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https://github.com/huggingface/datasets/issues/2820 | Downloading “reddit” dataset keeps timing out. | Oh, then could you try deleting the parent directory `1ec12301abba4daa60eb3a90e53529b5b173296b22dc3bef3186e205c75e594c` instead ?
This way the download manager will know that it has to uncompress the data again | ## Describe the bug
A clear and concise description of what the bug is.
Everytime I try and download the reddit dataset it times out before finishing and I have to try again.
There is some timeout error that I will post once it happens again.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("reddit", ignore_verifications=True, cache_dir="/Volumes/My Passport for Mac/og-chat-data")
```
## Expected results
A clear and concise description of the expected results.
I would expect the download to finish, or at least provide a parameter to extend the read timeout window.
## Actual results
Specify the actual results or traceback.
Shown below in error message.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: macOS
- Python version: 3.9.6 (conda env)
- PyArrow version: N/A
| 27 | Downloading “reddit” dataset keeps timing out.
## Describe the bug
A clear and concise description of what the bug is.
Everytime I try and download the reddit dataset it times out before finishing and I have to try again.
There is some timeout error that I will post once it happens again.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("reddit", ignore_verifications=True, cache_dir="/Volumes/My Passport for Mac/og-chat-data")
```
## Expected results
A clear and concise description of the expected results.
I would expect the download to finish, or at least provide a parameter to extend the read timeout window.
## Actual results
Specify the actual results or traceback.
Shown below in error message.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: macOS
- Python version: 3.9.6 (conda env)
- PyArrow version: N/A
Oh, then could you try deleting the parent directory `1ec12301abba4daa60eb3a90e53529b5b173296b22dc3bef3186e205c75e594c` instead ?
This way the download manager will know that it has to uncompress the data again | [
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https://github.com/huggingface/datasets/issues/2820 | Downloading “reddit” dataset keeps timing out. | It seems to have worked. It only took like 20min! I think the extra timeout length did the trick! One thing is that it downloaded a total of 41gb instead of 20gb but at least it finished. | ## Describe the bug
A clear and concise description of what the bug is.
Everytime I try and download the reddit dataset it times out before finishing and I have to try again.
There is some timeout error that I will post once it happens again.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("reddit", ignore_verifications=True, cache_dir="/Volumes/My Passport for Mac/og-chat-data")
```
## Expected results
A clear and concise description of the expected results.
I would expect the download to finish, or at least provide a parameter to extend the read timeout window.
## Actual results
Specify the actual results or traceback.
Shown below in error message.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: macOS
- Python version: 3.9.6 (conda env)
- PyArrow version: N/A
| 37 | Downloading “reddit” dataset keeps timing out.
## Describe the bug
A clear and concise description of what the bug is.
Everytime I try and download the reddit dataset it times out before finishing and I have to try again.
There is some timeout error that I will post once it happens again.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("reddit", ignore_verifications=True, cache_dir="/Volumes/My Passport for Mac/og-chat-data")
```
## Expected results
A clear and concise description of the expected results.
I would expect the download to finish, or at least provide a parameter to extend the read timeout window.
## Actual results
Specify the actual results or traceback.
Shown below in error message.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: macOS
- Python version: 3.9.6 (conda env)
- PyArrow version: N/A
It seems to have worked. It only took like 20min! I think the extra timeout length did the trick! One thing is that it downloaded a total of 41gb instead of 20gb but at least it finished. | [
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https://github.com/huggingface/datasets/issues/2818 | cannot load data from my loacal path | Hi ! The `data_files` parameter must be a string, a list/tuple or a python dict.
Can you check the type of your `config.train_path` please ? Or use `data_files=str(config.train_path)` ? | ## Describe the bug
I just want to directly load data from my local path,but find a bug.And I compare it with pandas to provide my local path is real.
here is my code
```python3
# print my local path
print(config.train_path)
# read data and print data length
tarin=pd.read_csv(config.train_path)
print(len(tarin))
# loading data by load_dataset
data = load_dataset('csv',data_files=config.train_path)
print(len(data))
```
## Steps to reproduce the bug
```python
C:\Users\wie\Documents\项目\文本分类\data\train.csv
7613
Traceback (most recent call last):
File "c:/Users/wie/Documents/项目/文本分类/lib/DataPrecess.py", line 17, in <module>
data = load_dataset('csv',data_files=config.train_path)
File "C:\Users\wie\Miniconda3\lib\site-packages\datasets\load.py", line 830, in load_dataset
**config_kwargs,
File "C:\Users\wie\Miniconda3\lib\site-packages\datasets\load.py", line 710, in load_dataset_builder
**config_kwargs,
File "C:\Users\wie\Miniconda3\lib\site-packages\datasets\builder.py", line 271, in __init__
**config_kwargs,
File "C:\Users\wie\Miniconda3\lib\site-packages\datasets\builder.py", line 386, in _create_builder_config
config_kwargs, custom_features=custom_features, use_auth_token=self.use_auth_token
File "C:\Users\wie\Miniconda3\lib\site-packages\datasets\builder.py", line 156, in create_config_id
raise ValueError("Please provide a valid `data_files` in `DatasetBuilder`")
ValueError: Please provide a valid `data_files` in `DatasetBuilder`
```
## Expected results
A clear and concise description of the expected results.
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: win10
- Python version: 3.7.9
- PyArrow version: 5.0.0
| 29 | cannot load data from my loacal path
## Describe the bug
I just want to directly load data from my local path,but find a bug.And I compare it with pandas to provide my local path is real.
here is my code
```python3
# print my local path
print(config.train_path)
# read data and print data length
tarin=pd.read_csv(config.train_path)
print(len(tarin))
# loading data by load_dataset
data = load_dataset('csv',data_files=config.train_path)
print(len(data))
```
## Steps to reproduce the bug
```python
C:\Users\wie\Documents\项目\文本分类\data\train.csv
7613
Traceback (most recent call last):
File "c:/Users/wie/Documents/项目/文本分类/lib/DataPrecess.py", line 17, in <module>
data = load_dataset('csv',data_files=config.train_path)
File "C:\Users\wie\Miniconda3\lib\site-packages\datasets\load.py", line 830, in load_dataset
**config_kwargs,
File "C:\Users\wie\Miniconda3\lib\site-packages\datasets\load.py", line 710, in load_dataset_builder
**config_kwargs,
File "C:\Users\wie\Miniconda3\lib\site-packages\datasets\builder.py", line 271, in __init__
**config_kwargs,
File "C:\Users\wie\Miniconda3\lib\site-packages\datasets\builder.py", line 386, in _create_builder_config
config_kwargs, custom_features=custom_features, use_auth_token=self.use_auth_token
File "C:\Users\wie\Miniconda3\lib\site-packages\datasets\builder.py", line 156, in create_config_id
raise ValueError("Please provide a valid `data_files` in `DatasetBuilder`")
ValueError: Please provide a valid `data_files` in `DatasetBuilder`
```
## Expected results
A clear and concise description of the expected results.
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: win10
- Python version: 3.7.9
- PyArrow version: 5.0.0
Hi ! The `data_files` parameter must be a string, a list/tuple or a python dict.
Can you check the type of your `config.train_path` please ? Or use `data_files=str(config.train_path)` ? | [
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https://github.com/huggingface/datasets/issues/2813 | Remove compression from xopen | After discussing with @lhoestq, a reasonable alternative:
- `download_manager.extract(urlpath)` adds prefixes to `urlpath` in the same way as `fsspec` does for protocols, but we implement custom prefixes for all compression formats:
`bz2::http://domain.org/filename.bz2`
- `xopen` parses the `urlpath` and extracts the `compression` parameter and passes it to `fsspec.open`:
`fsspec.open("http://domain.org/filename.bz2", compression="bz2")`
Pros:
- clean solution that continues giving support to all compression formats
- no breaking change when opening non-decompressed files: if no compression-protocol-like is passed, fsspec.open does not uncompress (passes compression=None)
Cons:
- we create a "private" convention for the format of `urlpath`: although similar to `fsspec` protocols, we add custom prefixes for the `compression` argument | We implemented support for streaming with 2 requirements:
- transparent use for the end user: just needs to pass the parameter `streaming=True`
- no additional work for the contributors: previous loading scripts should also work in streaming mode with no (or minor) changes; and new loading scripts should not involve additional code to support streaming
In order to fulfill these requirements, streaming implementation patched some Python functions:
- the `open(urlpath)` function was patched with `fsspec.open(urlpath)`
- the `os.path.join(urlpath, *others)` function was patched in order to add to `urlpath` hops (`::`) and extractor protocols (`zip://`), which are required by `fsspec.open`
Recently, we implemented support for streaming all archive+compression formats: zip, tar, gz, bz2, lz4, xz, zst; tar.gz, tar.bz2,...
Under the hood, the implementation:
- passes an additional parameter `compression` to `fsspec.open`, so that it performs the decompression on the fly: `fsspec.open(urlpath, compression=...)`
Some concerns have been raised about passing the parameter `compression` to `fsspec.open`:
- https://github.com/huggingface/datasets/pull/2786#discussion_r689550254
- #2811
The main argument is that if `open` decompresses the file and afterwards we call `gzip.open` on it, that will raise an error in `oscar` dataset:
```python
gzip.open(open(urlpath
```
While this is true:
- it is not natural/usual to call `open` inside `gzip.open` (never seen this before)
- indeed, this was recently (2 months ago) coded that way in `datasets` in order to allow streaming support (with previous implementation of streaming)
In this particular case, there is a natural fix solution: #2811:
- Revert the `open` inside the `gzip.open` (change done 2 months ago): `gzip.open(open(urlpath` => `gzip.open(urlpath`
- Patch `gzip.open(urlpath` with `fsspec.open(urlpath, compression="gzip"`
Are there other issues apart from this?
Note that there is an issue just because the open inside of the gzip.open. There is no issue in the other cases where datasets loading scripts use just
- `gzip.open`
- `open` (after having called dl_manager.download_and_extract)
TODO:
- [ ] Is this really an issue? Please enumerate the `datasets` loading scripts where this is problematic.
- For the moment, there are only 3 datasets where we have an `open` inside a `gzip.open`:
- oscar (since 23 June), mc4 (since 2 July) and c4 (since 2 July)
- In the 3 datasets, the only reason to put an open inside a gzip.open was indeed to force supporting streaming
- [ ] If this is indeed an issue, which are the possible alternatives? Pros/cons? | 105 | Remove compression from xopen
We implemented support for streaming with 2 requirements:
- transparent use for the end user: just needs to pass the parameter `streaming=True`
- no additional work for the contributors: previous loading scripts should also work in streaming mode with no (or minor) changes; and new loading scripts should not involve additional code to support streaming
In order to fulfill these requirements, streaming implementation patched some Python functions:
- the `open(urlpath)` function was patched with `fsspec.open(urlpath)`
- the `os.path.join(urlpath, *others)` function was patched in order to add to `urlpath` hops (`::`) and extractor protocols (`zip://`), which are required by `fsspec.open`
Recently, we implemented support for streaming all archive+compression formats: zip, tar, gz, bz2, lz4, xz, zst; tar.gz, tar.bz2,...
Under the hood, the implementation:
- passes an additional parameter `compression` to `fsspec.open`, so that it performs the decompression on the fly: `fsspec.open(urlpath, compression=...)`
Some concerns have been raised about passing the parameter `compression` to `fsspec.open`:
- https://github.com/huggingface/datasets/pull/2786#discussion_r689550254
- #2811
The main argument is that if `open` decompresses the file and afterwards we call `gzip.open` on it, that will raise an error in `oscar` dataset:
```python
gzip.open(open(urlpath
```
While this is true:
- it is not natural/usual to call `open` inside `gzip.open` (never seen this before)
- indeed, this was recently (2 months ago) coded that way in `datasets` in order to allow streaming support (with previous implementation of streaming)
In this particular case, there is a natural fix solution: #2811:
- Revert the `open` inside the `gzip.open` (change done 2 months ago): `gzip.open(open(urlpath` => `gzip.open(urlpath`
- Patch `gzip.open(urlpath` with `fsspec.open(urlpath, compression="gzip"`
Are there other issues apart from this?
Note that there is an issue just because the open inside of the gzip.open. There is no issue in the other cases where datasets loading scripts use just
- `gzip.open`
- `open` (after having called dl_manager.download_and_extract)
TODO:
- [ ] Is this really an issue? Please enumerate the `datasets` loading scripts where this is problematic.
