http://nlp.cs.washington.edu/zeroshot/).
A common processing step seems to be replacing the `XXX` placeholder with the `subject`. I've left this out as it's something you could easily do with `map`. | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/311/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/311/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/311.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/311', 'merged_at': '2020-06-29T16:37:38Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/311.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/311'} | true |
https://api.github.com/repos/huggingface/datasets/issues/310 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/310/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/310/comments | https://api.github.com/repos/huggingface/datasets/issues/310/events | https://github.com/huggingface/datasets/pull/310 | 644,806,720 | MDExOlB1bGxSZXF1ZXN0NDM5MzY1MDg5 | 310 | add wikisql | {'avatar_url': 'https://avatars.githubusercontent.com/u/13795113?v=4', 'events_url': 'https://api.github.com/users/ghomasHudson/events{/privacy}', 'followers_url': 'https://api.github.com/users/ghomasHudson/followers', 'following_url': 'https://api.github.com/users/ghomasHudson/following{/other_user}', 'gists_url': 'https://api.github.com/users/ghomasHudson/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/ghomasHudson', 'id': 13795113, 'login': 'ghomasHudson', 'node_id': 'MDQ6VXNlcjEzNzk1MTEz', 'organizations_url': 'https://api.github.com/users/ghomasHudson/orgs', 'received_events_url': 'https://api.github.com/users/ghomasHudson/received_events', 'repos_url': 'https://api.github.com/users/ghomasHudson/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/ghomasHudson/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/ghomasHudson/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/ghomasHudson'} | [] | closed | false | null | [] | null | ["That's great work @ghomasHudson !"] | 2020-06-24 18:00:35+00:00 | 2020-06-25 12:32:25+00:00 | 2020-06-25 12:32:25+00:00 | CONTRIBUTOR | null | Adding the [WikiSQL](https://github.com/salesforce/WikiSQL) dataset.
Interesting things to note:
- Have copied the function (`_convert_to_human_readable`) which converts the SQL query to a human-readable (string) format as this is what most people will want when actually using this dataset for NLP applications.
- `conds` was originally a tuple but is converted to a dictionary to support differing types.
Would be nice to add the logical_form metrics too at some point. | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/310/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/310/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/310.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/310', 'merged_at': '2020-06-25T12:32:25Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/310.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/310'} | true |
https://api.github.com/repos/huggingface/datasets/issues/309 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/309/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/309/comments | https://api.github.com/repos/huggingface/datasets/issues/309/events | https://github.com/huggingface/datasets/pull/309 | 644,783,822 | MDExOlB1bGxSZXF1ZXN0NDM5MzQ1NzYz | 309 | Add narrative qa | {'avatar_url': 'https://avatars.githubusercontent.com/u/8019486?v=4', 'events_url': 'https://api.github.com/users/Varal7/events{/privacy}', 'followers_url': 'https://api.github.com/users/Varal7/followers', 'following_url': 'https://api.github.com/users/Varal7/following{/other_user}', 'gists_url': 'https://api.github.com/users/Varal7/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/Varal7', 'id': 8019486, 'login': 'Varal7', 'node_id': 'MDQ6VXNlcjgwMTk0ODY=', 'organizations_url': 'https://api.github.com/users/Varal7/orgs', 'received_events_url': 'https://api.github.com/users/Varal7/received_events', 'repos_url': 'https://api.github.com/users/Varal7/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/Varal7/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/Varal7/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/Varal7'} | [] | closed | false | null | [] | null | ['Does it make sense to download the full stories? I remember attempting to implement this dataset a while ago and ended up with something like:\r\n```python\r\n def _split_generators(self, dl_manager):\r\n """Returns SplitGenerators."""\r\n\r\n dl_dir = dl_manager.download_and_extract(_DOWNLOAD_URL)\r\n data_dir = os.path.join(dl_dir, "narrativeqa-master")\r\n\r\n urls = {"test":{}, "train": {},"valid":{}}\r\n with open(os.path.join(data_dir,"documents.csv")) as f_in:\r\n csv_reader = csv.reader(f_in)\r\n next(csv_reader) # discard header row\r\n for i,row in enumerate(csv_reader):\r\n if i > 1572:\r\n break\r\n if row != []:\r\n urls[row[1]][row[0]] = row[3]\r\n\r\n url_files = {}\r\n for key in urls.keys():\r\n url_files[key] = dl_manager.download_and_extract(urls[key])\r\n\r\n return [\r\n nlp.SplitGenerator(\r\n name=nlp.Split.TRAIN,\r\n gen_kwargs={\r\n "data_dir":data_dir,\r\n "split":"train",\r\n "doc_id_to_path":url_files["train"]\r\n }\r\n ),\r\n ....\r\n```\r\nIt does end up cluttering your huggingface cache dir though.'
'Also since there doesn\'t seem to be any meaning in the order of answer_1 and answer_2, it might make sense to combine them (see [squad.py](https://github.com/huggingface/nlp/blob/8b0ffc85e4e52ae1f18d31be99b6c70b82c991ca/datasets/squad/squad.py#L86-L88)):\r\n```python\r\n"answers": nlp.features.Sequence({\r\n "text": nlp.Value("string"),\r\n "tokenized": nlp.features.Sequence(nlp.Value("string"))\r\n})\r\n```\r\n(the tokenized features should also probably be lists of strings not just strings - see [natural_questions.py](https://github.com/huggingface/nlp/blob/4cd34287300a1135ce7b22f6dd209ca305c71b3a/datasets/natural_questions/natural_questions.py#L83))\r\n\r\nAgain, this is a personal preference thing, but it might be useful to combine the document-related features:\r\n```python\r\n{\r\n "document": {\r\n "id": nlp.Value("string"),\r\n "kind": nlp.Value("string"),\r\n "url": nlp.Value("string"),\r\n "file_size": nlp.Value("int32"),\r\n "word_count": nlp.Value("int32"),\r\n "start": nlp.Value("string"),\r\n "end": nlp.Value("string"),\r\n "wiki_url": nlp.Value("string"),\r\n "wiki_title": nlp.Value("string"),\r\n "summary": nlp.features.Sequence({\r\n "text": nlp.Value("string"),\r\n "tokens": nlp.features.Sequence(nlp.Value("string"))\r\n }),\r\n "text": nlp.Value("string"),\r\n },\r\n "question": nlp.features.Sequence({\r\n "text": nlp.Value("string"),\r\n "tokens": nlp.features.Sequence(nlp.Value("string"))\r\n }),\r\n "answers": nlp.features.Sequence({\r\n "text": nlp.Value("string"),\r\n "tokens": nlp.features.Sequence(nlp.Value("string"))\r\n })\r\n}\r\n```'
'Did you manage to fix the dummy data @Varal7 ?'
"@lhoestq do you think it's acceptable for the `dl_manager` to go grab all the individual stories from project gutenburg? I've got a working version of that but it does clutter up your huggingface cache somewhat.\r\n\r\nThe real value (and original purpose) of this dataset is doing question answering on the full text."
"> @lhoestq do you think it's acceptable for the `dl_manager` to go grab all the individual stories from project gutenburg? I've got a working version of that but it does clutter up your huggingface cache somewhat.\r\n> \r\n> The real value (and original purpose) of this dataset is doing question answering on the full text.\r\n\r\nWhat's the problem exactly with the cache ?"
"Nothing, just that because each story is a separate download it gets a bit messy as all 1573 files are under `~/.cache/hugginface/datasets` rather than organized under a subdir.\r\n\r\nProbably doesn't matter to the end user though."
"Yea I agree it's a mess. I just created #393 to make things easier."
"I got the PR merged to have a cleaner the cache directory (everything is downloaded inside the 'downloads' sub-directory).\r\nFeel free to download all the stories then @ghomasHudson @Varal7 x)\r\nIf you have the possibility of downloading a compressed file with most of the stories at once it would be better though."
"Looks good @lhoestq . The problem I'm having at the moment is that stories from project Gutenberg occasionally fail. All books are out of copyright so we should be able to host them. \r\n\r\nHere's a zip file of the full text if we have anywhere to put them: https://drive.google.com/file/d/17jOR7NqvzDwSlPXrlHaYV-PGI8JG-KY5/view?usp=sharing\r\n"
'I put the zip file here @ghomasHudson \r\nhttps://storage.googleapis.com/huggingface-nlp/datasets/narrative_qa/narrativeqa_full_text.zip\r\n\r\nSorry for the delay'
'Closing in favor of #499'] | 2020-06-24 17:26:18+00:00 | 2020-09-03 09:02:10+00:00 | 2020-09-03 09:02:09+00:00 | NONE | null | Test cases for dummy data don't pass
Only contains data for summaries (not whole story) | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/309/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/309/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/309.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/309', 'merged_at': None, 'patch_url': 'https://github.com/huggingface/datasets/pull/309.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/309'} | true |
https://api.github.com/repos/huggingface/datasets/issues/308 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/308/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/308/comments | https://api.github.com/repos/huggingface/datasets/issues/308/events | https://github.com/huggingface/datasets/pull/308 | 644,195,251 | MDExOlB1bGxSZXF1ZXN0NDM4ODYyMzYy | 308 | Specify utf-8 encoding for MRPC files | {'avatar_url': 'https://avatars.githubusercontent.com/u/15801338?v=4', 'events_url': 'https://api.github.com/users/patpizio/events{/privacy}', 'followers_url': 'https://api.github.com/users/patpizio/followers', 'following_url': 'https://api.github.com/users/patpizio/following{/other_user}', 'gists_url': 'https://api.github.com/users/patpizio/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/patpizio', 'id': 15801338, 'login': 'patpizio', 'node_id': 'MDQ6VXNlcjE1ODAxMzM4', 'organizations_url': 'https://api.github.com/users/patpizio/orgs', 'received_events_url': 'https://api.github.com/users/patpizio/received_events', 'repos_url': 'https://api.github.com/users/patpizio/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/patpizio/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/patpizio/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/patpizio'} | [] | closed | false | null | [] | null | [] | 2020-06-23 22:44:36+00:00 | 2020-06-25 12:52:21+00:00 | 2020-06-25 12:16:10+00:00 | CONTRIBUTOR | null | Fixes #307, again probably a Windows-related issue. | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/308/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/308/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/308.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/308', 'merged_at': '2020-06-25T12:16:09Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/308.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/308'} | true |
https://api.github.com/repos/huggingface/datasets/issues/307 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/307/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/307/comments | https://api.github.com/repos/huggingface/datasets/issues/307/events | https://github.com/huggingface/datasets/issues/307 | 644,187,262 | MDU6SXNzdWU2NDQxODcyNjI= | 307 | Specify encoding for MRPC | {'avatar_url': 'https://avatars.githubusercontent.com/u/15801338?v=4', 'events_url': 'https://api.github.com/users/patpizio/events{/privacy}', 'followers_url': 'https://api.github.com/users/patpizio/followers', 'following_url': 'https://api.github.com/users/patpizio/following{/other_user}', 'gists_url': 'https://api.github.com/users/patpizio/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/patpizio', 'id': 15801338, 'login': 'patpizio', 'node_id': 'MDQ6VXNlcjE1ODAxMzM4', 'organizations_url': 'https://api.github.com/users/patpizio/orgs', 'received_events_url': 'https://api.github.com/users/patpizio/received_events', 'repos_url': 'https://api.github.com/users/patpizio/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/patpizio/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/patpizio/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/patpizio'} | [] | closed | false | null | [] | null | [] | 2020-06-23 22:24:49+00:00 | 2020-06-25 12:16:09+00:00 | 2020-06-25 12:16:09+00:00 | CONTRIBUTOR | null | Same as #242, but with MRPC: on Windows, I get a `UnicodeDecodeError` when I try to download the dataset:
```python
dataset = nlp.load_dataset('glue', 'mrpc')
```
```python
Downloading and preparing dataset glue/mrpc (download: Unknown size, generated: Unknown size, total: Unknown size) to C:\Users\Python\.cache\huggingface\datasets\glue\mrpc\1.0.0...
