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https://api.github.com/repos/huggingface/datasets/issues/6049 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6049/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6049/comments | https://api.github.com/repos/huggingface/datasets/issues/6049/events | https://github.com/huggingface/datasets/pull/6049 | 1,810,378,706 | PR_kwDODunzps5Vz1pd | 6,049 | Update `ruff` version in pre-commit config | {
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https://api.github.com/repos/huggingface/datasets/issues/6048 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6048/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6048/comments | https://api.github.com/repos/huggingface/datasets/issues/6048/events | https://github.com/huggingface/datasets/issues/6048 | 1,809,629,346 | I_kwDODunzps5r3MCi | 6,048 | when i use datasets.load_dataset, i encounter the http connect error! | {
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`common_voice_test = load_dataset("audiofolder", data_dir="./dataset/",cache_dir="./cache",split=datasets.Split.TEST)`
when i run the code above, i got the error as below:
--------------------------------------------
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.3.2/datasets/audiofolder/audiofolder.py (ConnectionError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.3.2/datasets/audiofolder/audiofolder.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f299ed082e0>: Failed to establish a new connection: [Errno 101] Network is unreachable'))")))
--------------------------------------------------
My all data is on local machine, why does it need to connect the internet? how can i fix it, because my machine cannot connect the internet.
### Steps to reproduce the bug
1
### Expected behavior
no error when i use the load_dataset func
### Environment info
python=3.8.15 | {
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https://github.com/huggingface/transformers/pull/24867#discussion_r1266519626 | {
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Using the HTTP_PROXY and HTTPS_PROXY environment variables works though since we use aiohttp to call the HF Hub.
This can be implemented in `_prepare_single_hop_path_and_storage_options`.
Though ideally the `HfFileSystem` could support passing at least the proxies | {
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https://api.github.com/repos/huggingface/datasets/issues/6044 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6044/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6044/comments | https://api.github.com/repos/huggingface/datasets/issues/6044/events | https://github.com/huggingface/datasets/pull/6044 | 1,808,057,906 | PR_kwDODunzps5Vr7jr | 6,044 | Rename "pattern" to "path" in YAML data_files configs | {
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They can use "path" to specify a single path, <s>or "paths" to use a list of paths.</s>
Glob patterns are still supported though
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} | [] | open | false | null | [] | null | 2 | "2023-07-17T13:19:21" | "2023-07-18T08:26:03" | null | NONE | null | null | null | ### Describe the bug
Attempting to save a dataset as a compressed csv file, the compression kwargs provided to `.to_csv()` that get piped to panda's `pandas.DataFrame.to_csv` do not have any effect - resulting in the dataset not getting compressed.
A warning is raised if explicitly providing a `compression` kwarg, but no warnings are raised if relying on the defaults. This can lead to datasets secretly not getting compressed for users expecting the behaviour to match panda's `.to_csv()`, where the compression format is automatically inferred from the destination path suffix.
### Steps to reproduce the bug
```python
# dataset is not compressed (but at least a warning is emitted)
import datasets
dataset = datasets.load_dataset("rotten_tomatoes", split="train")
dataset.to_csv("uncompressed.csv")
print(os.path.getsize("uncompressed.csv")) # 1008607
dataset.to_csv("compressed.csv.gz", compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1})
print(os.path.getsize("compressed.csv.gz")) # 1008607
```
```shell
>>>
RuntimeWarning: compression has no effect when passing a non-binary object as input.
csv_str = batch.to_pandas().to_csv(
```
```python
# dataset is not compressed and no warnings are emitted
dataset.to_csv("compressed.csv.gz")
print(os.path.getsize("compressed.csv.gz")) # 1008607
# compare with
dataset.to_pandas().to_csv("pandas.csv.gz")
print(os.path.getsize("pandas.csv.gz")) # 418561
```
---
I think that this is because behind the scenes `pandas.DataFrame.to_csv` is always called with a buf-like `path_or_buf`, but users that are providing a path-like to `datasets.Dataset.to_csv` are likely not to expect / know that - leading to a mismatch in their understanding of the expected behaviour of the `compression` kwarg.
### Expected behavior
The dataset to be saved as a compressed csv file when providing a `compression` kwarg, or when relying on the default `compression='infer'`
### Environment info
`datasets == 2.13.1`
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https://api.github.com/repos/huggingface/datasets/issues/6041 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6041/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6041/comments | https://api.github.com/repos/huggingface/datasets/issues/6041/events | https://github.com/huggingface/datasets/pull/6041 | 1,807,441,055 | PR_kwDODunzps5Vp0GX | 6,041 | Flatten repository_structure docs on yaml | {
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https://api.github.com/repos/huggingface/datasets/issues/6040 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6040/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6040/comments | https://api.github.com/repos/huggingface/datasets/issues/6040/events | https://github.com/huggingface/datasets/pull/6040 | 1,807,410,238 | PR_kwDODunzps5VptVf | 6,040 | Fix legacy_dataset_infos | {
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https://circleci.com/gh/huggingface/transformers/855105 | {
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https://api.github.com/repos/huggingface/datasets/issues/6039 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6039/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6039/comments | https://api.github.com/repos/huggingface/datasets/issues/6039/events | https://github.com/huggingface/datasets/issues/6039 | 1,806,508,451 | I_kwDODunzps5rrSGj | 6,039 | Loading column subset from parquet file produces error since version 2.13 | {
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} | [] | open | false | null | [] | null | 0 | "2023-07-16T09:13:07" | "2023-07-16T09:16:39" | null | NONE | null | null | null | ### Describe the bug
`load_dataset` allows loading a subset of columns from a parquet file with the `columns` argument. Since version 2.13, this produces the following error:
```
Traceback (most recent call last):
File "/usr/lib/python3.10/site-packages/datasets/builder.py", line 1879, in _prepare_split_single
for _, table in generator:
File "/usr/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py", line 68, in _generate_tables
raise ValueError(
ValueError: Tried to load parquet data with columns '['sepal_length']' with mismatching features '{'sepal_length': Value(dtype='float64', id=None), 'sepal_width': Value(dtype='float64', id=None), 'petal_length': Value(dtype='float64', id=None), 'petal_width': Value(dtype='float64', id=None), 'species': Value(dtype='string', id=None)}'
```
This seems to occur because `datasets` is checking whether the columns in the schema exactly match the provided list of columns, instead of whether they are a subset.
### Steps to reproduce the bug
```python
# Prepare some sample data
import pandas as pd
iris = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')
iris.to_parquet('iris.parquet')
# ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']
print(iris.columns)
# Load data with datasets
from datasets import load_dataset
# Load full parquet file
dataset = load_dataset('parquet', data_files='iris.parquet')
# Load column subset; throws error for datasets>=2.13
dataset = load_dataset('parquet', data_files='iris.parquet', columns=['sepal_length'])
```
### Expected behavior
No error should be thrown and the given column subset should be loaded.