- For the moment, there are only 3 datasets where we have an `open` inside a `gzip.open`:
- oscar (since 23 June), mc4 (since 2 July) and c4 (since 2 July)
- In the 3 datasets, the only reason to put an open inside a gzip.open was indeed to force supporting streaming
- [ ] If this is indeed an issue, which are the possible alternatives? Pros/cons?
After discussing with @lhoestq, a reasonable alternative:
- `download_manager.extract(urlpath)` adds prefixes to `urlpath` in the same way as `fsspec` does for protocols, but we implement custom prefixes for all compression formats:
`bz2::http://domain.org/filename.bz2`
- `xopen` parses the `urlpath` and extracts the `compression` parameter and passes it to `fsspec.open`:
`fsspec.open("http://domain.org/filename.bz2", compression="bz2")`
Pros:
- clean solution that continues giving support to all compression formats
- no breaking change when opening non-decompressed files: if no compression-protocol-like is passed, fsspec.open does not uncompress (passes compression=None)
Cons:
- we create a "private" convention for the format of `urlpath`: although similar to `fsspec` protocols, we add custom prefixes for the `compression` argument | [
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https://github.com/huggingface/datasets/issues/2799 | Loading JSON throws ArrowNotImplementedError | Hi @lewtun, thanks for reporting.
Apparently, `pyarrow.json` tries to cast timestamp-like fields in your JSON file to pyarrow timestamp type, and it fails with `ArrowNotImplementedError`.
I will investigate if there is a way to tell pyarrow not to try that timestamp casting. | ## Describe the bug
I have created a [dataset](https://huggingface.co/datasets/lewtun/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).
Curiously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.
You can find a Colab notebook which reproduces the error [here](https://colab.research.google.com/drive/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).
**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)
## Steps to reproduce the bug
```python
from datasets import load_dataset
from huggingface_hub import hf_hub_url
import pandas as pd
# returns https://huggingface.co/datasets/lewtun/github-issues-test/resolve/main/issues-datasets.jsonl
data_files = hf_hub_url(repo_id="lewtun/github-issues-test", filename="issues-datasets.jsonl", repo_type="dataset")
# throws ArrowNotImplementedError
dset = load_dataset("json", data_files=data_files, split="test")
# no problem with pandas ...
df = pd.read_json(data_files, orient="records", lines=True)
df.head()
```
## Expected results
I can load any line-separated JSON file, similar to `pandas`.
## Actual results
```
---------------------------------------------------------------------------
ArrowNotImplementedError Traceback (most recent call last)
<ipython-input-7-5b8e82b6c3a2> in <module>()
----> 1 dset = load_dataset("json", data_files=data_files, split="test")
9 frames
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.11
- PyArrow version: 3.0.0
| 42 | Loading JSON throws ArrowNotImplementedError
## Describe the bug
I have created a [dataset](https://huggingface.co/datasets/lewtun/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).
Curiously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.
You can find a Colab notebook which reproduces the error [here](https://colab.research.google.com/drive/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).
**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)
## Steps to reproduce the bug
```python
from datasets import load_dataset
from huggingface_hub import hf_hub_url
import pandas as pd
# returns https://huggingface.co/datasets/lewtun/github-issues-test/resolve/main/issues-datasets.jsonl
data_files = hf_hub_url(repo_id="lewtun/github-issues-test", filename="issues-datasets.jsonl", repo_type="dataset")
# throws ArrowNotImplementedError
dset = load_dataset("json", data_files=data_files, split="test")
# no problem with pandas ...
df = pd.read_json(data_files, orient="records", lines=True)
df.head()
```
## Expected results
I can load any line-separated JSON file, similar to `pandas`.
## Actual results
```
---------------------------------------------------------------------------
ArrowNotImplementedError Traceback (most recent call last)
<ipython-input-7-5b8e82b6c3a2> in <module>()
----> 1 dset = load_dataset("json", data_files=data_files, split="test")
9 frames
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.11
- PyArrow version: 3.0.0
Hi @lewtun, thanks for reporting.
Apparently, `pyarrow.json` tries to cast timestamp-like fields in your JSON file to pyarrow timestamp type, and it fails with `ArrowNotImplementedError`.
I will investigate if there is a way to tell pyarrow not to try that timestamp casting. | [
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https://github.com/huggingface/datasets/issues/2799 | Loading JSON throws ArrowNotImplementedError | I think the issue is more complex than that...
I just took one of your JSON lines and pyarrow.json read it without problem. | ## Describe the bug
I have created a [dataset](https://huggingface.co/datasets/lewtun/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).
Curiously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.
You can find a Colab notebook which reproduces the error [here](https://colab.research.google.com/drive/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).
**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)
## Steps to reproduce the bug
```python
from datasets import load_dataset
from huggingface_hub import hf_hub_url
import pandas as pd
# returns https://huggingface.co/datasets/lewtun/github-issues-test/resolve/main/issues-datasets.jsonl
data_files = hf_hub_url(repo_id="lewtun/github-issues-test", filename="issues-datasets.jsonl", repo_type="dataset")
# throws ArrowNotImplementedError
dset = load_dataset("json", data_files=data_files, split="test")
# no problem with pandas ...
df = pd.read_json(data_files, orient="records", lines=True)
df.head()
```
## Expected results
I can load any line-separated JSON file, similar to `pandas`.
## Actual results
```
---------------------------------------------------------------------------
ArrowNotImplementedError Traceback (most recent call last)
<ipython-input-7-5b8e82b6c3a2> in <module>()
----> 1 dset = load_dataset("json", data_files=data_files, split="test")
9 frames
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.11
- PyArrow version: 3.0.0
| 23 | Loading JSON throws ArrowNotImplementedError
## Describe the bug
I have created a [dataset](https://huggingface.co/datasets/lewtun/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).
Curiously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.
You can find a Colab notebook which reproduces the error [here](https://colab.research.google.com/drive/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).
**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)
## Steps to reproduce the bug
```python
from datasets import load_dataset
from huggingface_hub import hf_hub_url
import pandas as pd
# returns https://huggingface.co/datasets/lewtun/github-issues-test/resolve/main/issues-datasets.jsonl
data_files = hf_hub_url(repo_id="lewtun/github-issues-test", filename="issues-datasets.jsonl", repo_type="dataset")
# throws ArrowNotImplementedError
dset = load_dataset("json", data_files=data_files, split="test")
# no problem with pandas ...
df = pd.read_json(data_files, orient="records", lines=True)
df.head()
```
## Expected results
I can load any line-separated JSON file, similar to `pandas`.
## Actual results
```
---------------------------------------------------------------------------
ArrowNotImplementedError Traceback (most recent call last)
<ipython-input-7-5b8e82b6c3a2> in <module>()
----> 1 dset = load_dataset("json", data_files=data_files, split="test")
9 frames
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.11
- PyArrow version: 3.0.0
I think the issue is more complex than that...
I just took one of your JSON lines and pyarrow.json read it without problem. | [
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https://github.com/huggingface/datasets/issues/2799 | Loading JSON throws ArrowNotImplementedError | > I just took one of your JSON lines an pyarrow.json read it without problem.
yes, and for some peculiar reason the error is non-deterministic (i was eventually able to load the whole dataset by just re-running the `load_dataset` cell multiple times 🤔)
thanks for looking into this 🙏 ! | ## Describe the bug
I have created a [dataset](https://huggingface.co/datasets/lewtun/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).
Curiously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.
You can find a Colab notebook which reproduces the error [here](https://colab.research.google.com/drive/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).
**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)
## Steps to reproduce the bug
```python
from datasets import load_dataset
from huggingface_hub import hf_hub_url
import pandas as pd
# returns https://huggingface.co/datasets/lewtun/github-issues-test/resolve/main/issues-datasets.jsonl
data_files = hf_hub_url(repo_id="lewtun/github-issues-test", filename="issues-datasets.jsonl", repo_type="dataset")
# throws ArrowNotImplementedError
dset = load_dataset("json", data_files=data_files, split="test")
# no problem with pandas ...
df = pd.read_json(data_files, orient="records", lines=True)
df.head()
```
## Expected results
I can load any line-separated JSON file, similar to `pandas`.
## Actual results
```
---------------------------------------------------------------------------
ArrowNotImplementedError Traceback (most recent call last)
<ipython-input-7-5b8e82b6c3a2> in <module>()
----> 1 dset = load_dataset("json", data_files=data_files, split="test")
9 frames
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.11
- PyArrow version: 3.0.0
| 50 | Loading JSON throws ArrowNotImplementedError
## Describe the bug
I have created a [dataset](https://huggingface.co/datasets/lewtun/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).
Curiously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.
You can find a Colab notebook which reproduces the error [here](https://colab.research.google.com/drive/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).
**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)
## Steps to reproduce the bug
```python
from datasets import load_dataset
from huggingface_hub import hf_hub_url
import pandas as pd
# returns https://huggingface.co/datasets/lewtun/github-issues-test/resolve/main/issues-datasets.jsonl
data_files = hf_hub_url(repo_id="lewtun/github-issues-test", filename="issues-datasets.jsonl", repo_type="dataset")
# throws ArrowNotImplementedError
dset = load_dataset("json", data_files=data_files, split="test")
# no problem with pandas ...
df = pd.read_json(data_files, orient="records", lines=True)
df.head()
```
## Expected results
I can load any line-separated JSON file, similar to `pandas`.
## Actual results
```
---------------------------------------------------------------------------
ArrowNotImplementedError Traceback (most recent call last)
<ipython-input-7-5b8e82b6c3a2> in <module>()
----> 1 dset = load_dataset("json", data_files=data_files, split="test")
9 frames
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.11
- PyArrow version: 3.0.0
> I just took one of your JSON lines an pyarrow.json read it without problem.
yes, and for some peculiar reason the error is non-deterministic (i was eventually able to load the whole dataset by just re-running the `load_dataset` cell multiple times 🤔)
thanks for looking into this 🙏 ! | [
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https://github.com/huggingface/datasets/issues/2799 | Loading JSON throws ArrowNotImplementedError | The code works fine on my side.
Not sure what's going on here :/
I remember we did a few changes in the JSON loader in #2638 , did you do an update `datasets` when debugging this ?
| ## Describe the bug
I have created a [dataset](https://huggingface.co/datasets/lewtun/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).
Curiously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.
You can find a Colab notebook which reproduces the error [here](https://colab.research.google.com/drive/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).
**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)
## Steps to reproduce the bug
```python
from datasets import load_dataset
from huggingface_hub import hf_hub_url
import pandas as pd
# returns https://huggingface.co/datasets/lewtun/github-issues-test/resolve/main/issues-datasets.jsonl
data_files = hf_hub_url(repo_id="lewtun/github-issues-test", filename="issues-datasets.jsonl", repo_type="dataset")
# throws ArrowNotImplementedError
dset = load_dataset("json", data_files=data_files, split="test")
# no problem with pandas ...
df = pd.read_json(data_files, orient="records", lines=True)
df.head()
```
## Expected results
I can load any line-separated JSON file, similar to `pandas`.
## Actual results
```
---------------------------------------------------------------------------
ArrowNotImplementedError Traceback (most recent call last)
<ipython-input-7-5b8e82b6c3a2> in <module>()
----> 1 dset = load_dataset("json", data_files=data_files, split="test")
9 frames
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.11
- PyArrow version: 3.0.0
| 38 | Loading JSON throws ArrowNotImplementedError
## Describe the bug
I have created a [dataset](https://huggingface.co/datasets/lewtun/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).
Curiously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.
You can find a Colab notebook which reproduces the error [here](https://colab.research.google.com/drive/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).
**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)
## Steps to reproduce the bug
```python
from datasets import load_dataset
from huggingface_hub import hf_hub_url
import pandas as pd
# returns https://huggingface.co/datasets/lewtun/github-issues-test/resolve/main/issues-datasets.jsonl
data_files = hf_hub_url(repo_id="lewtun/github-issues-test", filename="issues-datasets.jsonl", repo_type="dataset")
# throws ArrowNotImplementedError
dset = load_dataset("json", data_files=data_files, split="test")
# no problem with pandas ...
df = pd.read_json(data_files, orient="records", lines=True)
df.head()
```
## Expected results
I can load any line-separated JSON file, similar to `pandas`.
## Actual results
```
---------------------------------------------------------------------------
ArrowNotImplementedError Traceback (most recent call last)
<ipython-input-7-5b8e82b6c3a2> in <module>()
----> 1 dset = load_dataset("json", data_files=data_files, split="test")
9 frames
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.11
- PyArrow version: 3.0.0
The code works fine on my side.