---------------------------------------------------------------------------
UnicodeDecodeError Traceback (most recent call last)
~\Miniconda3\envs\nlp\lib\site-packages\nlp\builder.py in incomplete_dir(dirname)
369 try:
--> 370 yield tmp_dir
371 if os.path.isdir(dirname):
~\Miniconda3\envs\nlp\lib\site-packages\nlp\builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
430 verify_infos = not save_infos and not ignore_verifications
--> 431 self._download_and_prepare(
432 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
~\Miniconda3\envs\nlp\lib\site-packages\nlp\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
482 # Prepare split will record examples associated to the split
--> 483 self._prepare_split(split_generator, **prepare_split_kwargs)
484 except OSError:
~\Miniconda3\envs\nlp\lib\site-packages\nlp\builder.py in _prepare_split(self, split_generator)
663 generator = self._generate_examples(**split_generator.gen_kwargs)
--> 664 for key, record in utils.tqdm(generator, unit=" examples", total=split_info.num_examples, leave=False):
665 example = self.info.features.encode_example(record)
~\Miniconda3\envs\nlp\lib\site-packages\tqdm\notebook.py in __iter__(self, *args, **kwargs)
217 try:
--> 218 for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):
219 # return super(tqdm...) will not catch exception
~\Miniconda3\envs\nlp\lib\site-packages\tqdm\std.py in __iter__(self)
1128 try:
-> 1129 for obj in iterable:
1130 yield obj
~\Miniconda3\envs\nlp\lib\site-packages\nlp\datasets\glue\7fc58099eb3983a04c8dac8500b70d27e6eceae63ffb40d7900c977897bb58c6\glue.py in _generate_examples(self, data_file, split, mrpc_files)
514 examples = self._generate_example_mrpc_files(mrpc_files=mrpc_files, split=split)
--> 515 for example in examples:
516 yield example["idx"], example
~\Miniconda3\envs\nlp\lib\site-packages\nlp\datasets\glue\7fc58099eb3983a04c8dac8500b70d27e6eceae63ffb40d7900c977897bb58c6\glue.py in _generate_example_mrpc_files(self, mrpc_files, split)
576 reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
--> 577 for n, row in enumerate(reader):
578 is_row_in_dev = [row["#1 ID"], row["#2 ID"]] in dev_ids
~\Miniconda3\envs\nlp\lib\csv.py in __next__(self)
110 self.fieldnames
--> 111 row = next(self.reader)
112 self.line_num = self.reader.line_num
~\Miniconda3\envs\nlp\lib\encodings\cp1252.py in decode(self, input, final)
22 def decode(self, input, final=False):
---> 23 return codecs.charmap_decode(input,self.errors,decoding_table)[0]
24
UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 1180: character maps to <undefined>
```
The fix is the same: specify `utf-8` encoding when opening the file. The previous fix didn't work as MRPC's download process is different from the others in GLUE.
I am going to propose a new PR :) | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/307/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/307/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/306 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/306/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/306/comments | https://api.github.com/repos/huggingface/datasets/issues/306/events | https://github.com/huggingface/datasets/pull/306 | 644,176,078 | MDExOlB1bGxSZXF1ZXN0NDM4ODQ2MTI3 | 306 | add pg19 dataset | {'avatar_url': 'https://avatars.githubusercontent.com/u/108653?v=4', 'events_url': 'https://api.github.com/users/lucidrains/events{/privacy}', 'followers_url': 'https://api.github.com/users/lucidrains/followers', 'following_url': 'https://api.github.com/users/lucidrains/following{/other_user}', 'gists_url': 'https://api.github.com/users/lucidrains/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/lucidrains', 'id': 108653, 'login': 'lucidrains', 'node_id': 'MDQ6VXNlcjEwODY1Mw==', 'organizations_url': 'https://api.github.com/users/lucidrains/orgs', 'received_events_url': 'https://api.github.com/users/lucidrains/received_events', 'repos_url': 'https://api.github.com/users/lucidrains/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/lucidrains/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/lucidrains/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/lucidrains'} | [] | closed | false | null | [] | null | ['@lucidrains - Thanks a lot for making the PR - PG19 is a super important dataset! Thanks for making it. Many people are asking for PG-19, so it would be great to have that in the library as soon as possible @thomwolf .'
'@mariamabarham yup! around 11GB!'
"I'm looking forward to our first deep learning written novel already lol. It's definitely happening"
'Good to merge IMO.'
'Oh I just noticed but as we changed the urls to download the files, we have to update `dataset_infos.json`.\r\nCould you re-rurn `nlp-cli test ./datasets/pg19 --save_infos` ?'
'@lhoestq on it!' 'should be good!'
"@lhoestq - I think it's good to merge no?"
'`dataset_infos.json` is still not up to date with the new urls (we can see that there are urls like `gs://deepmind-gutenberg/train/*` instead of `https://storage.googleapis.com/deepmind-gutenberg/train/*` in the json file)\r\n\r\nCan you check that you re-ran the command to update the json file, and that you pushed the changes @lucidrains ?'
"@lhoestq ohhh, I made the change in this commit https://github.com/lucidrains/nlp/commit/f3e23d823ad9942031be80b7c4e4212c592cd90c , that's interesting that the pull request didn't pick it up. maybe it's because I did it on another machine, let me check and get back to you!"
'@lhoestq wrong branch 😅 thanks for catching! ' 'Awesome thanks 🎉'] | 2020-06-23 22:03:52+00:00 | 2020-07-06 07:55:59+00:00 | 2020-07-06 07:55:59+00:00 | CONTRIBUTOR | null | https://github.com/huggingface/nlp/issues/274
Add functioning PG19 dataset with dummy data
`cos_e.py` was just auto-linted by `make style` | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/306/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/306/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/306.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/306', 'merged_at': '2020-07-06T07:55:59Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/306.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/306'} | true |
https://api.github.com/repos/huggingface/datasets/issues/305 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/305/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/305/comments | https://api.github.com/repos/huggingface/datasets/issues/305/events | https://github.com/huggingface/datasets/issues/305 | 644,148,149 | MDU6SXNzdWU2NDQxNDgxNDk= | 305 | Importing downloaded package repository fails | {'avatar_url': 'https://avatars.githubusercontent.com/u/10469459?v=4', 'events_url': 'https://api.github.com/users/yjernite/events{/privacy}', 'followers_url': 'https://api.github.com/users/yjernite/followers', 'following_url': 'https://api.github.com/users/yjernite/following{/other_user}', 'gists_url': 'https://api.github.com/users/yjernite/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/yjernite', 'id': 10469459, 'login': 'yjernite', 'node_id': 'MDQ6VXNlcjEwNDY5NDU5', 'organizations_url': 'https://api.github.com/users/yjernite/orgs', 'received_events_url': 'https://api.github.com/users/yjernite/received_events', 'repos_url': 'https://api.github.com/users/yjernite/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/yjernite/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/yjernite/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/yjernite'} | [{'color': '25b21e', 'default': False, 'description': 'A bug in a metric script', 'id': 2067393914, 'name': 'metric bug', 'node_id': 'MDU6TGFiZWwyMDY3MzkzOTE0', 'url': 'https://api.github.com/repos/huggingface/datasets/labels/metric%20bug'}] | closed | false | null | [] | null | [] | 2020-06-23 21:09:05+00:00 | 2020-07-30 16:44:23+00:00 | 2020-07-30 16:44:23+00:00 | MEMBER | null | The `get_imports` function in `src/nlp/load.py` has a feature to download a package as a zip archive of the github repository and import functions from the unpacked directory. This is used for example in the `metrics/coval.py` file, and would be useful to add BLEURT (@ankparikh).
Currently however, the code seems to have trouble with imports within the package. For example:
```
import nlp
coval = nlp.load_metric('coval')
```
yields:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/yacine/Code/nlp/src/nlp/load.py", line 432, in load_metric
metric_cls = import_main_class(module_path, dataset=False)
File "/home/yacine/Code/nlp/src/nlp/load.py", line 57, in import_main_class
module = importlib.import_module(module_path)
File "/home/yacine/anaconda3/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1006, in _gcd_import
File "<frozen importlib._bootstrap>", line 983, in _find_and_load
File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 728, in exec_module
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "/home/yacine/Code/nlp/src/nlp/metrics/coval/a78807df33ac45edbb71799caf2b3b47e55df4fd690267808fe963a5e8b30952/coval.py", line 21, in <module>
from .coval_backend.conll import reader # From: https://github.com/ns-moosavi/coval
File "/home/yacine/Code/nlp/src/nlp/metrics/coval/a78807df33ac45edbb71799caf2b3b47e55df4fd690267808fe963a5e8b30952/coval_backend/conll/reader.py", line 2, in <module>
from conll import mention
ModuleNotFoundError: No module named 'conll'
```
Not sure what the fix would be there. | {'+1': 0, '-1': 0, 'confused': 1, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 1, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/305/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/305/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/304 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/304/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/304/comments | https://api.github.com/repos/huggingface/datasets/issues/304/events | https://github.com/huggingface/datasets/issues/304 | 644,091,970 | MDU6SXNzdWU2NDQwOTE5NzA= | 304 | Problem while printing doc string when instantiating multiple metrics. | {'avatar_url': 'https://avatars.githubusercontent.com/u/51091425?v=4', 'events_url': 'https://api.github.com/users/codehunk628/events{/privacy}', 'followers_url': 'https://api.github.com/users/codehunk628/followers', 'following_url': 'https://api.github.com/users/codehunk628/following{/other_user}', 'gists_url': 'https://api.github.com/users/codehunk628/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/codehunk628', 'id': 51091425, 'login': 'codehunk628', 'node_id': 'MDQ6VXNlcjUxMDkxNDI1', 'organizations_url': 'https://api.github.com/users/codehunk628/orgs', 'received_events_url': 'https://api.github.com/users/codehunk628/received_events', 'repos_url': 'https://api.github.com/users/codehunk628/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/codehunk628/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/codehunk628/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/codehunk628'} | [{'color': '25b21e', 'default': False, 'description': 'A bug in a metric script', 'id': 2067393914, 'name': 'metric bug', 'node_id': 'MDU6TGFiZWwyMDY3MzkzOTE0', 'url': 'https://api.github.com/repos/huggingface/datasets/labels/metric%20bug'}] | closed | false | null | [] | null | [] | 2020-06-23 19:32:05+00:00 | 2020-07-22 09:50:58+00:00 | 2020-07-22 09:50:58+00:00 | CONTRIBUTOR | null | When I load more than one metric and try to print doc string of a particular metric,. It shows the doc strings of all imported metric one after the other which looks quite confusing and clumsy.