### Environment info
- `datasets` version: 2.13.0
- Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.35
- Python version: 3.10.9
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 1.5.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/6038 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6038/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6038/comments | https://api.github.com/repos/huggingface/datasets/issues/6038/events | https://github.com/huggingface/datasets/issues/6038 | 1,805,960,244 | I_kwDODunzps5rpMQ0 | 6,038 | File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/builder.py", line 992, in _download_and_prepare if str(split_generator.split_info.name).lower() == "all": AttributeError: 'str' object has no attribute 'split_info'. Did you mean: 'splitlines'? | {
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} | [] | open | false | null | [] | null | 1 | "2023-07-15T07:58:08" | "2023-07-17T13:35:12" | null | NONE | null | null | null | Hi, I use the code below to load local file
```
def _split_generators(self, dl_manager):
# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
# urls = _URLS[self.config.name]
data_dir = dl_manager.download_and_extract(_URLs)
print(data_dir)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir["train"]),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir["dev"]),
"split": "dev",
},
),
]
```
and error occured
```
Traceback (most recent call last):
File "/home/zhizhou/data1/zhanghao/huggingface/FineTuning_Transformer/load_local_dataset.py", line 2, in <module>
dataset = load_dataset("./QA_script.py",data_files='/home/zhizhou/.cache/huggingface/datasets/conversatiom_corps/part_file.json')
File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/load.py", line 1809, in load_dataset
builder_instance.download_and_prepare(
File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/builder.py", line 909, in download_and_prepare
self._download_and_prepare(
File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/builder.py", line 1670, in _download_and_prepare
super()._download_and_prepare(
File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/builder.py", line 992, in _download_and_prepare
if str(split_generator.split_info.name).lower() == "all":
AttributeError: 'str' object has no attribute 'split_info'. Did you mean: 'splitlines'?
```
Could you help me? | {
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https://api.github.com/repos/huggingface/datasets/issues/6037 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6037/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6037/comments | https://api.github.com/repos/huggingface/datasets/issues/6037/events | https://github.com/huggingface/datasets/issues/6037 | 1,805,887,184 | I_kwDODunzps5ro6bQ | 6,037 | Documentation links to examples are broken | {
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} | [] | closed | false | null | [] | null | 2 | "2023-07-15T04:54:50" | "2023-07-17T22:35:14" | "2023-07-17T15:10:32" | NONE | null | null | null | ### Describe the bug
The links at the bottom of [add_dataset](https://huggingface.co/docs/datasets/v1.2.1/add_dataset.html) to examples of specific datasets are all broken, for example
- text classification: [ag_news](https://github.com/huggingface/datasets/blob/master/datasets/ag_news/ag_news.py) (original data are in csv files)
### Steps to reproduce the bug
Click on links to examples from latest documentation
### Expected behavior
Links should be up to date - it might be more stable to link to https://huggingface.co/datasets/ag_news/blob/main/ag_news.py
### Environment info
dataset v1.2.1 | {
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https://api.github.com/repos/huggingface/datasets/issues/6036 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6036/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6036/comments | https://api.github.com/repos/huggingface/datasets/issues/6036/events | https://github.com/huggingface/datasets/pull/6036 | 1,805,138,898 | PR_kwDODunzps5ViKc4 | 6,036 | Deprecate search API | {
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} | [] | open | false | null | [] | null | 6 | "2023-07-14T16:22:09" | "2023-07-17T10:41:23" | null | CONTRIBUTOR | null | false | {
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} | The Search API only supports Faiss and ElasticSearch as vector stores, is somewhat difficult to maintain (e.g., it still doesn't support ElasticSeach 8.0, difficult testing, ...), does not have the best design (adds a bunch of methods to the `Dataset` class that are only useful after creating an index), the usage doesn't seem to be significant and is not integrated with the Hub. Since we have no plans/bandwidth to improve it and better alternatives such as `langchain` and `docarray` exist, I think it should be deprecated (and eventually removed).
If we decide to deprecate/remove it, the following usage instances need to be addressed:
* [Course](https://github.com/huggingface/course/blob/0018bb434204d9750a03592cb0d4e846093218d8/chapters/en/chapter5/6.mdx#L342 ) and [Blog](https://github.com/huggingface/blog/blob/4897c6f73d4492a0955ade503281711d01840e09/image-search-datasets.md?plain=1#L252) - calling the FAISS API directly should be OK in these instances as it's pretty simple to use for basic scenarios. Alternatively, we can use `langchain`, but this adds an extra dependency
* [Transformers](https://github.com/huggingface/transformers/blob/50726f9ea7afc6113da617f8f4ca1ab264a5e28a/src/transformers/models/rag/retrieval_rag.py#L183) - we can use the FAISS API directly and store the index as a separate attribute (and instead of building the `wiki_dpr` index each time the dataset is generated, we can generate it once and push it to the Hub repo, and then read it from there
cc @huggingface/datasets @LysandreJik for the opinion | {
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https://api.github.com/repos/huggingface/datasets/issues/6035 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6035/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6035/comments | https://api.github.com/repos/huggingface/datasets/issues/6035/events | https://github.com/huggingface/datasets/pull/6035 | 1,805,087,687 | PR_kwDODunzps5Vh_QR | 6,035 | Dataset representation | {
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https://api.github.com/repos/huggingface/datasets/issues/6034 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6034/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6034/comments | https://api.github.com/repos/huggingface/datasets/issues/6034/events | https://github.com/huggingface/datasets/issues/6034 | 1,804,501,361 | I_kwDODunzps5rjoFx | 6,034 | load_dataset hangs on WSL | {
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load_dataset simply hangs. It happens once every ~5 times, and interestingly hangs for a multiple of 5 minutes (hangs for 5/10/15 minutes). Using the profiler in PyCharm shows that it spends the time at <method 'connect' of '_socket.socket' objects>. However, a local cache is available so I am not sure why socket is needed. ([profiler result](https://ibb.co/0Btbbp8))
It only happens on WSL for me. It works for native Windows and my MacBook. (cache quickly recognized and loaded within a second).
### Steps to reproduce the bug
I am using Ubuntu 22.04.2 LTS (GNU/Linux 5.15.90.1-microsoft-standard-WSL2 x86_64)
Python 3.10.10 (main, Mar 21 2023, 18:45:11) [GCC 11.2.0] on linux
>>> import datasets
>>> datasets.load_dataset('ai2_arc', 'ARC-Challenge') # hangs for 5/10/15 minutes
### Expected behavior
cache quickly recognized and loaded within a second
### Environment info
Please let me know if I should provide more environment information. | {
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https://api.github.com/repos/huggingface/datasets/issues/6033 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6033/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6033/comments | https://api.github.com/repos/huggingface/datasets/issues/6033/events | https://github.com/huggingface/datasets/issues/6033 | 1,804,482,051 | I_kwDODunzps5rjjYD | 6,033 | `map` function doesn't fully utilize `input_columns`. | {
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} | [] | closed | false | null | [] | null | 0 | "2023-07-14T08:49:28" | "2023-07-14T09:16:04" | "2023-07-14T09:16:04" | NONE | null | null | null | ### Describe the bug
I wanted to select only some columns of data.
And I thought that's why the argument `input_columns` exists.
What I expected is like this:
If there are ["a", "b", "c", "d"] columns, and if I set `input_columns=["a", "d"]`, the data will have only ["a", "d"] columns.
But it doesn't select columns.
It preserves existing columns.
The main cause is `update` function of `dictionary` type `transformed_batch`.
https://github.com/huggingface/datasets/blob/682d21e94ab1e64c11b583de39dc4c93f0101c5a/src/datasets/iterable_dataset.py#L687-L691
`transformed_batch` gets all the columns by `transformed_batch = dict(batch)`.
Even `function_args` selects `input_columns`, `update` preserves columns other than `input_columns`.
I think it should take a new dictionary with columns in `input_columns` like this:
```
# transformed_batch = dict(batch)
# transformed_batch.update(self.function(*function_args, **self.fn_kwargs)
# This is what I think correct.
transformed_batch = self.function(*function_args, **self.fn_kwargs)
```
Let me know how to use `input_columns`.