Not sure what's going on here :/
I remember we did a few changes in the JSON loader in #2638 , did you do an update `datasets` when debugging this ?
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https://github.com/huggingface/datasets/issues/2799 | Loading JSON throws ArrowNotImplementedError | OK after upgrading `datasets` to v1.12.1 the issue seems to have gone away. Closing this now :) | ## Describe the bug
I have created a [dataset](https://huggingface.co/datasets/lewtun/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).
Curiously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.
You can find a Colab notebook which reproduces the error [here](https://colab.research.google.com/drive/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).
**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)
## Steps to reproduce the bug
```python
from datasets import load_dataset
from huggingface_hub import hf_hub_url
import pandas as pd
# returns https://huggingface.co/datasets/lewtun/github-issues-test/resolve/main/issues-datasets.jsonl
data_files = hf_hub_url(repo_id="lewtun/github-issues-test", filename="issues-datasets.jsonl", repo_type="dataset")
# throws ArrowNotImplementedError
dset = load_dataset("json", data_files=data_files, split="test")
# no problem with pandas ...
df = pd.read_json(data_files, orient="records", lines=True)
df.head()
```
## Expected results
I can load any line-separated JSON file, similar to `pandas`.
## Actual results
```
---------------------------------------------------------------------------
ArrowNotImplementedError Traceback (most recent call last)
<ipython-input-7-5b8e82b6c3a2> in <module>()
----> 1 dset = load_dataset("json", data_files=data_files, split="test")
9 frames
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.11
- PyArrow version: 3.0.0
| 17 | Loading JSON throws ArrowNotImplementedError
## Describe the bug
I have created a [dataset](https://huggingface.co/datasets/lewtun/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).
Curiously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.
You can find a Colab notebook which reproduces the error [here](https://colab.research.google.com/drive/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).
**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)
## Steps to reproduce the bug
```python
from datasets import load_dataset
from huggingface_hub import hf_hub_url
import pandas as pd
# returns https://huggingface.co/datasets/lewtun/github-issues-test/resolve/main/issues-datasets.jsonl
data_files = hf_hub_url(repo_id="lewtun/github-issues-test", filename="issues-datasets.jsonl", repo_type="dataset")
# throws ArrowNotImplementedError
dset = load_dataset("json", data_files=data_files, split="test")
# no problem with pandas ...
df = pd.read_json(data_files, orient="records", lines=True)
df.head()
```
## Expected results
I can load any line-separated JSON file, similar to `pandas`.
## Actual results
```
---------------------------------------------------------------------------
ArrowNotImplementedError Traceback (most recent call last)
<ipython-input-7-5b8e82b6c3a2> in <module>()
----> 1 dset = load_dataset("json", data_files=data_files, split="test")
9 frames
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.11
- PyArrow version: 3.0.0
OK after upgrading `datasets` to v1.12.1 the issue seems to have gone away. Closing this now :) | [
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https://github.com/huggingface/datasets/issues/2799 | Loading JSON throws ArrowNotImplementedError | Oops, I spoke too soon 😓
After deleting the cache and trying the above code snippet again I am hitting the same error. You can also reproduce it in the Colab notebook I linked to in the issue description. | ## Describe the bug
I have created a [dataset](https://huggingface.co/datasets/lewtun/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).
Curiously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.
You can find a Colab notebook which reproduces the error [here](https://colab.research.google.com/drive/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).
**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)
## Steps to reproduce the bug
```python
from datasets import load_dataset
from huggingface_hub import hf_hub_url
import pandas as pd
# returns https://huggingface.co/datasets/lewtun/github-issues-test/resolve/main/issues-datasets.jsonl
data_files = hf_hub_url(repo_id="lewtun/github-issues-test", filename="issues-datasets.jsonl", repo_type="dataset")
# throws ArrowNotImplementedError
dset = load_dataset("json", data_files=data_files, split="test")
# no problem with pandas ...
df = pd.read_json(data_files, orient="records", lines=True)
df.head()
```
## Expected results
I can load any line-separated JSON file, similar to `pandas`.
## Actual results
```
---------------------------------------------------------------------------
ArrowNotImplementedError Traceback (most recent call last)
<ipython-input-7-5b8e82b6c3a2> in <module>()
----> 1 dset = load_dataset("json", data_files=data_files, split="test")
9 frames
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.11
- PyArrow version: 3.0.0
| 39 | Loading JSON throws ArrowNotImplementedError
## Describe the bug
I have created a [dataset](https://huggingface.co/datasets/lewtun/github-issues-test) of GitHub issues in line-separated JSON format and am finding that I cannot load it with the `json` loading script (see stack trace below).
Curiously, there is no problem loading the dataset with `pandas` which suggests some incorrect type inference is being made on the `datasets` side. For example, the stack trace indicates that some URL fields are being parsed as timestamps.
You can find a Colab notebook which reproduces the error [here](https://colab.research.google.com/drive/1YUCM0j1vx5ZrouQbYSzal6RwB4-Aoh4o?usp=sharing).
**Edit:** If one repeatedly tries to load the dataset, it _eventually_ works but I think it would still be good to understand why it fails in the first place :)
## Steps to reproduce the bug
```python
from datasets import load_dataset
from huggingface_hub import hf_hub_url
import pandas as pd
# returns https://huggingface.co/datasets/lewtun/github-issues-test/resolve/main/issues-datasets.jsonl
data_files = hf_hub_url(repo_id="lewtun/github-issues-test", filename="issues-datasets.jsonl", repo_type="dataset")
# throws ArrowNotImplementedError
dset = load_dataset("json", data_files=data_files, split="test")
# no problem with pandas ...
df = pd.read_json(data_files, orient="records", lines=True)
df.head()
```
## Expected results
I can load any line-separated JSON file, similar to `pandas`.
## Actual results
```
---------------------------------------------------------------------------
ArrowNotImplementedError Traceback (most recent call last)
<ipython-input-7-5b8e82b6c3a2> in <module>()
----> 1 dset = load_dataset("json", data_files=data_files, split="test")
9 frames
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowNotImplementedError: JSON conversion to struct<url: timestamp[s], html_url: timestamp[s], labels_url: timestamp[s], id: int64, node_id: timestamp[s], number: int64, title: timestamp[s], description: timestamp[s], creator: struct<login: timestamp[s], id: int64, node_id: timestamp[s], avatar_url: timestamp[s], gravatar_id: timestamp[s], url: timestamp[s], html_url: timestamp[s], followers_url: timestamp[s], following_url: timestamp[s], gists_url: timestamp[s], starred_url: timestamp[s], subscriptions_url: timestamp[s], organizations_url: timestamp[s], repos_url: timestamp[s], events_url: timestamp[s], received_events_url: timestamp[s], type: timestamp[s], site_admin: bool>, open_issues: int64, closed_issues: int64, state: timestamp[s], created_at: timestamp[s], updated_at: timestamp[s], due_on: timestamp[s], closed_at: timestamp[s]> is not supported
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.11
- PyArrow version: 3.0.0
Oops, I spoke too soon 😓
After deleting the cache and trying the above code snippet again I am hitting the same error. You can also reproduce it in the Colab notebook I linked to in the issue description. | [
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] |
https://github.com/huggingface/datasets/issues/2788 | How to sample every file in a list of files making up a split in a dataset when loading? | Hi ! This is not possible just with `load_dataset`.
You can do something like this instead:
```python
seed=42
data_files_dict = {
"train": [train_file1, train_file2],
"test": [test_file1, test_file2],
"val": [val_file1, val_file2]
}
dataset = datasets.load_dataset(
"csv",
data_files=data_files_dict,
).shuffle(seed=seed)
sample_dataset = {splitname: split.select(range(8)) for splitname, split in dataset.items()}
```
Another alternative is loading each file separately with `split="train[:8]"` and then use `concatenate_datasets` to merge the sample of each file. | I am loading a dataset with multiple train, test, and validation files like this:
```
data_files_dict = {
"train": [train_file1, train_file2],
"test": [test_file1, test_file2],
"val": [val_file1, val_file2]
}
dataset = datasets.load_dataset(
"csv",
data_files=data_files_dict,
split=['train[:8]', 'test[:8]', 'val[:8]']
)
```
However, this only selects the first 8 rows from train_file1, test_file1, val_file1, since they are the first files in the lists.
I'm trying to formulate a split argument that can sample from each file specified in my list of files that make up each split.
Is this type of splitting supported? If so, how can I do it? | 67 | How to sample every file in a list of files making up a split in a dataset when loading?
I am loading a dataset with multiple train, test, and validation files like this:
```
data_files_dict = {
"train": [train_file1, train_file2],
"test": [test_file1, test_file2],
"val": [val_file1, val_file2]
}
dataset = datasets.load_dataset(
"csv",
data_files=data_files_dict,
split=['train[:8]', 'test[:8]', 'val[:8]']
)
```
However, this only selects the first 8 rows from train_file1, test_file1, val_file1, since they are the first files in the lists.
I'm trying to formulate a split argument that can sample from each file specified in my list of files that make up each split.
Is this type of splitting supported? If so, how can I do it?
Hi ! This is not possible just with `load_dataset`.
You can do something like this instead:
```python
seed=42
data_files_dict = {
"train": [train_file1, train_file2],
"test": [test_file1, test_file2],
"val": [val_file1, val_file2]
}
dataset = datasets.load_dataset(
"csv",
data_files=data_files_dict,
).shuffle(seed=seed)
sample_dataset = {splitname: split.select(range(8)) for splitname, split in dataset.items()}
```
Another alternative is loading each file separately with `split="train[:8]"` and then use `concatenate_datasets` to merge the sample of each file. | [
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https://github.com/huggingface/datasets/issues/2787 | ConnectionError: Couldn't reach https://raw.githubusercontent.com | the bug code locate in ：
if data_args.task_name is not None:
# Downloading and loading a dataset from the hub.
datasets = load_dataset("glue", data_args.task_name, cache_dir=model_args.cache_dir) | Hello,
I am trying to run run_glue.py and it gives me this error -
Traceback (most recent call last):
File "E:/BERT/pytorch_hugging/transformers/examples/pytorch/text-classification/run_glue.py", line 546, in <module>
main()
File "E:/BERT/pytorch_hugging/transformers/examples/pytorch/text-classification/run_glue.py", line 250, in main
datasets = load_dataset("glue", data_args.task_name, cache_dir=model_args.cache_dir)
File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\load.py", line 718, in load_dataset
use_auth_token=use_auth_token,
File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\load.py", line 320, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\utils\file_utils.py", line 291, in cached_path
use_auth_token=download_config.use_auth_token,
File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\utils\file_utils.py", line 623, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.7.0/datasets/glue/glue.py
Trying to do python run_glue.py --model_name_or_path
bert-base-cased
--task_name
mrpc
--do_train
--do_eval
--max_seq_length
128
--per_device_train_batch_size
32
--learning_rate
2e-5
--num_train_epochs
3
--output_dir
./tmp/mrpc/
Is this something on my end? From what I can tell, this was re-fixeded by @fullyz a few months ago.
Thank you!
| 25 | ConnectionError: Couldn't reach https://raw.githubusercontent.com
Hello,
I am trying to run run_glue.py and it gives me this error -
Traceback (most recent call last):
File "E:/BERT/pytorch_hugging/transformers/examples/pytorch/text-classification/run_glue.py", line 546, in <module>
main()
File "E:/BERT/pytorch_hugging/transformers/examples/pytorch/text-classification/run_glue.py", line 250, in main
datasets = load_dataset("glue", data_args.task_name, cache_dir=model_args.cache_dir)
File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\load.py", line 718, in load_dataset
use_auth_token=use_auth_token,
File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\load.py", line 320, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\utils\file_utils.py", line 291, in cached_path
use_auth_token=download_config.use_auth_token,
File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\utils\file_utils.py", line 623, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.7.0/datasets/glue/glue.py
Trying to do python run_glue.py --model_name_or_path
bert-base-cased
--task_name
mrpc
--do_train
--do_eval
--max_seq_length
128
--per_device_train_batch_size
32
--learning_rate
2e-5
--num_train_epochs
3
--output_dir
./tmp/mrpc/
Is this something on my end? From what I can tell, this was re-fixeded by @fullyz a few months ago.
Thank you!
the bug code locate in ：
if data_args.task_name is not None:
# Downloading and loading a dataset from the hub.
datasets = load_dataset("glue", data_args.task_name, cache_dir=model_args.cache_dir) | [
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https://github.com/huggingface/datasets/issues/2787 | ConnectionError: Couldn't reach https://raw.githubusercontent.com | Hi @jinec,
From time to time we get this kind of `ConnectionError` coming from the github.com website: https://raw.githubusercontent.com
Normally, it should work if you wait a little and then retry.