Attached [Colab](https://colab.research.google.com/drive/13H0ZgyQ2se0mqJ2yyew0bNEgJuHaJ8H3?usp=sharing) Notebook for problem clarification.. | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/304/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/304/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/303 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/303/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/303/comments | https://api.github.com/repos/huggingface/datasets/issues/303/events | https://github.com/huggingface/datasets/pull/303 | 643,912,464 | MDExOlB1bGxSZXF1ZXN0NDM4NjI3Nzcw | 303 | allow to move files across file systems | {'avatar_url': 'https://avatars.githubusercontent.com/u/42851186?v=4', 'events_url': 'https://api.github.com/users/lhoestq/events{/privacy}', 'followers_url': 'https://api.github.com/users/lhoestq/followers', 'following_url': 'https://api.github.com/users/lhoestq/following{/other_user}', 'gists_url': 'https://api.github.com/users/lhoestq/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/lhoestq', 'id': 42851186, 'login': 'lhoestq', 'node_id': 'MDQ6VXNlcjQyODUxMTg2', 'organizations_url': 'https://api.github.com/users/lhoestq/orgs', 'received_events_url': 'https://api.github.com/users/lhoestq/received_events', 'repos_url': 'https://api.github.com/users/lhoestq/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/lhoestq/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/lhoestq/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/lhoestq'} | [] | closed | false | null | [] | null | [] | 2020-06-23 14:56:08+00:00 | 2020-06-23 15:08:44+00:00 | 2020-06-23 15:08:43+00:00 | MEMBER | null | Users are allowed to use the `cache_dir` that they want.
Therefore it can happen that we try to move files across filesystems.
We were using `os.rename` that doesn't allow that, so I changed some of them to `shutil.move`.
This should fix #301 | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/303/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/303/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/303.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/303', 'merged_at': '2020-06-23T15:08:43Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/303.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/303'} | true |
https://api.github.com/repos/huggingface/datasets/issues/302 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/302/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/302/comments | https://api.github.com/repos/huggingface/datasets/issues/302/events | https://github.com/huggingface/datasets/issues/302 | 643,910,418 | MDU6SXNzdWU2NDM5MTA0MTg= | 302 | Question - Sign Language Datasets | {'avatar_url': 'https://avatars.githubusercontent.com/u/5757359?v=4', 'events_url': 'https://api.github.com/users/AmitMY/events{/privacy}', 'followers_url': 'https://api.github.com/users/AmitMY/followers', 'following_url': 'https://api.github.com/users/AmitMY/following{/other_user}', 'gists_url': 'https://api.github.com/users/AmitMY/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/AmitMY', 'id': 5757359, 'login': 'AmitMY', 'node_id': 'MDQ6VXNlcjU3NTczNTk=', 'organizations_url': 'https://api.github.com/users/AmitMY/orgs', 'received_events_url': 'https://api.github.com/users/AmitMY/received_events', 'repos_url': 'https://api.github.com/users/AmitMY/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/AmitMY/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/AmitMY/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/AmitMY'} | [{'color': 'a2eeef', 'default': True, 'description': 'New feature or request', 'id': 1935892871, 'name': 'enhancement', 'node_id': 'MDU6TGFiZWwxOTM1ODkyODcx', 'url': 'https://api.github.com/repos/huggingface/datasets/labels/enhancement'}
{'color': 'c5def5', 'default': False, 'description': 'Generic discussion on the library', 'id': 2067400324, 'name': 'generic discussion', 'node_id': 'MDU6TGFiZWwyMDY3NDAwMzI0', 'url': 'https://api.github.com/repos/huggingface/datasets/labels/generic%20discussion'}] | closed | false | null | [] | null | ['Even more complicating - \r\n\r\nAs I see it, datasets can have "addons".\r\nFor example, the WebNLG dataset is a dataset for data-to-text. However, a work of mine and other works enriched this dataset with text plans / underlying text structures. In that case, I see a need to load the dataset "WebNLG" with "plans" addon.\r\n\r\nSame for sign language - if there is a dataset of videos, one addon can be to run OpenPose, another to run ARKit4 pose estimation, and another to run PoseNet, or even just a video embedding addon. (which are expensive to run individually for everyone who wants to use these data)\r\n\r\nThis is something I dabbled with my own implementation to a [research datasets library](https://github.com/AmitMY/meta-scholar/) and I love to get the discussion going on these topics.'
"This is a really cool idea !\r\nThe example for data objects you gave for the RWTH-PHOENIX-Weather 2014 T dataset can totally fit inside the library.\r\n\r\nFor your point about formats like `ilex`, `eaf`, or `srt`, it is possible to use any library in your dataset script.\r\nHowever most user probably won't need these libraries, as most datasets don't need them, and therefore it's unlikely that we will have them in the minimum requirements to use `nlp` (we want to keep it as light-weight as possible). If a user wants to load your dataset and doesn't have the libraries you need, an error is raised asking the user to install them.\r\n\r\nMore generally, we plan to have something like a `requirements.txt` per dataset. This could also be a place for addons as you said. What do you think ?"
"Thanks, Quentin, I think a `requirements.txt` per dataset will be a good thing.\r\nI will work on adding this dataset next week, and once we sort all of the kinks, I'll add more."] | 2020-06-23 14:53:40+00:00 | 2020-11-25 11:25:33+00:00 | 2020-11-25 11:25:33+00:00 | CONTRIBUTOR | null | An emerging field in NLP is SLP - sign language processing.
I was wondering about adding datasets here, specifically because it's shaping up to be large and easily usable.
The metrics for sign language to text translation are the same.
So, what do you think about (me, or others) adding datasets here?
An example dataset would be [RWTH-PHOENIX-Weather 2014 T](https://www-i6.informatik.rwth-aachen.de/~koller/RWTH-PHOENIX-2014-T/)
For every item in the dataset, the data object includes:
1. video_path - path to mp4 file
2. pose_path - a path to `.pose` file with human pose landmarks
3. openpose_path - a path to a `.json` file with human pose landmarks
4. gloss - string
5. text - string
6. video_metadata - height, width, frames, framerate
------
To make it a tad more complicated - what if sign language libraries add requirements to `nlp`? for example, sign language is commonly annotated using `ilex`, `eaf`, or `srt` files, which are all loadable as text, but there is no reason for the dataset to parse that file by itself, if libraries exist to do so. | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/302/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/302/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/301 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/301/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/301/comments | https://api.github.com/repos/huggingface/datasets/issues/301/events | https://github.com/huggingface/datasets/issues/301 | 643,763,525 | MDU6SXNzdWU2NDM3NjM1MjU= | 301 | Setting cache_dir gives error on wikipedia download | {'avatar_url': 'https://avatars.githubusercontent.com/u/33862536?v=4', 'events_url': 'https://api.github.com/users/hallvagi/events{/privacy}', 'followers_url': 'https://api.github.com/users/hallvagi/followers', 'following_url': 'https://api.github.com/users/hallvagi/following{/other_user}', 'gists_url': 'https://api.github.com/users/hallvagi/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/hallvagi', 'id': 33862536, 'login': 'hallvagi', 'node_id': 'MDQ6VXNlcjMzODYyNTM2', 'organizations_url': 'https://api.github.com/users/hallvagi/orgs', 'received_events_url': 'https://api.github.com/users/hallvagi/received_events', 'repos_url': 'https://api.github.com/users/hallvagi/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/hallvagi/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/hallvagi/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/hallvagi'} | [] | closed | false | null | [] | null | ["Whoops didn't mean to close this one.\r\nI did some changes, could you try to run it from the master branch ?"
'Now it works, thanks!'] | 2020-06-23 11:31:44+00:00 | 2020-06-24 07:05:07+00:00 | 2020-06-24 07:05:07+00:00 | NONE | null | First of all thank you for a super handy library! I'd like to download large files to a specific drive so I set `cache_dir=my_path`. This works fine with e.g. imdb and squad. But on wikipedia I get an error:
```
nlp.load_dataset('wikipedia', '20200501.de', split = 'train', cache_dir=my_path)
```
```
OSError Traceback (most recent call last)
<ipython-input-2-23551344d7bc> in <module>
1 import nlp
----> 2 nlp.load_dataset('wikipedia', '20200501.de', split = 'train', cache_dir=path)
~/anaconda3/envs/fastai2/lib/python3.7/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
522 download_mode=download_mode,
523 ignore_verifications=ignore_verifications,
--> 524 save_infos=save_infos,
525 )
526
~/anaconda3/envs/fastai2/lib/python3.7/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
385 with utils.temporary_assignment(self, "_cache_dir", tmp_data_dir):
386 reader = ArrowReader(self._cache_dir, self.info)
--> 387 reader.download_from_hf_gcs(self._cache_dir, self._relative_data_dir(with_version=True))
388 downloaded_info = DatasetInfo.from_directory(self._cache_dir)
389 self.info.update(downloaded_info)
~/anaconda3/envs/fastai2/lib/python3.7/site-packages/nlp/arrow_reader.py in download_from_hf_gcs(self, cache_dir, relative_data_dir)
231 remote_dataset_info = os.path.join(remote_cache_dir, "dataset_info.json")
232 downloaded_dataset_info = cached_path(remote_dataset_info)
--> 233 os.rename(downloaded_dataset_info, os.path.join(cache_dir, "dataset_info.json"))
234 if self._info is not None:
235 self._info.update(self._info.from_directory(cache_dir))
OSError: [Errno 18] Invalid cross-device link: '/home/local/NTU/nn/.cache/huggingface/datasets/025fa4fd4f04aaafc9e939260fbc8f0bb190ce14c61310c8ae1ddd1dcb31f88c.9637f367b6711a79ca478be55fe6989b8aea4941b7ef7adc67b89ff403020947' -> '/data/nn/nlp/wikipedia/20200501.de/1.0.0.incomplete/dataset_info.json'
``` | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/301/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/301/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/300 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/300/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/300/comments | https://api.github.com/repos/huggingface/datasets/issues/300/events | https://github.com/huggingface/datasets/pull/300 | 643,688,304 | MDExOlB1bGxSZXF1ZXN0NDM4NDQ4Mjk1 | 300 | Fix bertscore references | {'avatar_url': 'https://avatars.githubusercontent.com/u/42851186?v=4', 'events_url': 'https://api.github.com/users/lhoestq/events{/privacy}', 'followers_url': 'https://api.github.com/users/lhoestq/followers', 'following_url': 'https://api.github.com/users/lhoestq/following{/other_user}', 'gists_url': 'https://api.github.com/users/lhoestq/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/lhoestq', 'id': 42851186, 'login': 'lhoestq', 'node_id': 'MDQ6VXNlcjQyODUxMTg2', 'organizations_url': 'https://api.github.com/users/lhoestq/orgs', 'received_events_url': 'https://api.github.com/users/lhoestq/received_events', 'repos_url': 'https://api.github.com/users/lhoestq/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/lhoestq/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/lhoestq/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/lhoestq'} | [] | closed | false | null | [] | null | [] | 2020-06-23 09:38:59+00:00 | 2020-06-23 14:47:38+00:00 | 2020-06-23 14:47:37+00:00 | MEMBER | null | I added some type checking for metrics. There was an issue where a metric could interpret a string a a list. A `ValueError` is raised if a string is given instead of a list.