### Steps to reproduce the bug
Described all above.
### Expected behavior
Described all above.
### Environment info
datasets: 2.12
python: 3.8 | {
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https://api.github.com/repos/huggingface/datasets/issues/6032 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6032/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6032/comments | https://api.github.com/repos/huggingface/datasets/issues/6032/events | https://github.com/huggingface/datasets/issues/6032 | 1,804,358,679 | I_kwDODunzps5rjFQX | 6,032 | DownloadConfig.proxies not work when load_dataset_builder calling HfApi.dataset_info | {
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} | [] | open | false | null | [] | null | 2 | "2023-07-14T07:22:55" | "2023-07-17T04:12:45" | null | NONE | null | null | null | ### Describe the bug
```python
download_config = DownloadConfig(proxies={'https': '<my proxy>'})
builder = load_dataset_builder(..., download_config=download_config)
```
But, when getting the dataset_info from HfApi, the http requests not using the proxies.
### Steps to reproduce the bug
1. Setup proxies in DownloadConfig.
2. Call `load_dataset_build` with download_config.
3. Inspect the call stack in HfApi.dataset_info.
### Expected behavior
DownloadConfig.proxies works for getting dataset_info.
### Environment info
https://github.com/huggingface/datasets/commit/406b2212263c0d33f267e35b917f410ff6b3bc00
Python 3.11.4 | {
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https://api.github.com/repos/huggingface/datasets/issues/6031 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6031/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6031/comments | https://api.github.com/repos/huggingface/datasets/issues/6031/events | https://github.com/huggingface/datasets/issues/6031 | 1,804,183,858 | I_kwDODunzps5riaky | 6,031 | Argument type for map function changes when using `input_columns` for `IterableDataset` | {
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} | [] | closed | false | null | [] | null | 1 | "2023-07-14T05:11:14" | "2023-07-14T14:44:15" | "2023-07-14T14:44:15" | NONE | null | null | null | ### Describe the bug
I wrote `tokenize(examples)` function as an argument for `map` function for `IterableDataset`.
It process dictionary type `examples` as a parameter.
It is used in `train_dataset = train_dataset.map(tokenize, batched=True)`
No error is raised.
And then, I found some unnecessary keys and values in `examples` so I added `input_columns` argument to `map` function to select keys and values.
It gives me an error saying
```
TypeError: tokenize() takes 1 positional argument but 3 were given.
```
The code below matters.
https://github.com/huggingface/datasets/blob/406b2212263c0d33f267e35b917f410ff6b3bc00/src/datasets/iterable_dataset.py#L687
For example, `inputs = {"a":1, "b":2, "c":3}`.
If `self.input_coluns` is `None`,
`inputs` is a dictionary type variable and `function_args` becomes a `list` of a single `dict` variable.
`function_args` becomes `[{"a":1, "b":2, "c":3}]`
Otherwise, lets say `self.input_columns = ["a", "c"]`
`[inputs[col] for col in self.input_columns]` results in `[1, 3]`.
I think it should be `[{"a":1, "c":3}]`.
I want to ask if the resulting format is intended.
Maybe I can modify `tokenize()` to have 2 parameters in this case instead of having 1 dictionary.
But this is confusing to me.
Or it should be fixed as `[{col:inputs[col] for col in self.input_columns}]`
### Steps to reproduce the bug
Run `map` function of `IterableDataset` with `input_columns` argument.
### Expected behavior
`function_args` looks better to have same format.
I think it should be `[{"a":1, "c":3}]`.
### Environment info
dataset version: 2.12
python: 3.8 | {
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https://api.github.com/repos/huggingface/datasets/issues/6030 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6030/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6030/comments | https://api.github.com/repos/huggingface/datasets/issues/6030/events | https://github.com/huggingface/datasets/pull/6030 | 1,803,864,744 | PR_kwDODunzps5Vd0ZG | 6,030 | fixed typo in comment | {
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} | This mistake was a bit confusing, so I thought it was worth sending a PR over. | {
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https://api.github.com/repos/huggingface/datasets/issues/6029 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6029/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6029/comments | https://api.github.com/repos/huggingface/datasets/issues/6029/events | https://github.com/huggingface/datasets/pull/6029 | 1,803,460,046 | PR_kwDODunzps5VcbPW | 6,029 | [docs] Fix link | {
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} | Fixes link to the builder classes :) | {
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https://api.github.com/repos/huggingface/datasets/issues/6028 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6028/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6028/comments | https://api.github.com/repos/huggingface/datasets/issues/6028/events | https://github.com/huggingface/datasets/pull/6028 | 1,803,294,981 | PR_kwDODunzps5Vb3LJ | 6,028 | Use new hffs | {
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} | Thanks to @janineguo 's work in https://github.com/huggingface/datasets/pull/5919 which was needed to support HfFileSystem.
Switching to `HfFileSystem` will help implementing optimization in data files resolution
## Implementation details
I replaced all the from_hf_repo and from_local_or_remote in data_files.py to only use a new `from_patterns` which works for any fsspec path, including hf:// paths, https:// URLs and local paths. This simplifies the codebase since there is no logic duplication anymore when it comes to data files resolution.
I added `_prepare_path_and_storage_options` which returns the right storage_options to use given a path and a `DownloadConfig`. This is the only place where the logic depends on the filesystem type that must be used.
I also removed the `get_metadata_data_files_list ` and `get_patterns_and_data_files` functions added recently, since data files resolution is now handled using a common interface.
## New features
hf:// paths are now supported in data_files
## Breaking changes
DataFilesList and DataFilesDict:
- use `str` paths instead of `Union[Path, Url]`
- require posix paths for windows paths
close https://github.com/huggingface/datasets/issues/6017 | {
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https://api.github.com/repos/huggingface/datasets/issues/6027 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6027/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6027/comments | https://api.github.com/repos/huggingface/datasets/issues/6027/events | https://github.com/huggingface/datasets/pull/6027 | 1,803,008,486 | PR_kwDODunzps5Va4g3 | 6,027 | Delete `task_templates` in `IterableDataset` when they are no longer valid | {
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https://api.github.com/repos/huggingface/datasets/issues/6026 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6026/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6026/comments | https://api.github.com/repos/huggingface/datasets/issues/6026/events | https://github.com/huggingface/datasets/pull/6026 | 1,802,929,222 | PR_kwDODunzps5VanI8 | 6,026 | Fix style with ruff 0.0.278 | {
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https://api.github.com/repos/huggingface/datasets/issues/6025 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6025/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6025/comments | https://api.github.com/repos/huggingface/datasets/issues/6025/events | https://github.com/huggingface/datasets/issues/6025 | 1,801,852,601 | I_kwDODunzps5rZha5 | 6,025 | Using a dataset for a use other than it was intended for. | {
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} | [] | closed | false | null | [] | null | 1 | "2023-07-12T22:33:17" | "2023-07-13T13:57:36" | "2023-07-13T13:57:36" | NONE | null | null | null | ### Describe the bug
Hi, I want to use the rotten tomatoes dataset but for a task other than classification, but when I interleave the dataset, it throws ```'ValueError: Column label is not present in features.'```. It seems that the label_col must be there in the dataset for some reason?