Could you please confirm if the problem persists? | Hello,
I am trying to run run_glue.py and it gives me this error -
Traceback (most recent call last):
File "E:/BERT/pytorch_hugging/transformers/examples/pytorch/text-classification/run_glue.py", line 546, in <module>
main()
File "E:/BERT/pytorch_hugging/transformers/examples/pytorch/text-classification/run_glue.py", line 250, in main
datasets = load_dataset("glue", data_args.task_name, cache_dir=model_args.cache_dir)
File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\load.py", line 718, in load_dataset
use_auth_token=use_auth_token,
File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\load.py", line 320, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\utils\file_utils.py", line 291, in cached_path
use_auth_token=download_config.use_auth_token,
File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\utils\file_utils.py", line 623, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.7.0/datasets/glue/glue.py
Trying to do python run_glue.py --model_name_or_path
bert-base-cased
--task_name
mrpc
--do_train
--do_eval
--max_seq_length
128
--per_device_train_batch_size
32
--learning_rate
2e-5
--num_train_epochs
3
--output_dir
./tmp/mrpc/
Is this something on my end? From what I can tell, this was re-fixeded by @fullyz a few months ago.
Thank you!
| 38 | ConnectionError: Couldn't reach https://raw.githubusercontent.com
Hello,
I am trying to run run_glue.py and it gives me this error -
Traceback (most recent call last):
File "E:/BERT/pytorch_hugging/transformers/examples/pytorch/text-classification/run_glue.py", line 546, in <module>
main()
File "E:/BERT/pytorch_hugging/transformers/examples/pytorch/text-classification/run_glue.py", line 250, in main
datasets = load_dataset("glue", data_args.task_name, cache_dir=model_args.cache_dir)
File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\load.py", line 718, in load_dataset
use_auth_token=use_auth_token,
File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\load.py", line 320, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\utils\file_utils.py", line 291, in cached_path
use_auth_token=download_config.use_auth_token,
File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\utils\file_utils.py", line 623, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.7.0/datasets/glue/glue.py
Trying to do python run_glue.py --model_name_or_path
bert-base-cased
--task_name
mrpc
--do_train
--do_eval
--max_seq_length
128
--per_device_train_batch_size
32
--learning_rate
2e-5
--num_train_epochs
3
--output_dir
./tmp/mrpc/
Is this something on my end? From what I can tell, this was re-fixeded by @fullyz a few months ago.
Thank you!
Hi @jinec,
From time to time we get this kind of `ConnectionError` coming from the github.com website: https://raw.githubusercontent.com
Normally, it should work if you wait a little and then retry.
Could you please confirm if the problem persists? | [
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https://github.com/huggingface/datasets/issues/2787 | ConnectionError: Couldn't reach https://raw.githubusercontent.com | > I can access https://raw.githubusercontent.com/huggingface/datasets/1.7.0/datasets/glue/glue.py without problem...
I can not access https://raw.githubusercontent.com/huggingface/datasets either, I am in China | Hello,
I am trying to run run_glue.py and it gives me this error -
Traceback (most recent call last):
File "E:/BERT/pytorch_hugging/transformers/examples/pytorch/text-classification/run_glue.py", line 546, in <module>
main()
File "E:/BERT/pytorch_hugging/transformers/examples/pytorch/text-classification/run_glue.py", line 250, in main
datasets = load_dataset("glue", data_args.task_name, cache_dir=model_args.cache_dir)
File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\load.py", line 718, in load_dataset
use_auth_token=use_auth_token,
File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\load.py", line 320, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\utils\file_utils.py", line 291, in cached_path
use_auth_token=download_config.use_auth_token,
File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\utils\file_utils.py", line 623, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.7.0/datasets/glue/glue.py
Trying to do python run_glue.py --model_name_or_path
bert-base-cased
--task_name
mrpc
--do_train
--do_eval
--max_seq_length
128
--per_device_train_batch_size
32
--learning_rate
2e-5
--num_train_epochs
3
--output_dir
./tmp/mrpc/
Is this something on my end? From what I can tell, this was re-fixeded by @fullyz a few months ago.
Thank you!
| 17 | ConnectionError: Couldn't reach https://raw.githubusercontent.com
Hello,
I am trying to run run_glue.py and it gives me this error -
Traceback (most recent call last):
File "E:/BERT/pytorch_hugging/transformers/examples/pytorch/text-classification/run_glue.py", line 546, in <module>
main()
File "E:/BERT/pytorch_hugging/transformers/examples/pytorch/text-classification/run_glue.py", line 250, in main
datasets = load_dataset("glue", data_args.task_name, cache_dir=model_args.cache_dir)
File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\load.py", line 718, in load_dataset
use_auth_token=use_auth_token,
File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\load.py", line 320, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\utils\file_utils.py", line 291, in cached_path
use_auth_token=download_config.use_auth_token,
File "C:\install\Anaconda3\envs\huggingface\lib\site-packages\datasets\utils\file_utils.py", line 623, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.7.0/datasets/glue/glue.py
Trying to do python run_glue.py --model_name_or_path
bert-base-cased
--task_name
mrpc
--do_train
--do_eval
--max_seq_length
128
--per_device_train_batch_size
32
--learning_rate
2e-5
--num_train_epochs
3
--output_dir
./tmp/mrpc/
Is this something on my end? From what I can tell, this was re-fixeded by @fullyz a few months ago.
Thank you!
> I can access https://raw.githubusercontent.com/huggingface/datasets/1.7.0/datasets/glue/glue.py without problem...
I can not access https://raw.githubusercontent.com/huggingface/datasets either, I am in China | [
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https://github.com/huggingface/datasets/issues/2775 | `generate_random_fingerprint()` deterministic with 🤗Transformers' `set_seed()` | I dug into what I believe is the root of this issue and added a repro in my comment. If this is better addressed as a cross-team issue, let me know and I can open an issue in the Transformers repo | ## Describe the bug
**Update:** I dug into this to try to reproduce the underlying issue, and I believe it's that `set_seed()` from the `transformers` library makes the "random" fingerprint identical each time. I believe this is still a bug, because `datasets` is used exactly this way in `transformers` after `set_seed()` has been called, and I think that using `set_seed()` is a standard procedure to aid reproducibility. I've added more details to reproduce this below.
Hi there! I'm using my own local dataset and custom preprocessing function. My preprocessing function seems to be unpickle-able, perhaps because it is from a closure (will debug this separately). I get this warning, which is expected:
https://github.com/huggingface/datasets/blob/450b9174765374111e5c6daab0ed294bc3d9b639/src/datasets/fingerprint.py#L260-L265
However, what's not expected is that the `datasets` actually _does_ seem to cache and reuse this dataset between runs! After that line, the next thing that's logged looks like:
```text
Loading cached processed dataset at /home/xxx/.cache/huggingface/datasets/csv/default-xxx/0.0.0/xxx/cache-xxx.arrow
```
The path is exactly the same each run (e.g., last 26 runs).
This becomes a problem because I'll pass in the `--max_eval_samples` flag to the HuggingFace example script I'm running off of ([run_swag.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/multiple-choice/run_swag.py)). The fact that the cached dataset is reused means this flag gets ignored. I'll try to load 100 examples, and it will load the full cached 1,000,000.
I think that
https://github.com/huggingface/datasets/blob/450b9174765374111e5c6daab0ed294bc3d9b639/src/datasets/fingerprint.py#L248
... is actually consistent because randomness is being controlled in HuggingFace/Transformers for reproducibility. I've added a demo of this below.
## Steps to reproduce the bug
```python
# Contents of print_fingerprint.py
from transformers import set_seed
from datasets.fingerprint import generate_random_fingerprint
set_seed(42)
print(generate_random_fingerprint())
```
```bash
for i in {0..10}; do
python print_fingerprint.py
done
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
```
## Expected results
After the "random hash" warning is emitted, a random hash is generated, and no outdated cached datasets are reused.
## Actual results
After the "random hash" warning is emitted, an identical hash is generated each time, and an outdated cached dataset is reused each run.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Linux-5.8.0-1038-gcp-x86_64-with-glibc2.31
- Python version: 3.9.6
- PyArrow version: 4.0.1 | 41 | `generate_random_fingerprint()` deterministic with 🤗Transformers' `set_seed()`
## Describe the bug
**Update:** I dug into this to try to reproduce the underlying issue, and I believe it's that `set_seed()` from the `transformers` library makes the "random" fingerprint identical each time. I believe this is still a bug, because `datasets` is used exactly this way in `transformers` after `set_seed()` has been called, and I think that using `set_seed()` is a standard procedure to aid reproducibility. I've added more details to reproduce this below.
Hi there! I'm using my own local dataset and custom preprocessing function. My preprocessing function seems to be unpickle-able, perhaps because it is from a closure (will debug this separately). I get this warning, which is expected:
https://github.com/huggingface/datasets/blob/450b9174765374111e5c6daab0ed294bc3d9b639/src/datasets/fingerprint.py#L260-L265
However, what's not expected is that the `datasets` actually _does_ seem to cache and reuse this dataset between runs! After that line, the next thing that's logged looks like:
```text
Loading cached processed dataset at /home/xxx/.cache/huggingface/datasets/csv/default-xxx/0.0.0/xxx/cache-xxx.arrow
```
The path is exactly the same each run (e.g., last 26 runs).
This becomes a problem because I'll pass in the `--max_eval_samples` flag to the HuggingFace example script I'm running off of ([run_swag.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/multiple-choice/run_swag.py)). The fact that the cached dataset is reused means this flag gets ignored. I'll try to load 100 examples, and it will load the full cached 1,000,000.
I think that
https://github.com/huggingface/datasets/blob/450b9174765374111e5c6daab0ed294bc3d9b639/src/datasets/fingerprint.py#L248
... is actually consistent because randomness is being controlled in HuggingFace/Transformers for reproducibility. I've added a demo of this below.
## Steps to reproduce the bug
```python
# Contents of print_fingerprint.py
from transformers import set_seed
from datasets.fingerprint import generate_random_fingerprint
set_seed(42)
print(generate_random_fingerprint())
```
```bash
for i in {0..10}; do
python print_fingerprint.py
done
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
```
## Expected results
After the "random hash" warning is emitted, a random hash is generated, and no outdated cached datasets are reused.
## Actual results
After the "random hash" warning is emitted, an identical hash is generated each time, and an outdated cached dataset is reused each run.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Linux-5.8.0-1038-gcp-x86_64-with-glibc2.31
- Python version: 3.9.6
- PyArrow version: 4.0.1
I dug into what I believe is the root of this issue and added a repro in my comment. If this is better addressed as a cross-team issue, let me know and I can open an issue in the Transformers repo | [
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https://github.com/huggingface/datasets/issues/2775 | `generate_random_fingerprint()` deterministic with 🤗Transformers' `set_seed()` | Hi !
IMO we shouldn't try to modify `set_seed` from transformers but maybe make `datasets` have its own RNG just to generate random fingerprints.
Any opinion on this @LysandreJik ? | ## Describe the bug
**Update:** I dug into this to try to reproduce the underlying issue, and I believe it's that `set_seed()` from the `transformers` library makes the "random" fingerprint identical each time. I believe this is still a bug, because `datasets` is used exactly this way in `transformers` after `set_seed()` has been called, and I think that using `set_seed()` is a standard procedure to aid reproducibility. I've added more details to reproduce this below.
Hi there! I'm using my own local dataset and custom preprocessing function. My preprocessing function seems to be unpickle-able, perhaps because it is from a closure (will debug this separately). I get this warning, which is expected:
https://github.com/huggingface/datasets/blob/450b9174765374111e5c6daab0ed294bc3d9b639/src/datasets/fingerprint.py#L260-L265
However, what's not expected is that the `datasets` actually _does_ seem to cache and reuse this dataset between runs! After that line, the next thing that's logged looks like:
```text
Loading cached processed dataset at /home/xxx/.cache/huggingface/datasets/csv/default-xxx/0.0.0/xxx/cache-xxx.arrow
```
The path is exactly the same each run (e.g., last 26 runs).
This becomes a problem because I'll pass in the `--max_eval_samples` flag to the HuggingFace example script I'm running off of ([run_swag.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/multiple-choice/run_swag.py)). The fact that the cached dataset is reused means this flag gets ignored. I'll try to load 100 examples, and it will load the full cached 1,000,000.