Moreover I added support for both strings and lists of strings for `references` in `bertscore`, as it is the case in the original code.
Both ways work:
```
import nlp
scorer = nlp.load_metric("bertscore")
with open("pred.txt") as p, open("ref.txt") as g:
for lp, lg in zip(p, g):
scorer.add(lp, [lg])
score = scorer.compute(lang="en")
```
```
import nlp
scorer = nlp.load_metric("bertscore")
with open("pred.txt") as p, open("ref.txt") as g:
for lp, lg in zip(p, g):
scorer.add(lp, lg)
score = scorer.compute(lang="en")
```
This should fix #295 and #238 | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/300/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/300/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/300.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/300', 'merged_at': '2020-06-23T14:47:36Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/300.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/300'} | true |
https://api.github.com/repos/huggingface/datasets/issues/299 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/299/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/299/comments | https://api.github.com/repos/huggingface/datasets/issues/299/events | https://github.com/huggingface/datasets/pull/299 | 643,611,557 | MDExOlB1bGxSZXF1ZXN0NDM4Mzg0NDgw | 299 | remove some print in snli file | {'avatar_url': 'https://avatars.githubusercontent.com/u/38249783?v=4', 'events_url': 'https://api.github.com/users/mariamabarham/events{/privacy}', 'followers_url': 'https://api.github.com/users/mariamabarham/followers', 'following_url': 'https://api.github.com/users/mariamabarham/following{/other_user}', 'gists_url': 'https://api.github.com/users/mariamabarham/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/mariamabarham', 'id': 38249783, 'login': 'mariamabarham', 'node_id': 'MDQ6VXNlcjM4MjQ5Nzgz', 'organizations_url': 'https://api.github.com/users/mariamabarham/orgs', 'received_events_url': 'https://api.github.com/users/mariamabarham/received_events', 'repos_url': 'https://api.github.com/users/mariamabarham/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/mariamabarham/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/mariamabarham/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/mariamabarham'} | [] | closed | false | null | [] | null | ['I guess you can just rebase from master to fix the CI'] | 2020-06-23 07:46:06+00:00 | 2020-06-23 08:10:46+00:00 | 2020-06-23 08:10:44+00:00 | CONTRIBUTOR | null | This PR removes unwanted `print` statements in some files such as `snli.py` | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/299/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/299/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/299.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/299', 'merged_at': '2020-06-23T08:10:44Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/299.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/299'} | true |
https://api.github.com/repos/huggingface/datasets/issues/298 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/298/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/298/comments | https://api.github.com/repos/huggingface/datasets/issues/298/events | https://github.com/huggingface/datasets/pull/298 | 643,603,804 | MDExOlB1bGxSZXF1ZXN0NDM4Mzc4MDM4 | 298 | Add searchable datasets | {'avatar_url': 'https://avatars.githubusercontent.com/u/42851186?v=4', 'events_url': 'https://api.github.com/users/lhoestq/events{/privacy}', 'followers_url': 'https://api.github.com/users/lhoestq/followers', 'following_url': 'https://api.github.com/users/lhoestq/following{/other_user}', 'gists_url': 'https://api.github.com/users/lhoestq/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/lhoestq', 'id': 42851186, 'login': 'lhoestq', 'node_id': 'MDQ6VXNlcjQyODUxMTg2', 'organizations_url': 'https://api.github.com/users/lhoestq/orgs', 'received_events_url': 'https://api.github.com/users/lhoestq/received_events', 'repos_url': 'https://api.github.com/users/lhoestq/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/lhoestq/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/lhoestq/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/lhoestq'} | [] | closed | false | null | [] | null | ['Looks very cool! Only looked at it superficially though'
"Alright I think I've checked all your comments, thanks :)\r\n\r\nMoreover I just added a way to serialize faiss indexes.\r\nThis is important because for big datasets the index construction can take some time.\r\n\r\nExamples:\r\n\r\n```python\r\nds = nlp.load_dataset('crime_and_punish', split='train')\r\nds_with_embeddings = ds.map(lambda example: {'embeddings': embed(example['line']}))\r\nds_with_embeddings.add_faiss_index(column='embeddings')\r\n# query\r\nscores, retrieved_examples = ds_with_embeddings.get_nearest_examples('embeddings', embed('my new query'), k=10)\r\n# save index\r\nds_with_embeddings.get_index('embeddings').save('my_index.faiss')\r\n```\r\n\r\n```python\r\nds = nlp.load_dataset('crime_and_punish', split='train')\r\n# load index\r\nfaiss_index = nlp.search.FaissIndex.load('my_index.faiss')\r\nds.add_faiss_index('embeddings', faiss_index=faiss_index)\r\n# query\r\nscores, retrieved_examples = ds.get_nearest_examples('embeddings', embed('my new query'), k=10)\r\n```\r\n\r\nLet me know what you think"
"Nice!\r\n\r\nHere are a few comments:\r\n\r\nI think it would be good to separate (1) the name of the column we use for indexing and (2) the name of the index itself, at least in our head. As I understand it, once the index is created, the column we used to create it is irrelevant so the column name will only be relevant in the `add_faiss_index` and we should be able to supply a different index name, e.g. `my_faiss_index`. When we reload an index, we don't really care about the column that was used to create it, right? so it's maybe better to have an `index_name` (which default to the column name for a simple user experience but it can also be something else and this should be clear in our head when we define the API).\r\n\r\nI'm wondering if we should not have a triple of methods for each retrieval engine: `add_xxx_index`, `save_xxx_index` and `load_xxx_index` when `xxx` can be `faiss` or `elasticsearch`. I'm not a fan of exposing `nlp.search.FaissIndex` unless you think there is a strong reason to have the user learn this abstraction.\r\n\r\nLast but not least, I think we should already think about hosting index on our S3. I would maybe go for something like this: host the index serialized with the cached dataset on user-provided namespaces:\r\n```python\r\nwiki_indexed = load_dataset('thom/wiki_indexed_with_dpr_faiss')\r\n```"
'I agree, I just changed to using `index_name` and having add/save/load methods'
"To summarize:\r\n\r\n\r\n```python\r\nds = nlp.load_dataset('crime_and_punish', split='train')\r\nds_with_embeddings = ds.map(lambda example: {'embeddings': embed(example['line']}))\r\nds_with_embeddings.add_faiss_index(column='embeddings')\r\n# query\r\nscores, retrieved_examples = ds_with_embeddings.get_nearest_examples('embeddings', embed('my new query'), k=10)\r\n# save index\r\nds_with_embeddings.save_faiss_index('embeddings', 'my_index.faiss')\r\n```\r\n\r\n```python\r\nds = nlp.load_dataset('crime_and_punish', split='train')\r\n# load index\r\nds.load_faiss_index('embeddings', 'my_index.faiss')\r\n# query\r\nscores, retrieved_examples = ds.get_nearest_examples('embeddings', embed('my new query'), k=10)\r\n```"
'Good to me. I understand that for now there is no check that the index matches the dataset on loading.\r\nMaybe just add a basic test on the number of examples?'
'Ok I think this one is ready now'
'Looks like the CI is having troubles to pass because of `tests/test_dataset_common.py::AWSDatasetTest::test_builder_configs_{<insert_rando_dataset_name_here>}`, `requests.exceptions.ConnectionError` :/'] | 2020-06-23 07:33:03+00:00 | 2020-06-26 07:50:44+00:00 | 2020-06-26 07:50:43+00:00 | MEMBER | null | # Better support for Numpy format + Add Indexed Datasets
I was working on adding Indexed Datasets but in the meantime I had to also add more support for Numpy arrays in the lib.
## Better support for Numpy format
New features:
- New fast method to convert Numpy arrays from Arrow structure (up to x100 speed up) using Pandas.
- Allow to output Numpy arrays in batched `.map`, which was the only missing part to fully support Numpy arrays.
Pandas offers fast zero-copy Numpy arrays conversion from Arrow structures.
Using it we can speed up the reading of memory-mapped Numpy array stored in Arrow format.
With these changes you can easily compute embeddings of texts using `.map()`. For example:
```python
def embed(text):
tokenized_example = tokenizer.encode(text, return_tensors="pt")
embeddings = bert_encoder(tokenized_examples).numpy()
return embeddings
dset_with_embeddings = dset.map(lambda example: {"embeddings": embed(example["text])})
```
And then reading the embeddings from the arrow format is be very fast.
PS1: Note that right now only 1d arrays are supported.
PS2: It seems possible to do without pandas but it will require more _trickery_.
PS3: I did a simple benchmark with google colab that you can view here:
https://colab.research.google.com/drive/1QlLTR6LRwYOKGJ-hTHmHyolE3wJzvfFg?usp=sharing
## Add Indexed Datasets
For many retrieval tasks it is convenient to index a dataset to be able to run fast queries.
For example for models like DPR, REALM, RAG etc. that are models for Open Domain QA, the retrieval step is very important.
Therefore I added two ways to add an index to a column of a dataset:
1) You can index it using a Dense Index like Faiss. It is used to index vectors.
Faiss is a library for efficient similarity search and clustering of dense vectors.
It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM.
2) You can index it using a Sparse Index like Elasticsearch. It is used to index text and run queries based on BM25 similarity.
Example of usage:
```python
ds = nlp.load_dataset('crime_and_punish', split='train')
ds_with_embeddings = ds.map(lambda example: {'embeddings': embed(example['line']})) # `embed` outputs a `np.array`
ds_with_embeddings.add_vector_index(column='embeddings')
scores, retrieved_examples = ds_with_embeddings.get_nearest(column='embeddings', query=embed('my new query'), k=10)
```
```python
ds = nlp.load_dataset('crime_and_punish', split='train')
es_client = elasticsearch.Elasticsearch()
ds.add_text_index(column='line', es_client=es_client, index_name="my_es_index")
scores, retrieved_examples = ds.get_nearest(column='line', query='my new query', k=10)
```
PS4: Faiss allows to specify many options for the [index](https://github.com/facebookresearch/faiss/wiki/The-index-factory) and for [GPU settings](https://github.com/facebookresearch/faiss/wiki/Faiss-on-the-GPU). I made sure that the user has full control over those settings.
## Tests
I added tests for Faiss, Elasticsearch and indexed datasets.
I had to edit the CI config because all the test scripts were not being run by CircleCI.