Here is the full stacktrace
```
File "/home/suryahari/Vornoi/tryage-handoff-other-datasets.py", line 276, in create_dataloaders
dataset = interleave_datasets(dsfold, stopping_strategy="all_exhausted")
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py", line 134, in interleave_datasets
return _interleave_iterable_datasets(
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1833, in _interleave_iterable_datasets
info = DatasetInfo.from_merge([d.info for d in datasets])
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 275, in from_merge
dataset_infos = [dset_info.copy() for dset_info in dataset_infos if dset_info is not None]
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 275, in <listcomp>
dataset_infos = [dset_info.copy() for dset_info in dataset_infos if dset_info is not None]
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 378, in copy
return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()})
File "<string>", line 20, in __init__
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 208, in __post_init__
self.task_templates = [
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 209, in <listcomp>
template.align_with_features(self.features) for template in (self.task_templates)
File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/tasks/text_classification.py", line 20, in align_with_features
raise ValueError(f"Column {self.label_column} is not present in features.")
ValueError: Column label is not present in features.
```
### Steps to reproduce the bug
Delete the column `labels` from the `rotten_tomatoes` dataset. Try to interleave it with other datasets.
### Expected behavior
Should let me use the dataset with just the `text` field
### Environment info
latest datasets library? I don't think this was an issue in earlier versions. | {
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https://api.github.com/repos/huggingface/datasets/issues/6024 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6024/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6024/comments | https://api.github.com/repos/huggingface/datasets/issues/6024/events | https://github.com/huggingface/datasets/pull/6024 | 1,801,708,808 | PR_kwDODunzps5VWbGe | 6,024 | Don't reference self in Spark._validate_cache_dir | {
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} | [] | closed | false | null | [] | null | 4 | "2023-07-12T20:31:16" | "2023-07-13T16:58:32" | "2023-07-13T12:37:09" | CONTRIBUTOR | null | false | {
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} | Fix for https://github.com/huggingface/datasets/issues/5963 | {
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https://api.github.com/repos/huggingface/datasets/issues/6023 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6023/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6023/comments | https://api.github.com/repos/huggingface/datasets/issues/6023/events | https://github.com/huggingface/datasets/pull/6023 | 1,801,272,420 | PR_kwDODunzps5VU7EG | 6,023 | Fix `ClassLabel` min max check for `None` values | {
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} | [] | closed | false | null | [] | null | 3 | "2023-07-12T15:46:12" | "2023-07-12T16:29:26" | "2023-07-12T16:18:04" | CONTRIBUTOR | null | false | {
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} | Fix #6022 | {
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https://api.github.com/repos/huggingface/datasets/issues/6022 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6022/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6022/comments | https://api.github.com/repos/huggingface/datasets/issues/6022/events | https://github.com/huggingface/datasets/issues/6022 | 1,800,092,589 | I_kwDODunzps5rSzut | 6,022 | Batch map raises TypeError: '>=' not supported between instances of 'NoneType' and 'int' | {
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} | [] | closed | false | null | [] | null | 1 | "2023-07-12T03:20:17" | "2023-07-12T16:18:06" | "2023-07-12T16:18:05" | NONE | null | null | null | ### Describe the bug
When mapping some datasets with `batched=True`, datasets may raise an exeception:
```python
Traceback (most recent call last):
File "/Users/codingl2k1/Work/datasets/venv/lib/python3.11/site-packages/multiprocess/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/Work/datasets/src/datasets/utils/py_utils.py", line 1328, in _write_generator_to_queue
for i, result in enumerate(func(**kwargs)):
File "/Users/codingl2k1/Work/datasets/src/datasets/arrow_dataset.py", line 3483, in _map_single
writer.write_batch(batch)
File "/Users/codingl2k1/Work/datasets/src/datasets/arrow_writer.py", line 549, in write_batch
array = cast_array_to_feature(col_values, col_type) if col_type is not None else col_values
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/Work/datasets/src/datasets/table.py", line 1831, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/Work/datasets/src/datasets/table.py", line 1831, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/Work/datasets/src/datasets/table.py", line 2063, in cast_array_to_feature
return feature.cast_storage(array)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/Work/datasets/src/datasets/features/features.py", line 1098, in cast_storage
if min_max["max"] >= self.num_classes:
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: '>=' not supported between instances of 'NoneType' and 'int'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/Users/codingl2k1/Work/datasets/t1.py", line 33, in <module>
ds = ds.map(transforms, num_proc=14, batched=True, batch_size=5)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/Work/datasets/src/datasets/dataset_dict.py", line 850, in map
{
File "/Users/codingl2k1/Work/datasets/src/datasets/dataset_dict.py", line 851, in <dictcomp>
k: dataset.map(
^^^^^^^^^^^^
File "/Users/codingl2k1/Work/datasets/src/datasets/arrow_dataset.py", line 577, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/Work/datasets/src/datasets/arrow_dataset.py", line 542, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/Work/datasets/src/datasets/arrow_dataset.py", line 3179, in map
for rank, done, content in iflatmap_unordered(
File "/Users/codingl2k1/Work/datasets/src/datasets/utils/py_utils.py", line 1368, in iflatmap_unordered
[async_result.get(timeout=0.05) for async_result in async_results]
File "/Users/codingl2k1/Work/datasets/src/datasets/utils/py_utils.py", line 1368, in <listcomp>
[async_result.get(timeout=0.05) for async_result in async_results]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/codingl2k1/Work/datasets/venv/lib/python3.11/site-packages/multiprocess/pool.py", line 774, in get
raise self._value
TypeError: '>=' not supported between instances of 'NoneType' and 'int'
```
### Steps to reproduce the bug
1. Checkout the latest main of datasets.
2. Run the code:
```python
from datasets import load_dataset
def transforms(examples):
# examples["pixel_values"] = [image.convert("RGB").resize((100, 100)) for image in examples["image"]]
return examples
ds = load_dataset("scene_parse_150")
ds = ds.map(transforms, num_proc=14, batched=True, batch_size=5)
print(ds)
```
### Expected behavior
map without exception.
### Environment info
Datasets: https://github.com/huggingface/datasets/commit/b8067c0262073891180869f700ebef5ac3dc5cce
Python: 3.11.4
System: Macos | {
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https://api.github.com/repos/huggingface/datasets/issues/6021 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6021/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6021/comments | https://api.github.com/repos/huggingface/datasets/issues/6021/events | https://github.com/huggingface/datasets/pull/6021 | 1,799,785,904 | PR_kwDODunzps5VP11Q | 6,021 | [docs] Update return statement of index search | {
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} | Clarifies in the return statement of the docstring that the retrieval score is `IndexFlatL2` by default (see [PR](https://github.com/huggingface/transformers/issues/24739) and internal Slack [convo](https://huggingface.slack.com/archives/C01229B19EX/p1689105179711689)), and fixes the formatting because multiple return values are not supported. | {
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https://api.github.com/repos/huggingface/datasets/issues/6020 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6020/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6020/comments | https://api.github.com/repos/huggingface/datasets/issues/6020/events | https://github.com/huggingface/datasets/issues/6020 | 1,799,720,536 | I_kwDODunzps5rRY5Y | 6,020 | Inconsistent "The features can't be aligned" error when combining map, multiprocessing, and variable length outputs | {
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I'm using a dataset with map and multiprocessing to run a function that returned a variable length list of outputs. This output list may be empty. Normally this is handled fine, but there is an edge case that crops up when using multiprocessing. In some cases, an empty list result ends up in a dataset shard consisting of a single item. This results in a `The features can't be aligned` error that is difficult to debug because it depends on the number of processes/shards used.
I've reproduced a minimal example below. My current workaround is to fill empty results with a dummy value that I filter after, but this was a weird error that took a while to track down.