I think that
https://github.com/huggingface/datasets/blob/450b9174765374111e5c6daab0ed294bc3d9b639/src/datasets/fingerprint.py#L248
... is actually consistent because randomness is being controlled in HuggingFace/Transformers for reproducibility. I've added a demo of this below.
## Steps to reproduce the bug
```python
# Contents of print_fingerprint.py
from transformers import set_seed
from datasets.fingerprint import generate_random_fingerprint
set_seed(42)
print(generate_random_fingerprint())
```
```bash
for i in {0..10}; do
python print_fingerprint.py
done
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
1c80317fa3b1799d
```
## Expected results
After the "random hash" warning is emitted, a random hash is generated, and no outdated cached datasets are reused.
## Actual results
After the "random hash" warning is emitted, an identical hash is generated each time, and an outdated cached dataset is reused each run.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Linux-5.8.0-1038-gcp-x86_64-with-glibc2.31
- Python version: 3.9.6
- PyArrow version: 4.0.1 | 30 | `generate_random_fingerprint()` deterministic with 🤗Transformers' `set_seed()`
## Describe the bug
**Update:** I dug into this to try to reproduce the underlying issue, and I believe it's that `set_seed()` from the `transformers` library makes the "random" fingerprint identical each time. I believe this is still a bug, because `datasets` is used exactly this way in `transformers` after `set_seed()` has been called, and I think that using `set_seed()` is a standard procedure to aid reproducibility. I've added more details to reproduce this below.
Hi there! I'm using my own local dataset and custom preprocessing function. My preprocessing function seems to be unpickle-able, perhaps because it is from a closure (will debug this separately). I get this warning, which is expected:
https://github.com/huggingface/datasets/blob/450b9174765374111e5c6daab0ed294bc3d9b639/src/datasets/fingerprint.py#L260-L265
However, what's not expected is that the `datasets` actually _does_ seem to cache and reuse this dataset between runs! After that line, the next thing that's logged looks like:
```text
Loading cached processed dataset at /home/xxx/.cache/huggingface/datasets/csv/default-xxx/0.0.0/xxx/cache-xxx.arrow
```
The path is exactly the same each run (e.g., last 26 runs).
This becomes a problem because I'll pass in the `--max_eval_samples` flag to the HuggingFace example script I'm running off of ([run_swag.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/multiple-choice/run_swag.py)). The fact that the cached dataset is reused means this flag gets ignored. I'll try to load 100 examples, and it will load the full cached 1,000,000.
I think that
https://github.com/huggingface/datasets/blob/450b9174765374111e5c6daab0ed294bc3d9b639/src/datasets/fingerprint.py#L248
... is actually consistent because randomness is being controlled in HuggingFace/Transformers for reproducibility. I've added a demo of this below.
## Steps to reproduce the bug
```python
# Contents of print_fingerprint.py
from transformers import set_seed
from datasets.fingerprint import generate_random_fingerprint
set_seed(42)
print(generate_random_fingerprint())
```
```bash
for i in {0..10}; do
python print_fingerprint.py
done
1c80317fa3b1799d
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1c80317fa3b1799d
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1c80317fa3b1799d
```
## Expected results
After the "random hash" warning is emitted, a random hash is generated, and no outdated cached datasets are reused.
## Actual results
After the "random hash" warning is emitted, an identical hash is generated each time, and an outdated cached dataset is reused each run.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Linux-5.8.0-1038-gcp-x86_64-with-glibc2.31
- Python version: 3.9.6
- PyArrow version: 4.0.1
Hi !
IMO we shouldn't try to modify `set_seed` from transformers but maybe make `datasets` have its own RNG just to generate random fingerprints.
Any opinion on this @LysandreJik ? | [
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https://github.com/huggingface/datasets/issues/2768 | `ArrowInvalid: Added column's length must match table's length.` after using `select` | Hi,
the `select` method creates an indices mapping and doesn't modify the underlying PyArrow table by default for better performance. To modify the underlying table after the `select` call, call `flatten_indices` on the dataset object as follows:
```python
from datasets import load_dataset
ds = load_dataset("tweets_hate_speech_detection")['train'].select(range(128))
ds = ds.flatten_indices()
ds = ds.add_column('ones', [1]*128)
``` | ## Describe the bug
I would like to add a column to a downsampled dataset. However I get an error message saying the length don't match with the length of the unsampled dataset indicated. I suspect that the dataset size is not updated when calling `select`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset("tweets_hate_speech_detection")['train'].select(range(128))
ds = ds.add_column('ones', [1]*128)
```
## Expected results
I would expect a new column named `ones` filled with `1`. When I check the length of `ds` it says `128`. Interestingly, it works when calling `ds = ds.map(lambda x: x)` before adding the column.
## Actual results
Specify the actual results or traceback.
```python
---------------------------------------------------------------------------
ArrowInvalid Traceback (most recent call last)
/var/folders/l4/2905jygx4tx5jv8_kn03vxsw0000gn/T/ipykernel_6301/868709636.py in <module>
1 from datasets import load_dataset
2 ds = load_dataset("tweets_hate_speech_detection")['train'].select(range(128))
----> 3 ds = ds.add_column('ones', [0]*128)
~/git/semantic-clustering/env/lib/python3.8/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
183 }
184 # apply actual function
--> 185 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
186 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
187 # re-apply format to the output
~/git/semantic-clustering/env/lib/python3.8/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)
395 # Call actual function
396
--> 397 out = func(self, *args, **kwargs)
398
399 # Update fingerprint of in-place transforms + update in-place history of transforms
~/git/semantic-clustering/env/lib/python3.8/site-packages/datasets/arrow_dataset.py in add_column(self, name, column, new_fingerprint)
2965 column_table = InMemoryTable.from_pydict({name: column})
2966 # Concatenate tables horizontally
-> 2967 table = ConcatenationTable.from_tables([self._data, column_table], axis=1)
2968 # Update features
2969 info = self.info.copy()
~/git/semantic-clustering/env/lib/python3.8/site-packages/datasets/table.py in from_tables(cls, tables, axis)
715 table_blocks = to_blocks(table)
716 blocks = _extend_blocks(blocks, table_blocks, axis=axis)
--> 717 return cls.from_blocks(blocks)
718
719 @property
~/git/semantic-clustering/env/lib/python3.8/site-packages/datasets/table.py in from_blocks(cls, blocks)
663 return cls(table, blocks)
664 else:
--> 665 table = cls._concat_blocks_horizontally_and_vertically(blocks)
666 return cls(table, blocks)
667
~/git/semantic-clustering/env/lib/python3.8/site-packages/datasets/table.py in _concat_blocks_horizontally_and_vertically(cls, blocks)
623 if not tables:
624 continue
--> 625 pa_table_horizontally_concatenated = cls._concat_blocks(tables, axis=1)
626 pa_tables_to_concat_vertically.append(pa_table_horizontally_concatenated)
627 return cls._concat_blocks(pa_tables_to_concat_vertically, axis=0)
~/git/semantic-clustering/env/lib/python3.8/site-packages/datasets/table.py in _concat_blocks(blocks, axis)
612 else:
613 for name, col in zip(table.column_names, table.columns):
--> 614 pa_table = pa_table.append_column(name, col)
615 return pa_table
616 else:
~/git/semantic-clustering/env/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.append_column()
~/git/semantic-clustering/env/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.add_column()
~/git/semantic-clustering/env/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
~/git/semantic-clustering/env/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowInvalid: Added column's length must match table's length. Expected length 31962 but got length 128
```
## Environment info
- `datasets` version: 1.11.0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.5
- PyArrow version: 5.0.0
| 53 | `ArrowInvalid: Added column's length must match table's length.` after using `select`
## Describe the bug
I would like to add a column to a downsampled dataset. However I get an error message saying the length don't match with the length of the unsampled dataset indicated. I suspect that the dataset size is not updated when calling `select`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset("tweets_hate_speech_detection")['train'].select(range(128))
ds = ds.add_column('ones', [1]*128)
```
## Expected results
I would expect a new column named `ones` filled with `1`. When I check the length of `ds` it says `128`. Interestingly, it works when calling `ds = ds.map(lambda x: x)` before adding the column.
## Actual results
Specify the actual results or traceback.
```python
---------------------------------------------------------------------------
ArrowInvalid Traceback (most recent call last)
/var/folders/l4/2905jygx4tx5jv8_kn03vxsw0000gn/T/ipykernel_6301/868709636.py in <module>
1 from datasets import load_dataset
2 ds = load_dataset("tweets_hate_speech_detection")['train'].select(range(128))
----> 3 ds = ds.add_column('ones', [0]*128)
~/git/semantic-clustering/env/lib/python3.8/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
183 }
184 # apply actual function
--> 185 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
186 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
187 # re-apply format to the output
~/git/semantic-clustering/env/lib/python3.8/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)
395 # Call actual function
396
--> 397 out = func(self, *args, **kwargs)
398
399 # Update fingerprint of in-place transforms + update in-place history of transforms
~/git/semantic-clustering/env/lib/python3.8/site-packages/datasets/arrow_dataset.py in add_column(self, name, column, new_fingerprint)
2965 column_table = InMemoryTable.from_pydict({name: column})
2966 # Concatenate tables horizontally
-> 2967 table = ConcatenationTable.from_tables([self._data, column_table], axis=1)
2968 # Update features
2969 info = self.info.copy()
~/git/semantic-clustering/env/lib/python3.8/site-packages/datasets/table.py in from_tables(cls, tables, axis)
715 table_blocks = to_blocks(table)
716 blocks = _extend_blocks(blocks, table_blocks, axis=axis)
--> 717 return cls.from_blocks(blocks)
718
719 @property
~/git/semantic-clustering/env/lib/python3.8/site-packages/datasets/table.py in from_blocks(cls, blocks)
663 return cls(table, blocks)
664 else:
--> 665 table = cls._concat_blocks_horizontally_and_vertically(blocks)
666 return cls(table, blocks)
667
~/git/semantic-clustering/env/lib/python3.8/site-packages/datasets/table.py in _concat_blocks_horizontally_and_vertically(cls, blocks)
623 if not tables:
624 continue
--> 625 pa_table_horizontally_concatenated = cls._concat_blocks(tables, axis=1)
626 pa_tables_to_concat_vertically.append(pa_table_horizontally_concatenated)
627 return cls._concat_blocks(pa_tables_to_concat_vertically, axis=0)
~/git/semantic-clustering/env/lib/python3.8/site-packages/datasets/table.py in _concat_blocks(blocks, axis)
612 else:
613 for name, col in zip(table.column_names, table.columns):
--> 614 pa_table = pa_table.append_column(name, col)
615 return pa_table
616 else:
~/git/semantic-clustering/env/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.append_column()
~/git/semantic-clustering/env/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.add_column()
~/git/semantic-clustering/env/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
~/git/semantic-clustering/env/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowInvalid: Added column's length must match table's length. Expected length 31962 but got length 128
```
## Environment info
- `datasets` version: 1.11.0
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.5
- PyArrow version: 5.0.0
Hi,
the `select` method creates an indices mapping and doesn't modify the underlying PyArrow table by default for better performance. To modify the underlying table after the `select` call, call `flatten_indices` on the dataset object as follows:
```python
from datasets import load_dataset
ds = load_dataset("tweets_hate_speech_detection")['train'].select(range(128))
ds = ds.flatten_indices()
ds = ds.add_column('ones', [1]*128)
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https://github.com/huggingface/datasets/issues/2767 | equal operation to perform unbatch for huggingface datasets | Hi @lhoestq
Maybe this is clearer to explain like this, currently map function, map one example to "one" modified one, lets assume we want to map one example to "multiple" examples, in which we do not know in advance how many examples they would be per each entry. I greatly appreciate telling me how I can handle this operation, thanks a lot | Hi
I need to use "unbatch" operation in tensorflow on a huggingface dataset, I could not find this operation, could you kindly direct me how I can do it, here is the problem I am trying to solve:
I am considering "record" dataset in SuperGlue and I need to replicate each entery of the dataset for each answer, to make it similar to what T5 originally did:
https://github.com/google-research/text-to-text-transfer-transformer/blob/3c58859b8fe72c2dbca6a43bc775aa510ba7e706/t5/data/preprocessors.py#L925
Here please find an example:
For example, a typical example from ReCoRD might look like
{
'passsage': 'This is the passage.',
'query': 'A @placeholder is a bird.',
'entities': ['penguin', 'potato', 'pigeon'],
'answers': ['penguin', 'pigeon'],
}
and I need a prosessor which would turn this example into the following two examples:
{
'inputs': 'record query: A @placeholder is a bird. entities: penguin, '
'potato, pigeon passage: This is the passage.',
'targets': 'penguin',
}
and
{
'inputs': 'record query: A @placeholder is a bird. entities: penguin, '
'potato, pigeon passage: This is the passage.',
'targets': 'pigeon',
}
For doing this, one need unbatch, as each entry can map to multiple samples depending on the number of answers, I am not sure how to perform this operation with huggingface datasets library and greatly appreciate your help
@lhoestq
Thank you very much.