------------------
I'd be really happy to have some feedbacks :) | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 1, 'total_count': 1, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/298/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/298/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/298.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/298', 'merged_at': '2020-06-26T07:50:43Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/298.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/298'} | true |
https://api.github.com/repos/huggingface/datasets/issues/297 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/297/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/297/comments | https://api.github.com/repos/huggingface/datasets/issues/297/events | https://github.com/huggingface/datasets/issues/297 | 643,444,625 | MDU6SXNzdWU2NDM0NDQ2MjU= | 297 | Error in Demo for Specific Datasets | {'avatar_url': 'https://avatars.githubusercontent.com/u/60150701?v=4', 'events_url': 'https://api.github.com/users/s-jse/events{/privacy}', 'followers_url': 'https://api.github.com/users/s-jse/followers', 'following_url': 'https://api.github.com/users/s-jse/following{/other_user}', 'gists_url': 'https://api.github.com/users/s-jse/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/s-jse', 'id': 60150701, 'login': 's-jse', 'node_id': 'MDQ6VXNlcjYwMTUwNzAx', 'organizations_url': 'https://api.github.com/users/s-jse/orgs', 'received_events_url': 'https://api.github.com/users/s-jse/received_events', 'repos_url': 'https://api.github.com/users/s-jse/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/s-jse/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/s-jse/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/s-jse'} | [{'color': '94203D', 'default': False, 'description': '', 'id': 2107841032, 'name': 'nlp-viewer', 'node_id': 'MDU6TGFiZWwyMTA3ODQxMDMy', 'url': 'https://api.github.com/repos/huggingface/datasets/labels/nlp-viewer'}] | closed | false | null | [] | null | ["Thanks for reporting these errors :)\r\n\r\nI can actually see two issues here.\r\n\r\nFirst, datasets like `natural_questions` require apache_beam to be processed. Right now the import is not at the right place so we have this error message. However, even the imports are fixed, the nlp viewer doesn't actually have the resources to process NQ right now so we'll have to wait until we have a version that we've already processed on our google storage (that's what we've done for wikipedia for example).\r\n\r\nSecond, datasets like `newsroom` require manual downloads as we're not allowed to redistribute the data ourselves (if I'm not wrong). An error message should be displayed saying that we're not allowed to show the dataset.\r\n\r\nI can fix the first issue with the imports but for the second one I think we'll have to see with @srush to show a message for datasets that require manual downloads (it can be checked whether a dataset requires manual downloads if `dataset_builder_instance.manual_download_instructions is not None`).\r\n\r\n"
'I added apache-beam to the viewer. We can think about how to add newsroom. '
"We don't plan to host the source files of newsroom ourselves for now.\r\nYou can still get the dataset if you follow the download instructions given by `dataset = load_dataset('newsroom')` though.\r\nThe viewer also shows the instructions now.\r\n\r\nClosing this one. If you have other questions, feel free to re-open :)"] | 2020-06-23 00:38:42+00:00 | 2020-07-17 17:43:06+00:00 | 2020-07-17 17:43:06+00:00 | NONE | null | Selecting `natural_questions` or `newsroom` dataset in the online demo results in an error similar to the following.
![image](https://user-images.githubusercontent.com/60150701/85347842-ac861900-b4ae-11ea-98c4-a53a00934783.png)
| {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/297/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/297/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/296 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/296/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/296/comments | https://api.github.com/repos/huggingface/datasets/issues/296/events | https://github.com/huggingface/datasets/issues/296 | 643,423,717 | MDU6SXNzdWU2NDM0MjM3MTc= | 296 | snli -1 labels | {'avatar_url': 'https://avatars.githubusercontent.com/u/13238952?v=4', 'events_url': 'https://api.github.com/users/jxmorris12/events{/privacy}', 'followers_url': 'https://api.github.com/users/jxmorris12/followers', 'following_url': 'https://api.github.com/users/jxmorris12/following{/other_user}', 'gists_url': 'https://api.github.com/users/jxmorris12/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/jxmorris12', 'id': 13238952, 'login': 'jxmorris12', 'node_id': 'MDQ6VXNlcjEzMjM4OTUy', 'organizations_url': 'https://api.github.com/users/jxmorris12/orgs', 'received_events_url': 'https://api.github.com/users/jxmorris12/received_events', 'repos_url': 'https://api.github.com/users/jxmorris12/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/jxmorris12/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/jxmorris12/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/jxmorris12'} | [] | closed | false | null | [] | null | ['@jxmorris12 , we use `-1` to label examples for which `gold label` is missing (`gold label = -` in the original dataset). '
'Thanks @mariamabarham! so the original dataset is missing some labels? That is weird. Is standard practice just to discard those examples training/eval?'
"Yes the original dataset is missing some labels maybe @sleepinyourhat , @gangeli can correct me if I'm wrong \r\nFor my personal opinion at least if you want your model to learn to predict no answer (-1) you can leave it their but otherwise you can discard them. "
'thanks @mariamabarham :)'] | 2020-06-22 23:33:30+00:00 | 2020-06-23 14:41:59+00:00 | 2020-06-23 14:41:58+00:00 | CONTRIBUTOR | null | I'm trying to train a model on the SNLI dataset. Why does it have so many -1 labels?
```
import nlp
from collections import Counter
data = nlp.load_dataset('snli')['train']
print(Counter(data['label']))
Counter({0: 183416, 2: 183187, 1: 182764, -1: 785})
```
| {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/296/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/296/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/295 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/295/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/295/comments | https://api.github.com/repos/huggingface/datasets/issues/295/events | https://github.com/huggingface/datasets/issues/295 | 643,245,412 | MDU6SXNzdWU2NDMyNDU0MTI= | 295 | Improve input warning for evaluation metrics | {'avatar_url': 'https://avatars.githubusercontent.com/u/19514537?v=4', 'events_url': 'https://api.github.com/users/Tiiiger/events{/privacy}', 'followers_url': 'https://api.github.com/users/Tiiiger/followers', 'following_url': 'https://api.github.com/users/Tiiiger/following{/other_user}', 'gists_url': 'https://api.github.com/users/Tiiiger/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/Tiiiger', 'id': 19514537, 'login': 'Tiiiger', 'node_id': 'MDQ6VXNlcjE5NTE0NTM3', 'organizations_url': 'https://api.github.com/users/Tiiiger/orgs', 'received_events_url': 'https://api.github.com/users/Tiiiger/received_events', 'repos_url': 'https://api.github.com/users/Tiiiger/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/Tiiiger/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/Tiiiger/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/Tiiiger'} | [] | closed | false | null | [] | null | [] | 2020-06-22 17:28:57+00:00 | 2020-06-23 14:47:37+00:00 | 2020-06-23 14:47:37+00:00 | NONE | null | Hi,
I am the author of `bert_score`. Recently, we received [ an issue ](https://github.com/Tiiiger/bert_score/issues/62) reporting a problem in using `bert_score` from the `nlp` package (also see #238 in this repo). After looking into this, I realized that the problem arises from the format `nlp.Metric` takes input.
Here is a minimal example:
```python
import nlp
scorer = nlp.load_metric("bertscore")
with open("pred.txt") as p, open("ref.txt") as g:
for lp, lg in zip(p, g):
scorer.add(lp, lg)
score = scorer.compute(lang="en")
```
The problem in the above code is that `scorer.add()` expects a list of strings as input for the references. As a result, the `scorer` here would take a list of characters in `lg` to be the references. The correct implementation would be calling
```python
scorer.add(lp, [lg])
```
I just want to raise this issue to you to prevent future user errors of a similar kind. I assume some simple type checking can prevent this from happening?
Thanks! | {'+1': 2, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 2, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/295/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/295/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/294 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/294/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/294/comments | https://api.github.com/repos/huggingface/datasets/issues/294/events | https://github.com/huggingface/datasets/issues/294 | 643,181,179 | MDU6SXNzdWU2NDMxODExNzk= | 294 | Cannot load arxiv dataset on MacOS? | {'avatar_url': 'https://avatars.githubusercontent.com/u/8917831?v=4', 'events_url': 'https://api.github.com/users/JohnGiorgi/events{/privacy}', 'followers_url': 'https://api.github.com/users/JohnGiorgi/followers', 'following_url': 'https://api.github.com/users/JohnGiorgi/following{/other_user}', 'gists_url': 'https://api.github.com/users/JohnGiorgi/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/JohnGiorgi', 'id': 8917831, 'login': 'JohnGiorgi', 'node_id': 'MDQ6VXNlcjg5MTc4MzE=', 'organizations_url': 'https://api.github.com/users/JohnGiorgi/orgs', 'received_events_url': 'https://api.github.com/users/JohnGiorgi/received_events', 'repos_url': 'https://api.github.com/users/JohnGiorgi/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/JohnGiorgi/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/JohnGiorgi/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/JohnGiorgi'} | [{'color': '2edb81', 'default': False, 'description': 'A bug in a dataset script provided in the library', 'id': 2067388877, 'name': 'dataset bug', 'node_id': 'MDU6TGFiZWwyMDY3Mzg4ODc3', 'url': 'https://api.github.com/repos/huggingface/datasets/labels/dataset%20bug'}] | closed | false | null | [] | null | ["I couldn't replicate this issue on my macbook :/\r\nCould you try to play with different encodings in `with open(path, encoding=...) as f` in scientific_papers.py:L108 ?"
'I was able to track down the file causing the problem by adding the following to `scientific_papers.py` (starting at line 116):\r\n\r\n```python\r\n from json import JSONDecodeError\r\n try:\r\n d = json.loads(line)\r\n summary = "\\n".join(d["abstract_text"])\r\n except JSONDecodeError:\r\n print(path, line)\r\n```\r\n\r\n\r\n\r\nFor me it was at: `/Users/johngiorgi/.cache/huggingface/datasets/f87fd498c5003cbe253a2af422caa1e58f87a4fd74cb3e67350c635c8903b259/arxiv-dataset/train.txt` with `"article_id": "1407.3051"`.\r\n\r\nNot really 100% sure at the moment, but it looks like this specific substring from `"article_text"` may be causing the problem?\r\n\r\n```\r\n"after the missing - mass scale adjustment , the validity of the corrections was tested in the @xmath85 productions at 1.69 gev/@xmath1 . in fig . [", "fig : calibrations ] ( a ) , we show the missing - mass spectrum in the @xmath86 region in the @xmath87 reaction at 1.69 gev/@xmath1 . a fitting result with a lorentzian function for the @xmath86 ( dashed line ) and the three - body phas\r\n```\r\n\r\nperhaps because it appears to be truncated. I (think) I can recreate the problem by doing the following:\r\n\r\n```python\r\nimport json\r\n\r\n# A minimal example of the json file that causes the error\r\ninvalid_json = \'{"article_id": "1407.3051", "article_text": ["the missing - mass resolution was obtained to be 2.8 @xmath3 0.1 mev/@xmath4 ( fwhm ) , which corresponds to the missing - mass resolution of 3.2 @xmath3 0.2 mev/@xmath4 ( fwhm ) at the @xmath6 cusp region in the @xmath0 reaction .", "this resolution is at least by a factor of 2 better than the previous measurement with the same reaction ( 3.2@xmath595.5 mev/@xmath4 in @xmath84 ) @xcite .", "after the missing - mass scale adjustment , the validity of the corrections was tested in the @xmath85 productions at 1.69 gev/@xmath1 . in fig . [", "fig : calibrations ] ( a ) , we show the missing - mass spectrum in the @xmath86 region in the @xmath87 reaction at 1.69 gev/@xmath1 . a fitting result with a lorentzian function for the @xmath86 ( dashed line ) and the three - body phas\' \r\n# The line of code from `scientific_papers.py` which appears to cause the error\r\njson.loads(invalid_json)\r\n```\r\n\r\nThis is as far as I get before I am stumped.'
"I just checked inside `train.txt` and this line isn't truncated for me (line 163577).\r\nCould you try to clear your cache and re-download the dataset ?"