### Steps to reproduce the bug
```python
import datasets
dataset = datasets.Dataset.from_list([{'idx':i} for i in range(60)])
def test_func(row, idx):
if idx==58:
return {'output': []}
else:
return {'output' : [{'test':1}, {'test':2}]}
# this works fine
test1 = dataset.map(lambda row, idx: test_func(row, idx), with_indices=True, num_proc=4)
# this fails
test2 = dataset.map(lambda row, idx: test_func(row, idx), with_indices=True, num_proc=32)
>ValueError: The features can't be aligned because the key output of features {'idx': Value(dtype='int64', id=None), 'output': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None)} has unexpected type - Sequence(feature=Value(dtype='null', id=None), length=-1, id=None) (expected either [{'test': Value(dtype='int64', id=None)}] or Value("null").
```
The error occurs during the check
```python
_check_if_features_can_be_aligned([dset.features for dset in dsets])
```
When the multiprocessing splitting lines up just right with the empty return value, one of the `dset` in `dsets` will have a single item with an empty list value, causing the error.
### Expected behavior
Expected behavior is the result would be the same regardless of the `num_proc` value used.
### Environment info
Datasets version 2.11.0
Python 3.9.16 | {
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https://api.github.com/repos/huggingface/datasets/issues/6019 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6019/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6019/comments | https://api.github.com/repos/huggingface/datasets/issues/6019/events | https://github.com/huggingface/datasets/pull/6019 | 1,799,532,822 | PR_kwDODunzps5VPAlD | 6,019 | Improve logging | {
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} | Adds the StreamHandler (as `hfh` and `transformers` do) to the library's logger to log INFO messages and logs the messages about "loading a cached result" (and some other warnings) as INFO
(Also removes the `leave=False` arg in the progress bars to be consistent with `hfh` and `transformers` - progress bars serve as an indicator that a result is not cached, so it makes more sense not to delete them)
Fix #2832, fix https://github.com/huggingface/datasets/issues/1948, fix https://github.com/huggingface/datasets/issues/5444 | {
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https://api.github.com/repos/huggingface/datasets/issues/6017 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6017/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6017/comments | https://api.github.com/repos/huggingface/datasets/issues/6017/events | https://github.com/huggingface/datasets/issues/6017 | 1,799,309,132 | I_kwDODunzps5rP0dM | 6,017 | Switch to huggingface_hub's HfFileSystem | {
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] | null | 0 | "2023-07-11T16:24:40" | "2023-07-17T17:01:01" | "2023-07-17T17:01:01" | MEMBER | null | null | null | instead of the current datasets.filesystems.hffilesystem.HfFileSystem which can be slow in some cases
related to https://github.com/huggingface/datasets/issues/5846 and https://github.com/huggingface/datasets/pull/5919 | {
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not sure if this is the right way to go about it, I will wait for your feedback | {
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Overview:
The repository (huggingface/datasets-viewer) was recently archived and when I tried to run the code, there was the error message "AttributeError: module 'datasets.load' has no attribute 'prepare_module'". I could not resolve the issue myself due to lack of documentation of that attribute.
Request:
I kindly request the sharing of the code responsible for the dataset preview functionality or help with resolving the error. The dataset viewer on the Hugging Face website is incredibly useful since it is compatible with different types of inputs. It allows users to find datasets that meet their needs more efficiently. If needed, I am willing to contribute to the project by testing, documenting, and providing feedback on the dataset viewer code.
Thank you for considering this request, and I look forward to your response. | {
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] | open | false | null | [] | null | 1 | "2023-07-10T06:42:20" | "2023-07-10T15:37:52" | null | CONTRIBUTOR | null | null | null | ### Feature request
Currently adding a new column with `map` will cause all the data in the dataset to be duplicated and stored/cached on the disk again. It should reuse unchanged columns.
### Motivation
This allows having datasets with different columns but sharing some basic columns. Currently, these datasets would become too expensive to store and one would need some kind of on-the-fly join; which also doesn't seem implemented.
### Your contribution
_ | {
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https://api.github.com/repos/huggingface/datasets/issues/6012 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6012/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6012/comments | https://api.github.com/repos/huggingface/datasets/issues/6012/events | https://github.com/huggingface/datasets/issues/6012 | 1,795,575,432 | I_kwDODunzps5rBk6I | 6,012 | [FR] Transform Chaining, Lazy Mapping | {
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] | open | false | null | [] | null | 6 | "2023-07-09T21:40:21" | "2023-07-14T13:12:40" | null | CONTRIBUTOR | null | null | null | ### Feature request
Currently using a `map` call processes and duplicates the whole dataset, which takes both time and disk space.
The solution is to allow lazy mapping, which is essentially a saved chain of transforms that are applied on the fly whenever a slice of the dataset is requested.
The API should look like `map`, as `set_transform` changes the current dataset while `map` returns another dataset.
### Motivation
Lazy processing allows lower disk usage and faster experimentation.
### Your contribution
_ | {
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https://api.github.com/repos/huggingface/datasets/issues/6011 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6011/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6011/comments | https://api.github.com/repos/huggingface/datasets/issues/6011/events | https://github.com/huggingface/datasets/issues/6011 | 1,795,296,568 | I_kwDODunzps5rAg04 | 6,011 | Documentation: wiki_dpr Dataset has no metric_type for Faiss Index | {
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} | [] | closed | false | null | [] | null | 2 | "2023-07-09T08:30:19" | "2023-07-11T03:02:36" | "2023-07-11T03:02:36" | NONE | null | null | null | ### Describe the bug
After loading `wiki_dpr` using:
```py
ds = load_dataset(path='wiki_dpr', name='psgs_w100.multiset.compressed', split='train')
print(ds.get_index("embeddings").metric_type) # prints nothing because the value is None
```
the index does not have a defined `metric_type`. This is an issue because I do not know how the `scores` are being computed for `get_nearest_examples()`.
### Steps to reproduce the bug
System: Python 3.9.16, Transformers 4.30.2, WSL
After loading `wiki_dpr` using:
```py
ds = load_dataset(path='wiki_dpr', name='psgs_w100.multiset.compressed', split='train')
print(ds.get_index("embeddings").metric_type) # prints nothing because the value is None
```
the index does not have a defined `metric_type`. This is an issue because I do not know how the `scores` are being computed for `get_nearest_examples()`.
```py
from transformers import DPRQuestionEncoder, DPRContextEncoder, DPRQuestionEncoderTokenizer, DPRContextEncoderTokenizer
tokenizer = DPRQuestionEncoderTokenizer.from_pretrained("facebook/dpr-question_encoder-multiset-base")
encoder = DPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-multiset-base")
def encode_question(query, tokenizer=tokenizer, encoder=encoder):
inputs = tokenizer(query, return_tensors='pt')
question_embedding = encoder(**inputs)[0].detach().numpy()
return question_embedding
def get_knn(query, k=5, tokenizer=tokenizer, encoder=encoder, verbose=False):
enc_question = encode_question(query, tokenizer, encoder)
topk_results = ds.get_nearest_examples(index_name='embeddings',
query=enc_question,
k=k)
a = torch.tensor(enc_question[0]).reshape(768)
b = torch.tensor(topk_results.examples['embeddings'][0])
print(a.shape, b.shape)
print(torch.dot(a, b))
print((a-b).pow(2).sum())
return topk_results
```
The [FAISS documentation](https://github.com/facebookresearch/faiss/wiki/MetricType-and-distances) suggests the metric is usually L2 distance (without the square root) or the inner product. I compute both for the sample query:
```py
query = """ it catapulted into popular culture along with a line of action figures and other toys by Bandai.[2] By 2001, the media franchise had generated over $6 billion in toy sales.