| 62 | equal operation to perform unbatch for huggingface datasets
Hi
I need to use "unbatch" operation in tensorflow on a huggingface dataset, I could not find this operation, could you kindly direct me how I can do it, here is the problem I am trying to solve:
I am considering "record" dataset in SuperGlue and I need to replicate each entery of the dataset for each answer, to make it similar to what T5 originally did:
https://github.com/google-research/text-to-text-transfer-transformer/blob/3c58859b8fe72c2dbca6a43bc775aa510ba7e706/t5/data/preprocessors.py#L925
Here please find an example:
For example, a typical example from ReCoRD might look like
{
'passsage': 'This is the passage.',
'query': 'A @placeholder is a bird.',
'entities': ['penguin', 'potato', 'pigeon'],
'answers': ['penguin', 'pigeon'],
}
and I need a prosessor which would turn this example into the following two examples:
{
'inputs': 'record query: A @placeholder is a bird. entities: penguin, '
'potato, pigeon passage: This is the passage.',
'targets': 'penguin',
}
and
{
'inputs': 'record query: A @placeholder is a bird. entities: penguin, '
'potato, pigeon passage: This is the passage.',
'targets': 'pigeon',
}
For doing this, one need unbatch, as each entry can map to multiple samples depending on the number of answers, I am not sure how to perform this operation with huggingface datasets library and greatly appreciate your help
@lhoestq
Thank you very much.
Hi @lhoestq
Maybe this is clearer to explain like this, currently map function, map one example to "one" modified one, lets assume we want to map one example to "multiple" examples, in which we do not know in advance how many examples they would be per each entry. I greatly appreciate telling me how I can handle this operation, thanks a lot | [
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https://github.com/huggingface/datasets/issues/2767 | equal operation to perform unbatch for huggingface datasets | Hi,
this is also my question on how to perform similar operation as "unbatch" in tensorflow in great huggingface dataset library.
thanks. | Hi
I need to use "unbatch" operation in tensorflow on a huggingface dataset, I could not find this operation, could you kindly direct me how I can do it, here is the problem I am trying to solve:
I am considering "record" dataset in SuperGlue and I need to replicate each entery of the dataset for each answer, to make it similar to what T5 originally did:
https://github.com/google-research/text-to-text-transfer-transformer/blob/3c58859b8fe72c2dbca6a43bc775aa510ba7e706/t5/data/preprocessors.py#L925
Here please find an example:
For example, a typical example from ReCoRD might look like
{
'passsage': 'This is the passage.',
'query': 'A @placeholder is a bird.',
'entities': ['penguin', 'potato', 'pigeon'],
'answers': ['penguin', 'pigeon'],
}
and I need a prosessor which would turn this example into the following two examples:
{
'inputs': 'record query: A @placeholder is a bird. entities: penguin, '
'potato, pigeon passage: This is the passage.',
'targets': 'penguin',
}
and
{
'inputs': 'record query: A @placeholder is a bird. entities: penguin, '
'potato, pigeon passage: This is the passage.',
'targets': 'pigeon',
}
For doing this, one need unbatch, as each entry can map to multiple samples depending on the number of answers, I am not sure how to perform this operation with huggingface datasets library and greatly appreciate your help
@lhoestq
Thank you very much.
| 22 | equal operation to perform unbatch for huggingface datasets
Hi
I need to use "unbatch" operation in tensorflow on a huggingface dataset, I could not find this operation, could you kindly direct me how I can do it, here is the problem I am trying to solve:
I am considering "record" dataset in SuperGlue and I need to replicate each entery of the dataset for each answer, to make it similar to what T5 originally did:
https://github.com/google-research/text-to-text-transfer-transformer/blob/3c58859b8fe72c2dbca6a43bc775aa510ba7e706/t5/data/preprocessors.py#L925
Here please find an example:
For example, a typical example from ReCoRD might look like
{
'passsage': 'This is the passage.',
'query': 'A @placeholder is a bird.',
'entities': ['penguin', 'potato', 'pigeon'],
'answers': ['penguin', 'pigeon'],
}
and I need a prosessor which would turn this example into the following two examples:
{
'inputs': 'record query: A @placeholder is a bird. entities: penguin, '
'potato, pigeon passage: This is the passage.',
'targets': 'penguin',
}
and
{
'inputs': 'record query: A @placeholder is a bird. entities: penguin, '
'potato, pigeon passage: This is the passage.',
'targets': 'pigeon',
}
For doing this, one need unbatch, as each entry can map to multiple samples depending on the number of answers, I am not sure how to perform this operation with huggingface datasets library and greatly appreciate your help
@lhoestq
Thank you very much.
Hi,
this is also my question on how to perform similar operation as "unbatch" in tensorflow in great huggingface dataset library.
thanks. | [
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https://github.com/huggingface/datasets/issues/2767 | equal operation to perform unbatch for huggingface datasets | Hi,
`Dataset.map` in the batched mode allows you to map a single row to multiple rows. So to perform "unbatch", you can do the following:
```python
import collections
def unbatch(batch):
new_batch = collections.defaultdict(list)
keys = batch.keys()
for values in zip(*batch.values()):
ex = {k: v for k, v in zip(keys, values)}
inputs = f"record query: {ex['query']} entities: {', '.join(ex['entities'])} passage: {ex['passage']}"
new_batch["inputs"].extend([inputs] * len(ex["answers"]))
new_batch["targets"].extend(ex["answers"])
return new_batch
dset = dset.map(unbatch, batched=True, remove_columns=dset.column_names)
``` | Hi
I need to use "unbatch" operation in tensorflow on a huggingface dataset, I could not find this operation, could you kindly direct me how I can do it, here is the problem I am trying to solve:
I am considering "record" dataset in SuperGlue and I need to replicate each entery of the dataset for each answer, to make it similar to what T5 originally did:
https://github.com/google-research/text-to-text-transfer-transformer/blob/3c58859b8fe72c2dbca6a43bc775aa510ba7e706/t5/data/preprocessors.py#L925
Here please find an example:
For example, a typical example from ReCoRD might look like
{
'passsage': 'This is the passage.',
'query': 'A @placeholder is a bird.',
'entities': ['penguin', 'potato', 'pigeon'],
'answers': ['penguin', 'pigeon'],
}
and I need a prosessor which would turn this example into the following two examples:
{
'inputs': 'record query: A @placeholder is a bird. entities: penguin, '
'potato, pigeon passage: This is the passage.',
'targets': 'penguin',
}
and
{
'inputs': 'record query: A @placeholder is a bird. entities: penguin, '
'potato, pigeon passage: This is the passage.',
'targets': 'pigeon',
}
For doing this, one need unbatch, as each entry can map to multiple samples depending on the number of answers, I am not sure how to perform this operation with huggingface datasets library and greatly appreciate your help
@lhoestq
Thank you very much.
| 72 | equal operation to perform unbatch for huggingface datasets
Hi
I need to use "unbatch" operation in tensorflow on a huggingface dataset, I could not find this operation, could you kindly direct me how I can do it, here is the problem I am trying to solve:
I am considering "record" dataset in SuperGlue and I need to replicate each entery of the dataset for each answer, to make it similar to what T5 originally did:
https://github.com/google-research/text-to-text-transfer-transformer/blob/3c58859b8fe72c2dbca6a43bc775aa510ba7e706/t5/data/preprocessors.py#L925
Here please find an example:
For example, a typical example from ReCoRD might look like
{
'passsage': 'This is the passage.',
'query': 'A @placeholder is a bird.',
'entities': ['penguin', 'potato', 'pigeon'],
'answers': ['penguin', 'pigeon'],
}
and I need a prosessor which would turn this example into the following two examples:
{
'inputs': 'record query: A @placeholder is a bird. entities: penguin, '
'potato, pigeon passage: This is the passage.',
'targets': 'penguin',
}
and
{
'inputs': 'record query: A @placeholder is a bird. entities: penguin, '
'potato, pigeon passage: This is the passage.',
'targets': 'pigeon',
}
For doing this, one need unbatch, as each entry can map to multiple samples depending on the number of answers, I am not sure how to perform this operation with huggingface datasets library and greatly appreciate your help
@lhoestq
Thank you very much.
Hi,
`Dataset.map` in the batched mode allows you to map a single row to multiple rows. So to perform "unbatch", you can do the following:
```python
import collections
def unbatch(batch):
new_batch = collections.defaultdict(list)
keys = batch.keys()
for values in zip(*batch.values()):
ex = {k: v for k, v in zip(keys, values)}
inputs = f"record query: {ex['query']} entities: {', '.join(ex['entities'])} passage: {ex['passage']}"
new_batch["inputs"].extend([inputs] * len(ex["answers"]))
new_batch["targets"].extend(ex["answers"])
return new_batch
dset = dset.map(unbatch, batched=True, remove_columns=dset.column_names)
``` | [
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https://github.com/huggingface/datasets/issues/2767 | equal operation to perform unbatch for huggingface datasets | Dear @mariosasko
First, thank you very much for coming back to me on this, I appreciate it a lot. I tried this solution, I am getting errors, do you mind
giving me one test example to be able to run your code, to understand better the format of the inputs to your function?
in this function https://github.com/google-research/text-to-text-transfer-transformer/blob/3c58859b8fe72c2dbca6a43bc775aa510ba7e706/t5/data/preprocessors.py#L952 they copy each example to the number of "answers", do you mean one should not do the copying part and use directly your function?
thank you very much for your help and time. | Hi
I need to use "unbatch" operation in tensorflow on a huggingface dataset, I could not find this operation, could you kindly direct me how I can do it, here is the problem I am trying to solve:
I am considering "record" dataset in SuperGlue and I need to replicate each entery of the dataset for each answer, to make it similar to what T5 originally did:
https://github.com/google-research/text-to-text-transfer-transformer/blob/3c58859b8fe72c2dbca6a43bc775aa510ba7e706/t5/data/preprocessors.py#L925
Here please find an example:
For example, a typical example from ReCoRD might look like
{
'passsage': 'This is the passage.',
'query': 'A @placeholder is a bird.',
'entities': ['penguin', 'potato', 'pigeon'],
'answers': ['penguin', 'pigeon'],
}
and I need a prosessor which would turn this example into the following two examples:
{
'inputs': 'record query: A @placeholder is a bird. entities: penguin, '
'potato, pigeon passage: This is the passage.',
'targets': 'penguin',
}
and
{
'inputs': 'record query: A @placeholder is a bird. entities: penguin, '
'potato, pigeon passage: This is the passage.',
'targets': 'pigeon',
}
For doing this, one need unbatch, as each entry can map to multiple samples depending on the number of answers, I am not sure how to perform this operation with huggingface datasets library and greatly appreciate your help
@lhoestq
Thank you very much.
| 90 | equal operation to perform unbatch for huggingface datasets
Hi
I need to use "unbatch" operation in tensorflow on a huggingface dataset, I could not find this operation, could you kindly direct me how I can do it, here is the problem I am trying to solve:
I am considering "record" dataset in SuperGlue and I need to replicate each entery of the dataset for each answer, to make it similar to what T5 originally did:
https://github.com/google-research/text-to-text-transfer-transformer/blob/3c58859b8fe72c2dbca6a43bc775aa510ba7e706/t5/data/preprocessors.py#L925
Here please find an example:
For example, a typical example from ReCoRD might look like
{
'passsage': 'This is the passage.',
'query': 'A @placeholder is a bird.',
'entities': ['penguin', 'potato', 'pigeon'],
'answers': ['penguin', 'pigeon'],
}
and I need a prosessor which would turn this example into the following two examples:
{
'inputs': 'record query: A @placeholder is a bird. entities: penguin, '
'potato, pigeon passage: This is the passage.',
'targets': 'penguin',
}
and
{
'inputs': 'record query: A @placeholder is a bird. entities: penguin, '
'potato, pigeon passage: This is the passage.',
'targets': 'pigeon',
}
For doing this, one need unbatch, as each entry can map to multiple samples depending on the number of answers, I am not sure how to perform this operation with huggingface datasets library and greatly appreciate your help
@lhoestq
Thank you very much.