'Ah the turn-it-off-turn-it-on again solution! That did it, thanks a lot :) '] | 2020-06-22 15:46:55+00:00 | 2020-06-30 15:25:10+00:00 | 2020-06-30 15:25:10+00:00 | CONTRIBUTOR | null | I am having trouble loading the `"arxiv"` config from the `"scientific_papers"` dataset on MacOS. When I try loading the dataset with:
```python
arxiv = nlp.load_dataset("scientific_papers", "arxiv")
```
I get the following stack trace:
```bash
JSONDecodeError Traceback (most recent call last)
<ipython-input-2-8e00c55d5a59> in <module>
----> 1 arxiv = nlp.load_dataset("scientific_papers", "arxiv")
~/miniconda3/envs/t2t/lib/python3.7/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
522 download_mode=download_mode,
523 ignore_verifications=ignore_verifications,
--> 524 save_infos=save_infos,
525 )
526
~/miniconda3/envs/t2t/lib/python3.7/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
430 verify_infos = not save_infos and not ignore_verifications
431 self._download_and_prepare(
--> 432 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
433 )
434 # Sync info
~/miniconda3/envs/t2t/lib/python3.7/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
481 try:
482 # Prepare split will record examples associated to the split
--> 483 self._prepare_split(split_generator, **prepare_split_kwargs)
484 except OSError:
485 raise OSError("Cannot find data file. " + (self.manual_download_instructions or ""))
~/miniconda3/envs/t2t/lib/python3.7/site-packages/nlp/builder.py in _prepare_split(self, split_generator)
662
663 generator = self._generate_examples(**split_generator.gen_kwargs)
--> 664 for key, record in utils.tqdm(generator, unit=" examples", total=split_info.num_examples, leave=False):
665 example = self.info.features.encode_example(record)
666 writer.write(example)
~/miniconda3/envs/t2t/lib/python3.7/site-packages/tqdm/std.py in __iter__(self)
1106 fp_write=getattr(self.fp, 'write', sys.stderr.write))
1107
-> 1108 for obj in iterable:
1109 yield obj
1110 # Update and possibly print the progressbar.
~/miniconda3/envs/t2t/lib/python3.7/site-packages/nlp/datasets/scientific_papers/107a416c0e1958cb846f5934b5aae292f7884a5b27e86af3f3ef1a093e058bbc/scientific_papers.py in _generate_examples(self, path)
114 # "section_names": list[str], list of section names.
115 # "sections": list[list[str]], list of sections (list of paragraphs)
--> 116 d = json.loads(line)
117 summary = "\n".join(d["abstract_text"])
118 # In original paper, <S> and </S> are not used in vocab during training
~/miniconda3/envs/t2t/lib/python3.7/json/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
346 parse_int is None and parse_float is None and
347 parse_constant is None and object_pairs_hook is None and not kw):
--> 348 return _default_decoder.decode(s)
349 if cls is None:
350 cls = JSONDecoder
~/miniconda3/envs/t2t/lib/python3.7/json/decoder.py in decode(self, s, _w)
335
336 """
--> 337 obj, end = self.raw_decode(s, idx=_w(s, 0).end())
338 end = _w(s, end).end()
339 if end != len(s):
~/miniconda3/envs/t2t/lib/python3.7/json/decoder.py in raw_decode(self, s, idx)
351 """
352 try:
--> 353 obj, end = self.scan_once(s, idx)
354 except StopIteration as err:
355 raise JSONDecodeError("Expecting value", s, err.value) from None
JSONDecodeError: Unterminated string starting at: line 1 column 46983 (char 46982)
163502 examples [02:10, 2710.68 examples/s]
```
I am not sure how to trace back to the specific JSON file that has the "Unterminated string". Also, I do not get this error on colab so I suspect it may be MacOS specific. Copy pasting the relevant lines from `transformers-cli env` below:
- Platform: Darwin-19.5.0-x86_64-i386-64bit
- Python version: 3.7.5
- PyTorch version (GPU?): 1.5.0 (False)
- Tensorflow version (GPU?): 2.2.0 (False)
Any ideas? | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/294/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/294/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/293 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/293/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/293/comments | https://api.github.com/repos/huggingface/datasets/issues/293/events | https://github.com/huggingface/datasets/pull/293 | 642,942,182 | MDExOlB1bGxSZXF1ZXN0NDM3ODM1ODI4 | 293 | Don't test community datasets | {'avatar_url': 'https://avatars.githubusercontent.com/u/42851186?v=4', 'events_url': 'https://api.github.com/users/lhoestq/events{/privacy}', 'followers_url': 'https://api.github.com/users/lhoestq/followers', 'following_url': 'https://api.github.com/users/lhoestq/following{/other_user}', 'gists_url': 'https://api.github.com/users/lhoestq/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/lhoestq', 'id': 42851186, 'login': 'lhoestq', 'node_id': 'MDQ6VXNlcjQyODUxMTg2', 'organizations_url': 'https://api.github.com/users/lhoestq/orgs', 'received_events_url': 'https://api.github.com/users/lhoestq/received_events', 'repos_url': 'https://api.github.com/users/lhoestq/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/lhoestq/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/lhoestq/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/lhoestq'} | [] | closed | false | null | [] | null | [] | 2020-06-22 10:15:33+00:00 | 2020-06-22 11:07:00+00:00 | 2020-06-22 11:06:59+00:00 | MEMBER | null | This PR disables testing for community datasets on aws.
It should fix the CI that is currently failing. | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/293/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/293/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/293.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/293', 'merged_at': '2020-06-22T11:06:59Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/293.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/293'} | true |
https://api.github.com/repos/huggingface/datasets/issues/292 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/292/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/292/comments | https://api.github.com/repos/huggingface/datasets/issues/292/events | https://github.com/huggingface/datasets/pull/292 | 642,897,797 | MDExOlB1bGxSZXF1ZXN0NDM3Nzk4NTM2 | 292 | Update metadata for x_stance dataset | {'avatar_url': 'https://avatars.githubusercontent.com/u/5830820?v=4', 'events_url': 'https://api.github.com/users/jvamvas/events{/privacy}', 'followers_url': 'https://api.github.com/users/jvamvas/followers', 'following_url': 'https://api.github.com/users/jvamvas/following{/other_user}', 'gists_url': 'https://api.github.com/users/jvamvas/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/jvamvas', 'id': 5830820, 'login': 'jvamvas', 'node_id': 'MDQ6VXNlcjU4MzA4MjA=', 'organizations_url': 'https://api.github.com/users/jvamvas/orgs', 'received_events_url': 'https://api.github.com/users/jvamvas/received_events', 'repos_url': 'https://api.github.com/users/jvamvas/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/jvamvas/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/jvamvas/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/jvamvas'} | [] | closed | false | null | [] | null | ['Great! Thanks @jvamvas for these updates.\r\n'
'I have fixed a warning. The remaining test failure is due to an unrelated dataset.'
'We just fixed the other dataset on master. Could you rebase from master and push to rerun the CI ?'] | 2020-06-22 09:13:26+00:00 | 2020-06-23 08:07:24+00:00 | 2020-06-23 08:07:24+00:00 | CONTRIBUTOR | null | Thank you for featuring the x_stance dataset in your library. This PR updates some metadata:
- Citation: Replace preprint with proceedings
- URL: Use a URL with long-term availability
| {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/292/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/292/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/292.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/292', 'merged_at': '2020-06-23T08:07:24Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/292.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/292'} | true |
https://api.github.com/repos/huggingface/datasets/issues/291 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/291/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/291/comments | https://api.github.com/repos/huggingface/datasets/issues/291/events | https://github.com/huggingface/datasets/pull/291 | 642,688,450 | MDExOlB1bGxSZXF1ZXN0NDM3NjM1NjMy | 291 | break statement not required | {'avatar_url': 'https://avatars.githubusercontent.com/u/12967587?v=4', 'events_url': 'https://api.github.com/users/mayurnewase/events{/privacy}', 'followers_url': 'https://api.github.com/users/mayurnewase/followers', 'following_url': 'https://api.github.com/users/mayurnewase/following{/other_user}', 'gists_url': 'https://api.github.com/users/mayurnewase/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/mayurnewase', 'id': 12967587, 'login': 'mayurnewase', 'node_id': 'MDQ6VXNlcjEyOTY3NTg3', 'organizations_url': 'https://api.github.com/users/mayurnewase/orgs', 'received_events_url': 'https://api.github.com/users/mayurnewase/received_events', 'repos_url': 'https://api.github.com/users/mayurnewase/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/mayurnewase/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/mayurnewase/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/mayurnewase'} | [] | closed | false | null | [] | null | ['I guess,test failing due to connection error?'
'We just fixed the other dataset on master. Could you rebase from master and push to rerun the CI ?'
"If I'm not wrong this function returns None if no main class was found.\r\nI think it makes things less clear not to have a return at the end of the function.\r\nI guess we can have one return in the for loop instead of the break statement, AND one return at the end to explicitly return None.\r\nWhat do you think ?"] | 2020-06-22 01:40:55+00:00 | 2020-06-23 17:57:58+00:00 | 2020-06-23 09:37:02+00:00 | NONE | null | null | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/291/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/291/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/291.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/291', 'merged_at': None, 'patch_url': 'https://github.com/huggingface/datasets/pull/291.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/291'} | true |
https://api.github.com/repos/huggingface/datasets/issues/290 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/290/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/290/comments | https://api.github.com/repos/huggingface/datasets/issues/290/events | https://github.com/huggingface/datasets/issues/290 | 641,978,286 | MDU6SXNzdWU2NDE5NzgyODY= | 290 | ConnectionError - Eli5 dataset download | {'avatar_url': 'https://avatars.githubusercontent.com/u/8490096?v=4', 'events_url': 'https://api.github.com/users/JovanNj/events{/privacy}', 'followers_url': 'https://api.github.com/users/JovanNj/followers', 'following_url': 'https://api.github.com/users/JovanNj/following{/other_user}', 'gists_url': 'https://api.github.com/users/JovanNj/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/JovanNj', 'id': 8490096, 'login': 'JovanNj', 'node_id': 'MDQ6VXNlcjg0OTAwOTY=', 'organizations_url': 'https://api.github.com/users/JovanNj/orgs', 'received_events_url': 'https://api.github.com/users/JovanNj/received_events', 'repos_url': 'https://api.github.com/users/JovanNj/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/JovanNj/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/JovanNj/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/JovanNj'} | [] | closed | false | null | [] | null | ["It should ne fixed now, thanks for reporting this one :)\r\nIt was an issue on our google storage.\r\n\r\nLet me now if you're still facing this issue."
'It works now, thanks for prompt help!'] | 2020-06-19 13:40:33+00:00 | 2020-06-20 13:22:24+00:00 | 2020-06-20 13:22:24+00:00 | NONE | null | Hi, I have a problem with downloading Eli5 dataset. When typing `nlp.load_dataset('eli5')`, I get ConnectionError: Couldn't reach https://storage.googleapis.com/huggingface-nlp/cache/datasets/eli5/LFQA_reddit/1.0.0/explain_like_im_five-train_eli5.arrow
I would appreciate if you could help me with this issue. | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/290/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/290/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/289 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/289/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/289/comments | https://api.github.com/repos/huggingface/datasets/issues/289/events | https://github.com/huggingface/datasets/pull/289 | 641,934,194 | MDExOlB1bGxSZXF1ZXN0NDM3MDc0MTM3 | 289 | update xsum | {'avatar_url': 'https://avatars.githubusercontent.com/u/38249783?v=4', 'events_url': 'https://api.github.com/users/mariamabarham/events{/privacy}', 'followers_url': 'https://api.github.com/users/mariamabarham/followers', 'following_url': 'https://api.github.com/users/mariamabarham/following{/other_user}', 'gists_url': 'https://api.github.com/users/mariamabarham/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/mariamabarham', 'id': 38249783, 'login': 'mariamabarham', 'node_id': 'MDQ6VXNlcjM4MjQ5Nzgz', 'organizations_url': 'https://api.github.com/users/mariamabarham/orgs', 'received_events_url': 'https://api.github.com/users/mariamabarham/received_events', 'repos_url': 'https://api.github.com/users/mariamabarham/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/mariamabarham/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/mariamabarham/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/mariamabarham'} | [] | closed | false | null | [] | null | ['Looks cool!\r\n@mariamabarham can you add a detailed description here what exactly is changed and how the user can load xsum now?'