Despite initial criticism that its action violence targeted child audiences, the franchise has been commercially successful."""
get_knn(query,k=5)
```
Here, I get dot product of 80.6020 and L2 distance of 77.6616 and
```py
NearestExamplesResults(scores=array([76.20431 , 75.312416, 74.945404, 74.866394, 74.68506 ],
dtype=float32), examples={'id': ['3081096', '2004811', '8908258', '9594124', '286575'], 'text': ['actors, resulting in the "Power Rangers" franchise which has continued since then into sequel TV series (with "Power Rangers Beast Morphers" set to premiere in 2019), comic books, video games, and three feature films, with a further cinematic universe planned. Following from the success of "Power Rangers", Saban acquired the rights to more of Toei\'s library, creating "VR Troopers" and "Big Bad Beetleborgs" from several Metal Hero Series shows and "Masked Rider" from Kamen Rider Series footage. DIC Entertainment joined this boom by acquiring the rights to "Gridman the Hyper Agent" and turning it into "Superhuman Samurai Syber-Squad". In 2002,',
```
Doing `k=1` indicates the higher the outputted number, the better the match, so the metric should not be L2 distance. However, my manually computed inner product (80.6) has a discrepancy with the reported (76.2). Perhaps, this has to do with me using the `compressed` embeddings?
### Expected behavior
```py
ds = load_dataset(path='wiki_dpr', name='psgs_w100.multiset.compressed', split='train')
print(ds.get_index("embeddings").metric_type) # METRIC_INNER_PRODUCT
```
### Environment info
- `datasets` version: 2.12.0
- Platform: Linux-4.18.0-477.13.1.el8_8.x86_64-x86_64-with-glibc2.28
- Python version: 3.9.16
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.1 | {
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] | open | false | null | [] | null | 2 | "2023-07-07T16:38:03" | "2023-07-16T13:00:18" | null | CONTRIBUTOR | null | null | null | Currently, `Dataset.__repr__` outputs a dataset's column names and the number of rows. We could improve it by printing its features and the first few rows.
We should also implement `_repr_html_` to have a rich HTML representation in notebooks/Streamlit. | {
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https://api.github.com/repos/huggingface/datasets/issues/6008 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6008/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6008/comments | https://api.github.com/repos/huggingface/datasets/issues/6008/events | https://github.com/huggingface/datasets/issues/6008 | 1,789,869,344 | I_kwDODunzps5qrz0g | 6,008 | Dataset.from_generator consistently freezes at ~1000 rows | {
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} | [] | closed | false | null | [] | null | 3 | "2023-07-05T16:06:48" | "2023-07-10T13:46:39" | "2023-07-10T13:46:39" | NONE | null | null | null | ### Describe the bug
Whenever I try to create a dataset which contains images using `Dataset.from_generator`, it freezes around 996 rows. I suppose it has something to do with memory consumption, but there's more memory available. I
Somehow it worked a few times but mostly this makes the datasets library much more cumbersome to work with because generators are the easiest way to turn an existing dataset into a Hugging Face dataset.
I've let it run in the frozen state for way longer than it can possibly take to load the actual dataset.
Let me know if you have ideas how to resolve it!
### Steps to reproduce the bug
```python
from datasets import Dataset
import numpy as np
def gen():
for row in range(10000):
yield {"i": np.random.rand(512, 512, 3)}
Dataset.from_generator(gen)
# -> 90% of the time gets stuck around 1000 rows
```
### Expected behavior
Should continue and go through all the examples yielded by the generator, or at least throw an error or somehow communicate what's going on.
### Environment info
- `datasets` version: 2.8.0
- Platform: Linux-5.15.0-52-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 12.0.1
- Pandas version: 1.5.1
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https://api.github.com/repos/huggingface/datasets/issues/6007 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6007/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6007/comments | https://api.github.com/repos/huggingface/datasets/issues/6007/events | https://github.com/huggingface/datasets/issues/6007 | 1,789,782,693 | I_kwDODunzps5qreql | 6,007 | Get an error "OverflowError: Python int too large to convert to C long" when loading a large dataset | {
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] | open | false | null | [] | null | 7 | "2023-07-05T15:16:50" | "2023-07-10T19:11:17" | null | CONTRIBUTOR | null | null | null | ### Describe the bug
When load a large dataset with the following code
```python
from datasets import load_dataset
dataset = load_dataset("liwu/MNBVC", 'news_peoples_daily', split='train')
```
We encountered the error: "OverflowError: Python int too large to convert to C long"
The error look something like:
```
OverflowError: Python int too large to convert to C long
During handling of the above exception, another exception occurred:
OverflowError Traceback (most recent call last)
<ipython-input-7-0ed8700e662d> in <module>
----> 1 dataset = load_dataset("liwu/MNBVC", 'news_peoples_daily', split='train', cache_dir='/sfs/MNBVC/.cache/')
/sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, **config_kwargs)
1749 ignore_verifications=ignore_verifications,
1750 try_from_hf_gcs=try_from_hf_gcs,
-> 1751 use_auth_token=use_auth_token,
1752 )
1753
/sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
703 if not downloaded_from_gcs:
704 self._download_and_prepare(
--> 705 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
706 )
707 # Sync info
/sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos)
1225
1226 def _download_and_prepare(self, dl_manager, verify_infos):
-> 1227 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos)
1228
1229 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable:
/sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
791 try:
792 # Prepare split will record examples associated to the split
--> 793 self._prepare_split(split_generator, **prepare_split_kwargs)
794 except OSError as e:
795 raise OSError(
/sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/builder.py in _prepare_split(self, split_generator, check_duplicate_keys)
1219 writer.write(example, key)
1220 finally:
-> 1221 num_examples, num_bytes = writer.finalize()
1222
1223 split_generator.split_info.num_examples = num_examples
/sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/arrow_writer.py in finalize(self, close_stream)
536 # Re-intializing to empty list for next batch
537 self.hkey_record = []
--> 538 self.write_examples_on_file()
539 if self.pa_writer is None:
540 if self.schema:
/sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/arrow_writer.py in write_examples_on_file(self)
407 # Since current_examples contains (example, key) tuples
408 batch_examples[col] = [row[0][col] for row in self.current_examples]
--> 409 self.write_batch(batch_examples=batch_examples)
410 self.current_examples = []
411
/sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size)
506 col_try_type = try_features[col] if try_features is not None and col in try_features else None
507 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col)
--> 508 arrays.append(pa.array(typed_sequence))
509 inferred_features[col] = typed_sequence.get_inferred_type()
510 schema = inferred_features.arrow_schema if self.pa_writer is None else self.schema
/sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/array.pxi in pyarrow.lib.array()
/sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol()
/sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/arrow_writer.py in __arrow_array__(self, type)
180 else:
181 trying_cast_to_python_objects = True
--> 182 out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))
183 # use smaller integer precisions if possible
184 if self.trying_int_optimization:
/sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/array.pxi in pyarrow.lib.array()
/sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/array.pxi in pyarrow.lib._sequence_to_array()
/sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