Dear @mariosasko
First, thank you very much for coming back to me on this, I appreciate it a lot. I tried this solution, I am getting errors, do you mind
giving me one test example to be able to run your code, to understand better the format of the inputs to your function?
in this function https://github.com/google-research/text-to-text-transfer-transformer/blob/3c58859b8fe72c2dbca6a43bc775aa510ba7e706/t5/data/preprocessors.py#L952 they copy each example to the number of "answers", do you mean one should not do the copying part and use directly your function?
thank you very much for your help and time. | [
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https://github.com/huggingface/datasets/issues/2767 | equal operation to perform unbatch for huggingface datasets | Hi @mariosasko
I think finally I got this, I think you mean to do things in one step, here is the full example for completeness:
```
def unbatch(batch):
new_batch = collections.defaultdict(list)
keys = batch.keys()
for values in zip(*batch.values()):
ex = {k: v for k, v in zip(keys, values)}
# updates the passage.
passage = ex['passage']
passage = re.sub(r'(\.|\?|\!|\"|\')\n@highlight\n', r'\1 ', passage)
passage = re.sub(r'\n@highlight\n', '. ', passage)
inputs = f"record query: {ex['query']} entities: {', '.join(ex['entities'])} passage: {passage}"
# duplicates the samples based on number of answers.
num_answers = len(ex["answers"])
num_duplicates = np.maximum(1, num_answers)
new_batch["inputs"].extend([inputs] * num_duplicates) #len(ex["answers"]))
new_batch["targets"].extend(ex["answers"] if num_answers > 0 else ["<unk>"])
return new_batch
data = datasets.load_dataset('super_glue', 'record', split="train", script_version="master")
data = data.map(unbatch, batched=True, remove_columns=data.column_names)
```
Thanks a lot again, this was a super great way to do it. | Hi
I need to use "unbatch" operation in tensorflow on a huggingface dataset, I could not find this operation, could you kindly direct me how I can do it, here is the problem I am trying to solve:
I am considering "record" dataset in SuperGlue and I need to replicate each entery of the dataset for each answer, to make it similar to what T5 originally did:
https://github.com/google-research/text-to-text-transfer-transformer/blob/3c58859b8fe72c2dbca6a43bc775aa510ba7e706/t5/data/preprocessors.py#L925
Here please find an example:
For example, a typical example from ReCoRD might look like
{
'passsage': 'This is the passage.',
'query': 'A @placeholder is a bird.',
'entities': ['penguin', 'potato', 'pigeon'],
'answers': ['penguin', 'pigeon'],
}
and I need a prosessor which would turn this example into the following two examples:
{
'inputs': 'record query: A @placeholder is a bird. entities: penguin, '
'potato, pigeon passage: This is the passage.',
'targets': 'penguin',
}
and
{
'inputs': 'record query: A @placeholder is a bird. entities: penguin, '
'potato, pigeon passage: This is the passage.',
'targets': 'pigeon',
}
For doing this, one need unbatch, as each entry can map to multiple samples depending on the number of answers, I am not sure how to perform this operation with huggingface datasets library and greatly appreciate your help
@lhoestq
Thank you very much.
| 131 | equal operation to perform unbatch for huggingface datasets
Hi
I need to use "unbatch" operation in tensorflow on a huggingface dataset, I could not find this operation, could you kindly direct me how I can do it, here is the problem I am trying to solve:
I am considering "record" dataset in SuperGlue and I need to replicate each entery of the dataset for each answer, to make it similar to what T5 originally did:
https://github.com/google-research/text-to-text-transfer-transformer/blob/3c58859b8fe72c2dbca6a43bc775aa510ba7e706/t5/data/preprocessors.py#L925
Here please find an example:
For example, a typical example from ReCoRD might look like
{
'passsage': 'This is the passage.',
'query': 'A @placeholder is a bird.',
'entities': ['penguin', 'potato', 'pigeon'],
'answers': ['penguin', 'pigeon'],
}
and I need a prosessor which would turn this example into the following two examples:
{
'inputs': 'record query: A @placeholder is a bird. entities: penguin, '
'potato, pigeon passage: This is the passage.',
'targets': 'penguin',
}
and
{
'inputs': 'record query: A @placeholder is a bird. entities: penguin, '
'potato, pigeon passage: This is the passage.',
'targets': 'pigeon',
}
For doing this, one need unbatch, as each entry can map to multiple samples depending on the number of answers, I am not sure how to perform this operation with huggingface datasets library and greatly appreciate your help
@lhoestq
Thank you very much.
Hi @mariosasko
I think finally I got this, I think you mean to do things in one step, here is the full example for completeness:
```
def unbatch(batch):
new_batch = collections.defaultdict(list)
keys = batch.keys()
for values in zip(*batch.values()):
ex = {k: v for k, v in zip(keys, values)}
# updates the passage.
passage = ex['passage']
passage = re.sub(r'(\.|\?|\!|\"|\')\n@highlight\n', r'\1 ', passage)
passage = re.sub(r'\n@highlight\n', '. ', passage)
inputs = f"record query: {ex['query']} entities: {', '.join(ex['entities'])} passage: {passage}"
# duplicates the samples based on number of answers.
num_answers = len(ex["answers"])
num_duplicates = np.maximum(1, num_answers)
new_batch["inputs"].extend([inputs] * num_duplicates) #len(ex["answers"]))
new_batch["targets"].extend(ex["answers"] if num_answers > 0 else ["<unk>"])
return new_batch
data = datasets.load_dataset('super_glue', 'record', split="train", script_version="master")
data = data.map(unbatch, batched=True, remove_columns=data.column_names)
```
Thanks a lot again, this was a super great way to do it. | [
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https://github.com/huggingface/datasets/issues/2765 | BERTScore Error | Hi,
The `use_fast_tokenizer` argument has been recently added to the bert-score lib. I've opened a PR with the fix. In the meantime, you can try to downgrade the version of bert-score with the following command to make the code work:
```
pip uninstall bert-score
pip install "bert-score<0.3.10"
``` | ## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
predictions = ["hello there", "general kenobi"]
references = ["hello there", "general kenobi"]
bert = load_metric('bertscore')
bert.compute(predictions=predictions, references=references,lang='en')
```
# Bug
`TypeError: get_hash() missing 1 required positional argument: 'use_fast_tokenizer'`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Colab
- Python version:
- PyArrow version:
| 48 | BERTScore Error
## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
predictions = ["hello there", "general kenobi"]
references = ["hello there", "general kenobi"]
bert = load_metric('bertscore')
bert.compute(predictions=predictions, references=references,lang='en')
```
# Bug
`TypeError: get_hash() missing 1 required positional argument: 'use_fast_tokenizer'`
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:
- Platform: Colab
- Python version:
- PyArrow version:
Hi,
The `use_fast_tokenizer` argument has been recently added to the bert-score lib. I've opened a PR with the fix. In the meantime, you can try to downgrade the version of bert-score with the following command to make the code work:
```
pip uninstall bert-score
pip install "bert-score<0.3.10"
``` | [
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https://github.com/huggingface/datasets/issues/2763 | English wikipedia datasets is not clean | Hi ! Certain users might need these data (for training or simply to explore/index the dataset).
Feel free to implement a map function that gets rid of these paragraphs and process the wikipedia dataset with it before training | ## Describe the bug
Wikipedia english dumps contain many wikipedia paragraphs like "References", "Category:" and "See Also" that should not be used for training.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets import load_dataset
w = load_dataset('wikipedia', '20200501.en')
print(w['train'][0]['text'])
```
> 'Yangliuqing () is a market town in Xiqing District, in the western suburbs of Tianjin, People\'s Republic of China. Despite its relatively small size, it has been named since 2006 in the "famous historical and cultural market towns in China".\n\nIt is best known in China for creating nianhua or Yangliuqing nianhua. For more than 400 years, Yangliuqing has in effect specialised in the creation of these woodcuts for the New Year. wood block prints using vivid colourschemes to portray traditional scenes of children\'s games often interwoven with auspiciouse objects.\n\n, it had 27 residential communities () and 25 villages under its administration.\n\nShi Family Grand Courtyard\n\nShi Family Grand Courtyard (Tiānjīn Shí Jiā Dà Yuàn, 天津石家大院) is situated in Yangliuqing Town of Xiqing District, which is the former residence of wealthy merchant Shi Yuanshi - the 4th son of Shi Wancheng, one of the eight great masters in Tianjin. First built in 1875, it covers over 6,000 square meters, including large and small yards and over 200 folk houses, a theater and over 275 rooms that served as apartments and places of business and worship for this powerful family. Shifu Garden, which finished its expansion in October 2003, covers 1,200 square meters, incorporates the elegance of imperial garden and delicacy of south garden. Now the courtyard of Shi family covers about 10,000 square meters, which is called the first mansion in North China. Now it serves as the folk custom museum in Yangliuqing, which has a large collection of folk custom museum in Yanliuqing, which has a large collection of folk art pieces like Yanliuqing New Year pictures, brick sculpture.\n\nShi\'s ancestor came from Dong\'e County in Shandong Province, engaged in water transport of grain. As the wealth gradually accumulated, the Shi Family moved to Yangliuqing and bought large tracts of land and set up their residence. Shi Yuanshi came from the fourth generation of the family, who was a successful businessman and a good household manager, and the residence was thus enlarged for several times until it acquired the present scale. It is believed to be the first mansion in the west of Tianjin.\n\nThe residence is symmetric based on the axis formed by a passageway in the middle, on which there are four archways. On the east side of the courtyard, there are traditional single-story houses with rows of rooms around the four sides, which was once the living area for the Shi Family. The rooms on north side were the accountants\' office. On the west are the major constructions including the family hall for worshipping Buddha, theater and the south reception room. On both sides of the residence are side yard rooms for maids and servants.\n\nToday, the Shi mansion, located in the township of Yangliuqing to the west of central Tianjin, stands as a surprisingly well-preserved monument to China\'s pre-revolution mercantile spirit. It also serves as an on-location shoot for many of China\'s popular historical dramas. Many of the rooms feature period furniture, paintings and calligraphy, and the extensive Shifu Garden.\n\nPart of the complex has been turned into the Yangliuqing Museum, which includes displays focused on symbolic aspects of the courtyards\' construction, local folk art and customs, and traditional period furnishings and crafts.\n\n**See also \n\nList of township-level divisions of Tianjin\n\nReferences \n\n http://arts.cultural-china.com/en/65Arts4795.html\n\nCategory:Towns in Tianjin'**
## Expected results
I expect no junk in the data.
## Actual results
Specify the actual results or traceback.
## Environment info
- `datasets` version: 1.10.2
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.5
- PyArrow version: 3.0.0
| 38 | English wikipedia datasets is not clean
## Describe the bug
Wikipedia english dumps contain many wikipedia paragraphs like "References", "Category:" and "See Also" that should not be used for training.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets import load_dataset
w = load_dataset('wikipedia', '20200501.en')
print(w['train'][0]['text'])
```
> 'Yangliuqing () is a market town in Xiqing District, in the western suburbs of Tianjin, People\'s Republic of China. Despite its relatively small size, it has been named since 2006 in the "famous historical and cultural market towns in China".\n\nIt is best known in China for creating nianhua or Yangliuqing nianhua. For more than 400 years, Yangliuqing has in effect specialised in the creation of these woodcuts for the New Year. wood block prints using vivid colourschemes to portray traditional scenes of children\'s games often interwoven with auspiciouse objects.\n\n, it had 27 residential communities () and 25 villages under its administration.\n\nShi Family Grand Courtyard\n\nShi Family Grand Courtyard (Tiānjīn Shí Jiā Dà Yuàn, 天津石家大院) is situated in Yangliuqing Town of Xiqing District, which is the former residence of wealthy merchant Shi Yuanshi - the 4th son of Shi Wancheng, one of the eight great masters in Tianjin. First built in 1875, it covers over 6,000 square meters, including large and small yards and over 200 folk houses, a theater and over 275 rooms that served as apartments and places of business and worship for this powerful family. Shifu Garden, which finished its expansion in October 2003, covers 1,200 square meters, incorporates the elegance of imperial garden and delicacy of south garden. Now the courtyard of Shi family covers about 10,000 square meters, which is called the first mansion in North China. Now it serves as the folk custom museum in Yangliuqing, which has a large collection of folk custom museum in Yanliuqing, which has a large collection of folk art pieces like Yanliuqing New Year pictures, brick sculpture.\n\nShi\'s ancestor came from Dong\'e County in Shandong Province, engaged in water transport of grain. As the wealth gradually accumulated, the Shi Family moved to Yangliuqing and bought large tracts of land and set up their residence. Shi Yuanshi came from the fourth generation of the family, who was a successful businessman and a good household manager, and the residence was thus enlarged for several times until it acquired the present scale. It is believed to be the first mansion in the west of Tianjin.\n\nThe residence is symmetric based on the axis formed by a passageway in the middle, on which there are four archways. On the east side of the courtyard, there are traditional single-story houses with rows of rooms around the four sides, which was once the living area for the Shi Family. The rooms on north side were the accountants\' office. On the west are the major constructions including the family hall for worshipping Buddha, theater and the south reception room. On both sides of the residence are side yard rooms for maids and servants.\n\nToday, the Shi mansion, located in the township of Yangliuqing to the west of central Tianjin, stands as a surprisingly well-preserved monument to China\'s pre-revolution mercantile spirit. It also serves as an on-location shoot for many of China\'s popular historical dramas. Many of the rooms feature period furniture, paintings and calligraphy, and the extensive Shifu Garden.\n\nPart of the complex has been turned into the Yangliuqing Museum, which includes displays focused on symbolic aspects of the courtyards\' construction, local folk art and customs, and traditional period furnishings and crafts.\n\n**See also \n\nList of township-level divisions of Tianjin\n\nReferences \n\n http://arts.cultural-china.com/en/65Arts4795.html\n\nCategory:Towns in Tianjin'**
## Expected results
I expect no junk in the data.