'And a rebase should solve the conflicts'
'This is a super useful PR :-) @sshleifer - maybe you can take a look at the updated version of xsum if you can use it for your use case. Now, one should be able to just load it with:\r\n\r\n```python \r\nnlp.load_datasets("xsum", ....) # no manual dir required anymore\r\n```\r\n'] | 2020-06-19 12:28:32+00:00 | 2020-06-22 13:27:26+00:00 | 2020-06-22 07:20:07+00:00 | CONTRIBUTOR | null | This PR makes the following update to the xsum dataset:
- Manual download is not required anymore
- dataset can be loaded as follow: `nlp.load_dataset('xsum')`
**Important**
Instead of using on outdated url to download the data: "https://raw.githubusercontent.com/EdinburghNLP/XSum/master/XSum-Dataset/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json"
a more up-to-date url stored here: https://s3.amazonaws.com/datasets.huggingface.co/summarization/xsum.tar.gz is used
, so that the user does not need to manually download the data anymore.
There might be slight breaking changes here for xsum. | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/289/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/289/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/289.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/289', 'merged_at': '2020-06-22T07:20:07Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/289.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/289'} | true |
https://api.github.com/repos/huggingface/datasets/issues/288 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/288/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/288/comments | https://api.github.com/repos/huggingface/datasets/issues/288/events | https://github.com/huggingface/datasets/issues/288 | 641,888,610 | MDU6SXNzdWU2NDE4ODg2MTA= | 288 | Error at the first example in README: AttributeError: module 'dill' has no attribute '_dill' | {'avatar_url': 'https://avatars.githubusercontent.com/u/14964542?v=4', 'events_url': 'https://api.github.com/users/wutong8023/events{/privacy}', 'followers_url': 'https://api.github.com/users/wutong8023/followers', 'following_url': 'https://api.github.com/users/wutong8023/following{/other_user}', 'gists_url': 'https://api.github.com/users/wutong8023/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/wutong8023', 'id': 14964542, 'login': 'wutong8023', 'node_id': 'MDQ6VXNlcjE0OTY0NTQy', 'organizations_url': 'https://api.github.com/users/wutong8023/orgs', 'received_events_url': 'https://api.github.com/users/wutong8023/received_events', 'repos_url': 'https://api.github.com/users/wutong8023/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/wutong8023/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/wutong8023/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/wutong8023'} | [] | closed | false | null | [] | null | ['It looks like the bug comes from `dill`. Which version of `dill` are you using ?'
'Thank you. It is version 0.2.6, which version is better?'
'0.2.6 is three years old now, maybe try a more recent one, e.g. the current 0.3.2 if you can?'
'Thanks guys! I upgraded dill and it works.' 'Awesome'] | 2020-06-19 11:01:22+00:00 | 2020-06-21 09:05:11+00:00 | 2020-06-21 09:05:11+00:00 | NONE | null | /Users/parasol_tree/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:469: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint8 = np.dtype([("qint8", np.int8, 1)])
/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:470: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:471: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint16 = np.dtype([("qint16", np.int16, 1)])
/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:472: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:473: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint32 = np.dtype([("qint32", np.int32, 1)])
/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:476: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
np_resource = np.dtype([("resource", np.ubyte, 1)])
/Users/parasol_tree/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6
return f(*args, **kwds)
/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
from ._conv import register_converters as _register_converters
Traceback (most recent call last):
File "/Users/parasol_tree/Resource/019 - Github/AcademicEnglishToolkit /test.py", line 7, in <module>
import nlp
File "/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/nlp/__init__.py", line 27, in <module>
from .arrow_dataset import Dataset
File "/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/nlp/arrow_dataset.py", line 31, in <module>
from nlp.utils.py_utils import dumps
File "/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/nlp/utils/__init__.py", line 20, in <module>
from .download_manager import DownloadManager, GenerateMode
File "/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/nlp/utils/download_manager.py", line 25, in <module>
from .py_utils import flatten_nested, map_nested, size_str
File "/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/nlp/utils/py_utils.py", line 244, in <module>
class Pickler(dill.Pickler):
File "/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/nlp/utils/py_utils.py", line 247, in Pickler
dispatch = dill._dill.MetaCatchingDict(dill.Pickler.dispatch.copy())
AttributeError: module 'dill' has no attribute '_dill' | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/288/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/288/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/287 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/287/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/287/comments | https://api.github.com/repos/huggingface/datasets/issues/287/events | https://github.com/huggingface/datasets/pull/287 | 641,800,227 | MDExOlB1bGxSZXF1ZXN0NDM2OTY0NTg0 | 287 | fix squad_v2 metric | {'avatar_url': 'https://avatars.githubusercontent.com/u/42851186?v=4', 'events_url': 'https://api.github.com/users/lhoestq/events{/privacy}', 'followers_url': 'https://api.github.com/users/lhoestq/followers', 'following_url': 'https://api.github.com/users/lhoestq/following{/other_user}', 'gists_url': 'https://api.github.com/users/lhoestq/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/lhoestq', 'id': 42851186, 'login': 'lhoestq', 'node_id': 'MDQ6VXNlcjQyODUxMTg2', 'organizations_url': 'https://api.github.com/users/lhoestq/orgs', 'received_events_url': 'https://api.github.com/users/lhoestq/received_events', 'repos_url': 'https://api.github.com/users/lhoestq/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/lhoestq/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/lhoestq/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/lhoestq'} | [] | closed | false | null | [] | null | [] | 2020-06-19 08:24:46+00:00 | 2020-06-19 08:33:43+00:00 | 2020-06-19 08:33:41+00:00 | MEMBER | null | Fix #280
The imports were wrong | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/287/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/287/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/287.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/287', 'merged_at': '2020-06-19T08:33:41Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/287.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/287'} | true |
https://api.github.com/repos/huggingface/datasets/issues/286 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/286/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/286/comments | https://api.github.com/repos/huggingface/datasets/issues/286/events | https://github.com/huggingface/datasets/pull/286 | 641,585,758 | MDExOlB1bGxSZXF1ZXN0NDM2NzkzMjI4 | 286 | Add ANLI dataset. | {'avatar_url': 'https://avatars.githubusercontent.com/u/11016329?v=4', 'events_url': 'https://api.github.com/users/easonnie/events{/privacy}', 'followers_url': 'https://api.github.com/users/easonnie/followers', 'following_url': 'https://api.github.com/users/easonnie/following{/other_user}', 'gists_url': 'https://api.github.com/users/easonnie/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/easonnie', 'id': 11016329, 'login': 'easonnie', 'node_id': 'MDQ6VXNlcjExMDE2MzI5', 'organizations_url': 'https://api.github.com/users/easonnie/orgs', 'received_events_url': 'https://api.github.com/users/easonnie/received_events', 'repos_url': 'https://api.github.com/users/easonnie/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/easonnie/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/easonnie/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/easonnie'} | [] | closed | false | null | [] | null | ["Awesome!! Thanks @easonnie.\r\nLet's wait for additional reviews maybe from @lhoestq @patrickvonplaten @jplu"] | 2020-06-18 22:27:30+00:00 | 2020-06-22 12:23:27+00:00 | 2020-06-22 12:23:27+00:00 | CONTRIBUTOR | null | I completed all the steps in https://github.com/huggingface/nlp/blob/master/CONTRIBUTING.md#how-to-add-a-dataset and push the code for ANLI. Please let me know if there are any errors. | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/286/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/286/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/286.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/286', 'merged_at': '2020-06-22T12:23:26Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/286.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/286'} | true |
https://api.github.com/repos/huggingface/datasets/issues/285 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/285/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/285/comments | https://api.github.com/repos/huggingface/datasets/issues/285/events | https://github.com/huggingface/datasets/pull/285 | 641,360,702 | MDExOlB1bGxSZXF1ZXN0NDM2NjAyMjk4 | 285 | Consistent formatting of citations | {'avatar_url': 'https://avatars.githubusercontent.com/u/38249783?v=4', 'events_url': 'https://api.github.com/users/mariamabarham/events{/privacy}', 'followers_url': 'https://api.github.com/users/mariamabarham/followers', 'following_url': 'https://api.github.com/users/mariamabarham/following{/other_user}', 'gists_url': 'https://api.github.com/users/mariamabarham/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/mariamabarham', 'id': 38249783, 'login': 'mariamabarham', 'node_id': 'MDQ6VXNlcjM4MjQ5Nzgz', 'organizations_url': 'https://api.github.com/users/mariamabarham/orgs', 'received_events_url': 'https://api.github.com/users/mariamabarham/received_events', 'repos_url': 'https://api.github.com/users/mariamabarham/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/mariamabarham/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/mariamabarham/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/mariamabarham'} | [] | closed | false | null | [] | null | ['Circle CI shuold be green :-) '] | 2020-06-18 16:25:23+00:00 | 2020-06-22 08:09:25+00:00 | 2020-06-22 08:09:24+00:00 | CONTRIBUTOR | null | #283 | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/285/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/285/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/285.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/285', 'merged_at': '2020-06-22T08:09:23Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/285.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/285'} | true |
https://api.github.com/repos/huggingface/datasets/issues/284 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/284/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/284/comments | https://api.github.com/repos/huggingface/datasets/issues/284/events | https://github.com/huggingface/datasets/pull/284 | 641,337,217 | MDExOlB1bGxSZXF1ZXN0NDM2NTgxODQ2 | 284 | Fix manual download instructions | {'avatar_url': 'https://avatars.githubusercontent.com/u/23423619?v=4', 'events_url': 'https://api.github.com/users/patrickvonplaten/events{/privacy}', 'followers_url': 'https://api.github.com/users/patrickvonplaten/followers', 'following_url': 'https://api.github.com/users/patrickvonplaten/following{/other_user}', 'gists_url': 'https://api.github.com/users/patrickvonplaten/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/patrickvonplaten', 'id': 23423619, 'login': 'patrickvonplaten', 'node_id': 'MDQ6VXNlcjIzNDIzNjE5', 'organizations_url': 'https://api.github.com/users/patrickvonplaten/orgs', 'received_events_url': 'https://api.github.com/users/patrickvonplaten/received_events', 'repos_url': 'https://api.github.com/users/patrickvonplaten/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/patrickvonplaten/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/patrickvonplaten/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/patrickvonplaten'} | [] | closed | false | null | [] | null | ['Verified that this works, thanks!'