OverflowError: Python int too large to convert to C long
```
However, that dataset can be loaded in a streaming manner:
```python
from datasets import load_dataset
dataset = load_dataset("liwu/MNBVC", 'news_peoples_daily', split='train', streaming=True)
for i in dataset:
pass # it work well
```
Another issue is reported in our dataset hub:
https://huggingface.co/datasets/liwu/MNBVC/discussions/2
### Steps to reproduce the bug
from datasets import load_dataset
dataset = load_dataset("liwu/MNBVC", 'news_peoples_daily', split='train')
### Expected behavior
the dataset can be safely loaded
### Environment info
- `datasets` version: 2.4.0
- Platform: Linux-3.10.0-1160.an7.x86_64-x86_64-with-centos-7.9
- Python version: 3.6.8
- PyArrow version: 6.0.1
- Pandas version: 1.1.5 | {
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https://api.github.com/repos/huggingface/datasets/issues/6006 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6006/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6006/comments | https://api.github.com/repos/huggingface/datasets/issues/6006/events | https://github.com/huggingface/datasets/issues/6006 | 1,788,855,582 | I_kwDODunzps5qn8Ue | 6,006 | NotADirectoryError when loading gigawords | {
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} | [] | closed | false | null | [] | null | 1 | "2023-07-05T06:23:41" | "2023-07-05T06:31:02" | "2023-07-05T06:31:01" | NONE | null | null | null | ### Describe the bug
got `NotADirectoryError` whtn loading gigawords dataset
### Steps to reproduce the bug
When running
```
import datasets
datasets.load_dataset('gigaword')
```
Got the following exception:
```bash
Traceback (most recent call last): [0/1862]
File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1629, in _prepare_split_single
for key, record in generator:
File "/home/x/.cache/huggingface/modules/datasets_modules/datasets/gigaword/ea83a8b819190acac5f2dae011fad51dccf269a0604ec5dd24795b
64efb424b6/gigaword.py", line 115, in _generate_examples
with open(src_path, encoding="utf-8") as f_d, open(tgt_path, encoding="utf-8") as f_s:
File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/streaming.py", line 71, in wrapper
return function(*args, use_auth_token=use_auth_token, **kwargs)
File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/download/streaming_download_manager.py", line 493, in xope
n
return open(main_hop, mode, *args, **kwargs)
NotADirectoryError: [Errno 20] Not a directory: '/home/x/.cache/huggingface/datasets/downloads/6da52431bb5124d90cf51a0187d2dbee9046e
89780c4be7599794a4f559048ec/org_data/train.src.txt'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "gigaword.py", line 38, in <module>
main()
File "gigaword.py", line 35, in main
train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/")
File "/home/x/MICL/preprocess/fewshot_gym_dataset.py", line 199, in generate_k_shot_data
dataset = self.load_dataset()
File "gigaword.py", line 29, in load_dataset
return datasets.load_dataset('gigaword')
File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/load.py", line 1809, in load_dataset
builder_instance.download_and_prepare(
File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 909, in download_and_prepare
self._download_and_prepare(
File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1670, in _download_and_prepare
super()._download_and_prepare(
File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1004, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1508, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1665, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
```
### Expected behavior
Download and process the dataset successfully
### Environment info
- `datasets` version: 2.13.1
- Platform: Linux-5.0.0-1032-azure-x86_64-with-glibc2.10
- Python version: 3.8.0
- Huggingface_hub version: 0.15.1
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
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https://api.github.com/repos/huggingface/datasets/issues/6005 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6005/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6005/comments | https://api.github.com/repos/huggingface/datasets/issues/6005/events | https://github.com/huggingface/datasets/pull/6005 | 1,788,103,576 | PR_kwDODunzps5UoJ91 | 6,005 | Drop Python 3.7 support | {
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} | [] | closed | false | null | [] | null | 7 | "2023-07-04T15:02:37" | "2023-07-06T15:32:41" | "2023-07-06T15:22:43" | CONTRIBUTOR | null | false | {
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} | `hfh` and `transformers` have dropped Python 3.7 support, so we should do the same :).
(Based on the stats, it seems less than 10% of the users use `datasets` with Python 3.7) | {
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https://api.github.com/repos/huggingface/datasets/issues/6004 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6004/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6004/comments | https://api.github.com/repos/huggingface/datasets/issues/6004/events | https://github.com/huggingface/datasets/pull/6004 | 1,786,636,368 | PR_kwDODunzps5UjN2h | 6,004 | Misc improvements | {
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} | [] | closed | false | null | [] | null | 4 | "2023-07-03T18:29:14" | "2023-07-06T17:04:11" | "2023-07-06T16:55:25" | CONTRIBUTOR | null | false | {
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"merged_at": "2023-07-06T16:55:25"
} | Contains the following improvements:
* fixes a "share dataset" link in README and modifies the "hosting" part in the disclaimer section
* updates `Makefile` to also run the style checks on `utils` and `setup.py`
* deletes a test for GH-hosted datasets (no longer supported)
* deletes `convert_dataset.sh` (outdated)
* aligns `utils/release.py` with `transformers` (the current version is outdated) | {
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https://api.github.com/repos/huggingface/datasets/issues/6003 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6003/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6003/comments | https://api.github.com/repos/huggingface/datasets/issues/6003/events | https://github.com/huggingface/datasets/issues/6003 | 1,786,554,110 | I_kwDODunzps5qfKb- | 6,003 | interleave_datasets & DataCollatorForLanguageModeling having a conflict ? | {
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} | [] | open | false | null | [] | null | 0 | "2023-07-03T17:15:31" | "2023-07-03T17:15:31" | null | NONE | null | null | null | ### Describe the bug
Hi everyone :)
I have two local & custom datasets (1 "sentence" per line) which I split along the 95/5 lines for pre-training a Bert model. I use a modified version of `run_mlm.py` in order to be able to make use of `interleave_dataset`:
- `tokenize()` runs fine
- `group_text()` runs fine
Everytime, on step 19, I get
```pytb
File "env/lib/python3.9/site-packages/transformers/data/data_collator.py", line 779, in torch_mask_tokens
inputs[indices_random] = random_words[indices_random]
RuntimeError: Index put requires the source and destination dtypes match, got Float for the destination and Long for the source.
```
I tried:
- training without interleave on dataset 1, it runs
- training without interleave on dataset 2, it runs
- training without `.to_iterable_dataset()`, it hangs then crash
- training without group_text() and padding to max_length seemed to fix the issue, but who knows if this was just because it was an issue that would come much later in terms of steps.
I might have coded something wrong, but I don't get what
### Steps to reproduce the bug
I have this function:
```py
def build_dataset(path: str, percent: str):
dataset = load_dataset(
"text",
data_files={"train": [path]},
split=f"train[{percent}]"
)
dataset = dataset.map(
lambda examples: tokenize(examples["text"]),
batched=True,
num_proc=num_proc,
)
dataset = dataset.map(
group_texts,
batched=True,
num_proc=num_proc,
desc=f"Grouping texts in chunks of {tokenizer.max_seq_length}",
remove_columns=["text"]
)
print(len(dataset))
return dataset.to_iterable_dataset()
```
I hardcoded group_text:
```py
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, and if the total_length < max_seq_length we exclude this batch and return an empty dict.