"But I get\r\n```python\r\nConnectionError: Couldn't reach https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/./datasets/wmt16/wmt16.py\r\n```\r\nWhen I try from jupyter on brutasse or my mac. (the jupyter server is run from transformers).\r\n\r\n\r\nBoth machines can run\r\n```bash\r\naws s3 ls s3://datasets.huggingface.co/nlp/datasets/wmt16/\r\n```\r\nbut it seems one must be in the nlp directory to run the command?\r\n\r\n(I ran `pip install -e . ` on this branch in both situations.)\r\n\r\n\r\n"
"`https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/./datasets/wmt16/wmt16.py` looks very weird.\r\n\r\n(Also, S3 is not a file-system, it's a flat key-value store)"
'Good to merge I think @lhoestq '
'> But I get\r\n> \r\n> ```python\r\n> ConnectionError: Couldn\'t reach https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/./datasets/wmt16/wmt16.py\r\n> ```\r\n> \r\n> When I try from jupyter on brutasse or my mac. (the jupyter server is run from transformers).\r\n> \r\n> Both machines can run\r\n> \r\n> ```shell\r\n> aws s3 ls s3://datasets.huggingface.co/nlp/datasets/wmt16/\r\n> ```\r\n> \r\n> but it seems one must be in the nlp directory to run the command?\r\n> \r\n> (I ran `pip install -e . ` on this branch in both situations.)\r\n\r\nAs soon as it is on master, the dataset script wmt16.py will be synced on S3 and you\'ll be able to do `load_dataset("wmt16")`'] | 2020-06-18 15:59:57+00:00 | 2020-06-19 08:24:21+00:00 | 2020-06-19 08:24:19+00:00 | MEMBER | null | This PR replaces the static `DatasetBulider` variable `MANUAL_DOWNLOAD_INSTRUCTIONS` by a property function `manual_download_instructions()`.
Some datasets like XTREME and all WMT need the manual data dir only for a small fraction of the possible configs.
After some brainstorming with @mariamabarham and @lhoestq, we came to the conclusion that having a property function `manual_download_instructions()` gives us more flexibility to decide on a per config basis in the dataset builder if manual download instructions are needed.
Also this PR should unblock solves a bug with `wmt16 - ro-en`
@sshleifer from this branch you should be able to succesfully run
```python
import nlp
ds = nlp.load_dataset('./datasets/wmt16', 'ro-en')
```
and once this PR is merged S3 should be synched so that
```python
import nlp
ds = nlp.load_dataset("wmt16", "ro-en")
```
works as well.
**Important**: Since `MANUAL_DOWNLOAD_INSTRUCTIONS` was not really exposed to the user, this PR should not be a problem regarding backward compatibility. | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/284/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/284/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/284.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/284', 'merged_at': '2020-06-19T08:24:19Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/284.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/284'} | true |
https://api.github.com/repos/huggingface/datasets/issues/283 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/283/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/283/comments | https://api.github.com/repos/huggingface/datasets/issues/283/events | https://github.com/huggingface/datasets/issues/283 | 641,270,439 | MDU6SXNzdWU2NDEyNzA0Mzk= | 283 | Consistent formatting of citations | {'avatar_url': 'https://avatars.githubusercontent.com/u/35882?v=4', 'events_url': 'https://api.github.com/users/srush/events{/privacy}', 'followers_url': 'https://api.github.com/users/srush/followers', 'following_url': 'https://api.github.com/users/srush/following{/other_user}', 'gists_url': 'https://api.github.com/users/srush/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/srush', 'id': 35882, 'login': 'srush', 'node_id': 'MDQ6VXNlcjM1ODgy', 'organizations_url': 'https://api.github.com/users/srush/orgs', 'received_events_url': 'https://api.github.com/users/srush/received_events', 'repos_url': 'https://api.github.com/users/srush/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/srush/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/srush/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/srush'} | [] | closed | false | {'avatar_url': 'https://avatars.githubusercontent.com/u/38249783?v=4', 'events_url': 'https://api.github.com/users/mariamabarham/events{/privacy}', 'followers_url': 'https://api.github.com/users/mariamabarham/followers', 'following_url': 'https://api.github.com/users/mariamabarham/following{/other_user}', 'gists_url': 'https://api.github.com/users/mariamabarham/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/mariamabarham', 'id': 38249783, 'login': 'mariamabarham', 'node_id': 'MDQ6VXNlcjM4MjQ5Nzgz', 'organizations_url': 'https://api.github.com/users/mariamabarham/orgs', 'received_events_url': 'https://api.github.com/users/mariamabarham/received_events', 'repos_url': 'https://api.github.com/users/mariamabarham/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/mariamabarham/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/mariamabarham/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/mariamabarham'} | [{'avatar_url': 'https://avatars.githubusercontent.com/u/38249783?v=4', 'events_url': 'https://api.github.com/users/mariamabarham/events{/privacy}', 'followers_url': 'https://api.github.com/users/mariamabarham/followers', 'following_url': 'https://api.github.com/users/mariamabarham/following{/other_user}', 'gists_url': 'https://api.github.com/users/mariamabarham/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/mariamabarham', 'id': 38249783, 'login': 'mariamabarham', 'node_id': 'MDQ6VXNlcjM4MjQ5Nzgz', 'organizations_url': 'https://api.github.com/users/mariamabarham/orgs', 'received_events_url': 'https://api.github.com/users/mariamabarham/received_events', 'repos_url': 'https://api.github.com/users/mariamabarham/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/mariamabarham/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/mariamabarham/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/mariamabarham'}] | null | [] | 2020-06-18 14:48:45+00:00 | 2020-06-22 17:30:46+00:00 | 2020-06-22 17:30:46+00:00 | CONTRIBUTOR | null | The citations are all of a different format, some have "```" and have text inside, others are proper bibtex.
Can we make it so that they all are proper citations, i.e. parse by the bibtex spec:
https://bibtexparser.readthedocs.io/en/master/ | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/283/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/283/timeline | null | completed | null | null | false |
https://api.github.com/repos/huggingface/datasets/issues/282 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/282/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/282/comments | https://api.github.com/repos/huggingface/datasets/issues/282/events | https://github.com/huggingface/datasets/pull/282 | 641,217,759 | MDExOlB1bGxSZXF1ZXN0NDM2NDgxNzMy | 282 | Update dataset_info from gcs | {'avatar_url': 'https://avatars.githubusercontent.com/u/42851186?v=4', 'events_url': 'https://api.github.com/users/lhoestq/events{/privacy}', 'followers_url': 'https://api.github.com/users/lhoestq/followers', 'following_url': 'https://api.github.com/users/lhoestq/following{/other_user}', 'gists_url': 'https://api.github.com/users/lhoestq/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/lhoestq', 'id': 42851186, 'login': 'lhoestq', 'node_id': 'MDQ6VXNlcjQyODUxMTg2', 'organizations_url': 'https://api.github.com/users/lhoestq/orgs', 'received_events_url': 'https://api.github.com/users/lhoestq/received_events', 'repos_url': 'https://api.github.com/users/lhoestq/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/lhoestq/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/lhoestq/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/lhoestq'} | [] | closed | false | null | [] | null | [] | 2020-06-18 13:41:15+00:00 | 2020-06-18 16:24:52+00:00 | 2020-06-18 16:24:51+00:00 | MEMBER | null | Some datasets are hosted on gcs (wikipedia for example). In this PR I make sure that, when a user loads such datasets, the file_instructions are built using the dataset_info.json from gcs and not from the info extracted from the local `dataset_infos.json` (the one that contain the info for each config). Indeed local files may end up outdated.
Furthermore, to avoid outdated dataset_infos.json, I now make sure that each time you run `load_dataset` it also tries to update the file locally.
| {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/282/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/282/timeline | null | null | 0 | {'diff_url': 'https://github.com/huggingface/datasets/pull/282.diff', 'html_url': 'https://github.com/huggingface/datasets/pull/282', 'merged_at': '2020-06-18T16:24:51Z', 'patch_url': 'https://github.com/huggingface/datasets/pull/282.patch', 'url': 'https://api.github.com/repos/huggingface/datasets/pulls/282'} | true |
https://api.github.com/repos/huggingface/datasets/issues/281 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/281/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/281/comments | https://api.github.com/repos/huggingface/datasets/issues/281/events | https://github.com/huggingface/datasets/issues/281 | 641,067,856 | MDU6SXNzdWU2NDEwNjc4NTY= | 281 | Private/sensitive data | {'avatar_url': 'https://avatars.githubusercontent.com/u/6368040?v=4', 'events_url': 'https://api.github.com/users/MFreidank/events{/privacy}', 'followers_url': 'https://api.github.com/users/MFreidank/followers', 'following_url': 'https://api.github.com/users/MFreidank/following{/other_user}', 'gists_url': 'https://api.github.com/users/MFreidank/gists{/gist_id}', 'gravatar_id': '', 'html_url': 'https://github.com/MFreidank', 'id': 6368040, 'login': 'MFreidank', 'node_id': 'MDQ6VXNlcjYzNjgwNDA=', 'organizations_url': 'https://api.github.com/users/MFreidank/orgs', 'received_events_url': 'https://api.github.com/users/MFreidank/received_events', 'repos_url': 'https://api.github.com/users/MFreidank/repos', 'site_admin': False, 'starred_url': 'https://api.github.com/users/MFreidank/starred{/owner}{/repo}', 'subscriptions_url': 'https://api.github.com/users/MFreidank/subscriptions', 'type': 'User', 'url': 'https://api.github.com/users/MFreidank'} | [] | closed | false | null | [] | null | ["Hi @MFreidank, you should already be able to load a dataset from local sources, indeed. (ping @lhoestq and @jplu)\r\n\r\nWe're also thinking about the ability to host private datasets on a hosted bucket with permission management, but that's further down the road."
'Hi @MFreidank, it is possible to load a dataset from your local storage, but only CSV/TSV and JSON are supported. To load a dataset in JSON format:\r\n\r\n```\r\nnlp.load_dataset(path="json", data_files={nlp.Split.TRAIN: ["path/to/train.json"], nlp.Split.TEST: ["path/to/test.json"]})\r\n```\r\n\r\nFor CSV/TSV datasets, you have to replace `json` by `csv`.'
'Hi @julien-c @jplu,\r\nThanks for sharing this solution with me, it helps, this is what I was looking for. \r\nIf not already there and only missed by me, this could be a great addition in the docs.\r\n\r\nClosing my issue as resolved, thanks again.'] | 2020-06-18 09:47:27+00:00 | 2020-06-20 13:15:12+00:00 | 2020-06-20 13:15:12+00:00 | CONTRIBUTOR | null | Hi all,
Thanks for this fantastic library, it makes it very easy to do prototyping for NLP projects interchangeably between TF/Pytorch.
Unfortunately, there is data that cannot easily be shared publicly as it may contain sensitive information.
Is there support/a plan to support such data with NLP, e.g. by reading it from local sources?
Use case flow could look like this: use NLP to prototype an approach on similar, public data and apply the resulting prototype on sensitive/private data without the need to rethink data processing pipelines.
Many thanks for your responses ahead of time and kind regards,
MFreidank | {'+1': 0, '-1': 0, 'confused': 0, 'eyes': 0, 'heart': 0, 'hooray': 0, 'laugh': 0, 'rocket': 0, 'total_count': 0, 'url': 'https://api.github.com/repos/huggingface/datasets/issues/281/reactions'} | https://api.github.com/repos/huggingface/datasets/issues/281/timeline | null | completed | null | null | false |