# We could add padding if the model supported it instead of this drop, you can customize this part to your needs.
total_length = (total_length // 512) * 512
# Split by chunks of max_len.
result = {
k: [t[i: i + 512] for i in range(0, total_length, 512)]
for k, t in concatenated_examples.items()
}
# result = {k: [el for el in elements if el] for k, elements in result.items()}
return result
```
And then I build datasets using the following code:
```py
train1 = build_dataset("d1.txt", ":95%")
train2 = build_dataset("d2.txt", ":95%")
dev1 = build_dataset("d1.txt", "95%:")
dev2 = build_dataset("d2.txt", "95%:")
```
and finally I run
```py
train_dataset = interleave_datasets(
[train1, train2],
probabilities=[0.8, 0.2],
seed=42
)
eval_dataset = interleave_datasets(
[dev1, dev2],
probabilities=[0.8, 0.2],
seed=42
)
```
Then I run the training part which remains mostly untouched:
> CUDA_VISIBLE_DEVICES=1 python custom_dataset.py --model_type bert --per_device_train_batch_size 32 --do_train --output_dir /var/mlm/training-bert/model --max_seq_length 512 --save_steps 10000 --save_total_limit 3 --auto_find_batch_size --logging_dir ./logs-bert --learning_rate 0.0001 --do_train --num_train_epochs 25 --warmup_steps 10000 --max_step 45000 --fp16
### Expected behavior
The model should then train normally, but fails every time at the same step (19).
printing the variables at `inputs[indices_random] = random_words[indices_random]` shows a magnificient empty tensor (, 32) [if I remember well]
### Environment info
transformers[torch] 4.30.2
Ubuntu
A100 0 CUDA 12
Driver Version: 525.116.04 | {
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https://api.github.com/repos/huggingface/datasets/issues/6002 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6002/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6002/comments | https://api.github.com/repos/huggingface/datasets/issues/6002/events | https://github.com/huggingface/datasets/pull/6002 | 1,786,053,060 | PR_kwDODunzps5UhP-Z | 6,002 | Add KLUE-MRC metrics | {
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} | ## Metrics for KLUE-MRC (Korean Language Understanding Evaluation — Machine Reading Comprehension)
Adding metrics for [KLUE-MRC](https://huggingface.co/datasets/klue).
KLUE-MRC is very similar to SQuAD 2.0 but has a slightly different format which is why I added metrics for KLUE-MRC.
Specifically, in the case of [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness), it leverages the scoring script of SQuAD to evaluate SQuAD 2.0 and KorQuAD. But the script isn't suitable for KLUE-MRC because KLUE-MRC is a bit different from SQuAD 2.0. And this is why I added the scoring script for KLUE-MRC.
- [x] All tests passed
- [x] Added a metric card (referred the metric card of SQuAD 2.0)
- [x] Compatibility test with [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness) passed
### References
- [KLUE: Korean Language Understanding Evaluation](https://datasets-benchmarks-proceedings.neurips.cc/paper_files/paper/2021/file/98dce83da57b0395e163467c9dae521b-Paper-round2.pdf)
- [KLUE on Hugging Face Datasets](https://huggingface.co/datasets/klue)
- #2416 | {
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} | `joblibspark` doesn't support the latest `joblib` release.
See https://github.com/huggingface/datasets/actions/runs/5401870932/jobs/9812337078 for the errors | {
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https://api.github.com/repos/huggingface/datasets/issues/5999 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/5999/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/5999/comments | https://api.github.com/repos/huggingface/datasets/issues/5999/events | https://github.com/huggingface/datasets/issues/5999 | 1,781,851,513 | I_kwDODunzps5qNOV5 | 5,999 | Getting a 409 error while loading xglue dataset | {
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] | null | 1 | "2023-06-30T04:13:54" | "2023-06-30T05:57:23" | "2023-06-30T05:57:22" | NONE | null | null | null | ### Describe the bug
Unable to load xglue dataset
### Steps to reproduce the bug
```python
import datasets
dataset = datasets.load_dataset("xglue", "ntg")
```
> ConnectionError: Couldn't reach https://xglue.blob.core.windows.net/xglue/xglue_full_dataset.tar.gz (error 409)
### Expected behavior
Expected the dataset to load
### Environment info
- `datasets` version: 2.13.1
- Platform: Linux-5.15.107+-x86_64-with-glibc2.31
- Python version: 3.10.12
- Huggingface_hub version: 0.15.1
- PyArrow version: 9.0.0
- Pandas version: 1.5.3 | {
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In the sort method，here's a piece of code
```python
# column_names: Union[str, Sequence_[str]]
# Check proper format of and for duplicates in column_names
if not isinstance(column_names, list):
column_names = [column_names]
```
I get an error when I pass in a tuple based on the column_names type annotation, it will raise an errror.As in the example below, while the type annotation implies that a tuple can be passed.
```python
from datasets import load_dataset
dataset = load_dataset('glue', 'ax')['test']
dataset.sort(column_names=('premise', 'hypothesis'))
# Raise ValueError: Column '('premise', 'hypothesis')' not found in the dataset.
```
Of course, after I modified the tuple into a list, everything worked fine
Change the code to the following so there will be no problem
```python
# Check proper format of and for duplicates in column_names
if not isinstance(column_names, list):
if isinstance(column_names, str):
column_names = [column_names]
else:
column_names = list(column_names)
```
### Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset('glue', 'ax')['test']
dataset.sort(column_names=('premise', 'hypothesis'))
# Raise ValueError: Column '('premise', 'hypothesis')' not found in the dataset.
```
### Expected behavior
Passing tuple into column_names should be equivalent to passing list
### Environment info
- `datasets` version: 2.13.0
- Platform: macOS-13.1-arm64-arm-64bit
- Python version: 3.10.11
- Huggingface_hub version: 0.15.1
- PyArrow version: 12.0.1
- Pandas version: 2.0.2 | {
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] | open | false | null | [] | null | 2 | "2023-06-29T22:15:21" | "2023-07-03T17:58:52" | null | NONE | null | null | null | ### Feature request
I understand `dataset` provides a [`map`](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_dataset.py#L2849) function. This function in turn takes in a callable that is used to tokenize the text on which a model is trained. Frequently this text will not fit within a models's context window. In this case it would be useful to wrap around the text into multiple rows with each row fitting the model's context window. I tried to do it using this code as example which in turn I have borrowed from [here](https://stackoverflow.com/a/76343993/147530):
```
data = data.map(lambda samples: tokenizer(samples["text"], max_length=tokenizer.model_max_length, truncation=True, stride=4, return_overflowing_tokens=True), batched=True)
```
but running the code gives me this error:
```
File "/llm/fine-tune.py", line 117, in <module>
data = data.map(lambda samples: tokenizer(samples["text"], max_length=tokenizer.model_max_length, truncation=True, stride=4, return_overflowing_tokens=True), batched=True)
File "/llm/.env/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 580, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/llm/.env/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 545, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/llm/.env/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3087, in map
for rank, done, content in Dataset._map_single(**dataset_kwargs):
File "/llm/.env/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3480, in _map_single
writer.write_batch(batch)
File "/llm/.env/lib/python3.9/site-packages/datasets/arrow_writer.py", line 556, in write_batch
pa_table = pa.Table.from_arrays(arrays, schema=schema)
File "pyarrow/table.pxi", line 3798, in pyarrow.lib.Table.from_arrays
File "pyarrow/table.pxi", line 2962, in pyarrow.lib.Table.validate
File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Column 1 named input_ids expected length 394 but got length 447
```
The lambda function I have provided is correctly chopping up long text so it wraps around (and because of this 394 samples become 447 after wrap around) but the dataset `map` function does not like it.
### Motivation
please see above
### Your contribution
I'm afraid I don't have much knowledge to help | {
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} | https://api.github.com/repos/huggingface/datasets/issues/5997/timeline | null | null | false |
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