## Actual results
Specify the actual results or traceback.
## Environment info
- `datasets` version: 1.10.2
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.5
- PyArrow version: 3.0.0
Hi ! Certain users might need these data (for training or simply to explore/index the dataset).
Feel free to implement a map function that gets rid of these paragraphs and process the wikipedia dataset with it before training | [
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https://github.com/huggingface/datasets/issues/2761 | Error loading C4 realnewslike dataset | Hi @danshirron,
`c4` was updated few days back by @lhoestq. The new configs are `['en', 'en.noclean', 'en.realnewslike', 'en.webtextlike'].` You'll need to remove any older version of this dataset you previously downloaded and then run `load_dataset` again with new configuration. | ## Describe the bug
Error loading C4 realnewslike dataset. Validation part mismatch
## Steps to reproduce the bug
```python
raw_datasets = load_dataset('c4', 'realnewslike', cache_dir=model_args.cache_dir)
## Expected results
success on data loading
## Actual results
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15.3M/15.3M [00:00<00:00, 28.1MB/s]Traceback (most recent call last):
File "run_mlm_tf.py", line 794, in <module>
main()
File "run_mlm_tf.py", line 425, in main
raw_datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir) File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/load.py", line 843, in load_dataset
builder_instance.download_and_prepare(
File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py", line 608, in download_and_prepare
self._download_and_prepare(
File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py", line 698, in _download_and_prepare verify_splits(self.info.splits, split_dict) File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 74, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='validation', num_bytes=38165657946, num_examples=13799838, dataset_name='c4'), 'recorded': SplitInfo(name='validation', num_bytes=37875873, num_examples=13863, dataset_name='c4')}]
## Environment info
- `datasets` version: 1.10.2
- Platform: Linux-5.4.0-58-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 4.0.1 | 39 | Error loading C4 realnewslike dataset
## Describe the bug
Error loading C4 realnewslike dataset. Validation part mismatch
## Steps to reproduce the bug
```python
raw_datasets = load_dataset('c4', 'realnewslike', cache_dir=model_args.cache_dir)
## Expected results
success on data loading
## Actual results
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15.3M/15.3M [00:00<00:00, 28.1MB/s]Traceback (most recent call last):
File "run_mlm_tf.py", line 794, in <module>
main()
File "run_mlm_tf.py", line 425, in main
raw_datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir) File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/load.py", line 843, in load_dataset
builder_instance.download_and_prepare(
File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py", line 608, in download_and_prepare
self._download_and_prepare(
File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py", line 698, in _download_and_prepare verify_splits(self.info.splits, split_dict) File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 74, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='validation', num_bytes=38165657946, num_examples=13799838, dataset_name='c4'), 'recorded': SplitInfo(name='validation', num_bytes=37875873, num_examples=13863, dataset_name='c4')}]
## Environment info
- `datasets` version: 1.10.2
- Platform: Linux-5.4.0-58-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 4.0.1
Hi @danshirron,
`c4` was updated few days back by @lhoestq. The new configs are `['en', 'en.noclean', 'en.realnewslike', 'en.webtextlike'].` You'll need to remove any older version of this dataset you previously downloaded and then run `load_dataset` again with new configuration. | [
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https://github.com/huggingface/datasets/issues/2761 | Error loading C4 realnewslike dataset | @bhavitvyamalik @lhoestq , just tried the above and got:
>>> a=datasets.load_dataset('c4','en.realnewslike')
Downloading: 3.29kB [00:00, 1.66MB/s]
Downloading: 2.40MB [00:00, 12.6MB/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/load.py", line 819, in load_dataset
builder_instance = load_dataset_builder(
File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/load.py", line 701, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py", line 1049, in __init__
super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)
File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py", line 268, in __init__
self.config, self.config_id = self._create_builder_config(
File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py", line 360, in _create_builder_config
raise ValueError(
ValueError: BuilderConfig en.realnewslike not found. Available: ['en', 'realnewslike', 'en.noblocklist', 'en.noclean']
>>>
datasets version is 1.11.0
| ## Describe the bug
Error loading C4 realnewslike dataset. Validation part mismatch
## Steps to reproduce the bug
```python
raw_datasets = load_dataset('c4', 'realnewslike', cache_dir=model_args.cache_dir)
## Expected results
success on data loading
## Actual results
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15.3M/15.3M [00:00<00:00, 28.1MB/s]Traceback (most recent call last):
File "run_mlm_tf.py", line 794, in <module>
main()
File "run_mlm_tf.py", line 425, in main
raw_datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir) File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/load.py", line 843, in load_dataset
builder_instance.download_and_prepare(
File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py", line 608, in download_and_prepare
self._download_and_prepare(
File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py", line 698, in _download_and_prepare verify_splits(self.info.splits, split_dict) File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 74, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='validation', num_bytes=38165657946, num_examples=13799838, dataset_name='c4'), 'recorded': SplitInfo(name='validation', num_bytes=37875873, num_examples=13863, dataset_name='c4')}]
## Environment info
- `datasets` version: 1.10.2
- Platform: Linux-5.4.0-58-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 4.0.1 | 91 | Error loading C4 realnewslike dataset
## Describe the bug
Error loading C4 realnewslike dataset. Validation part mismatch
## Steps to reproduce the bug
```python
raw_datasets = load_dataset('c4', 'realnewslike', cache_dir=model_args.cache_dir)
## Expected results
success on data loading
## Actual results
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15.3M/15.3M [00:00<00:00, 28.1MB/s]Traceback (most recent call last):
File "run_mlm_tf.py", line 794, in <module>
main()
File "run_mlm_tf.py", line 425, in main
raw_datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir) File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/load.py", line 843, in load_dataset
builder_instance.download_and_prepare(
File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py", line 608, in download_and_prepare
self._download_and_prepare(
File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py", line 698, in _download_and_prepare verify_splits(self.info.splits, split_dict) File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 74, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='validation', num_bytes=38165657946, num_examples=13799838, dataset_name='c4'), 'recorded': SplitInfo(name='validation', num_bytes=37875873, num_examples=13863, dataset_name='c4')}]
## Environment info
- `datasets` version: 1.10.2
- Platform: Linux-5.4.0-58-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 4.0.1
@bhavitvyamalik @lhoestq , just tried the above and got:
>>> a=datasets.load_dataset('c4','en.realnewslike')
Downloading: 3.29kB [00:00, 1.66MB/s]
Downloading: 2.40MB [00:00, 12.6MB/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/load.py", line 819, in load_dataset
builder_instance = load_dataset_builder(
File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/load.py", line 701, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py", line 1049, in __init__
super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)
File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py", line 268, in __init__
self.config, self.config_id = self._create_builder_config(
File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py", line 360, in _create_builder_config
raise ValueError(
ValueError: BuilderConfig en.realnewslike not found. Available: ['en', 'realnewslike', 'en.noblocklist', 'en.noclean']
>>>
datasets version is 1.11.0
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https://github.com/huggingface/datasets/issues/2761 | Error loading C4 realnewslike dataset | I think I had an older version of datasets installed and that's why I commented the old configurations in my last comment, my bad! I re-checked and updated it to latest version (`datasets==1.11.0`) and it's showing `available configs: ['en', 'realnewslike', 'en.noblocklist', 'en.noclean']`.
I tried `raw_datasets = load_dataset('c4', 'realnewslike')` and the download started. Make sure you don't have any old copy of this dataset and you download it fresh using the latest version of datasets. Sorry for the mix up! | ## Describe the bug
Error loading C4 realnewslike dataset. Validation part mismatch
## Steps to reproduce the bug
```python
raw_datasets = load_dataset('c4', 'realnewslike', cache_dir=model_args.cache_dir)
## Expected results
success on data loading
## Actual results
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15.3M/15.3M [00:00<00:00, 28.1MB/s]Traceback (most recent call last):
File "run_mlm_tf.py", line 794, in <module>
main()
File "run_mlm_tf.py", line 425, in main
raw_datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir) File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/load.py", line 843, in load_dataset
builder_instance.download_and_prepare(
File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py", line 608, in download_and_prepare
self._download_and_prepare(
File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py", line 698, in _download_and_prepare verify_splits(self.info.splits, split_dict) File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 74, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='validation', num_bytes=38165657946, num_examples=13799838, dataset_name='c4'), 'recorded': SplitInfo(name='validation', num_bytes=37875873, num_examples=13863, dataset_name='c4')}]
## Environment info
- `datasets` version: 1.10.2
- Platform: Linux-5.4.0-58-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 4.0.1 | 79 | Error loading C4 realnewslike dataset
## Describe the bug
Error loading C4 realnewslike dataset. Validation part mismatch
## Steps to reproduce the bug
```python
raw_datasets = load_dataset('c4', 'realnewslike', cache_dir=model_args.cache_dir)
## Expected results
success on data loading
## Actual results
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15.3M/15.3M [00:00<00:00, 28.1MB/s]Traceback (most recent call last):
File "run_mlm_tf.py", line 794, in <module>
main()
File "run_mlm_tf.py", line 425, in main
raw_datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir) File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/load.py", line 843, in load_dataset
builder_instance.download_and_prepare(
File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py", line 608, in download_and_prepare
self._download_and_prepare(
File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py", line 698, in _download_and_prepare verify_splits(self.info.splits, split_dict) File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 74, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='validation', num_bytes=38165657946, num_examples=13799838, dataset_name='c4'), 'recorded': SplitInfo(name='validation', num_bytes=37875873, num_examples=13863, dataset_name='c4')}]
## Environment info
- `datasets` version: 1.10.2
- Platform: Linux-5.4.0-58-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 4.0.1
I think I had an older version of datasets installed and that's why I commented the old configurations in my last comment, my bad! I re-checked and updated it to latest version (`datasets==1.11.0`) and it's showing `available configs: ['en', 'realnewslike', 'en.noblocklist', 'en.noclean']`.
I tried `raw_datasets = load_dataset('c4', 'realnewslike')` and the download started. Make sure you don't have any old copy of this dataset and you download it fresh using the latest version of datasets. Sorry for the mix up! | [
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https://github.com/huggingface/datasets/issues/2761 | Error loading C4 realnewslike dataset | It works. I probably had some issue with the cache. after cleaning it im able to download the dataset. Thanks | ## Describe the bug
Error loading C4 realnewslike dataset. Validation part mismatch
## Steps to reproduce the bug
```python
raw_datasets = load_dataset('c4', 'realnewslike', cache_dir=model_args.cache_dir)
## Expected results
success on data loading
## Actual results
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15.3M/15.3M [00:00<00:00, 28.1MB/s]Traceback (most recent call last):
File "run_mlm_tf.py", line 794, in <module>
main()
File "run_mlm_tf.py", line 425, in main
raw_datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir) File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/load.py", line 843, in load_dataset
builder_instance.download_and_prepare(
File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py", line 608, in download_and_prepare
self._download_and_prepare(
File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py", line 698, in _download_and_prepare verify_splits(self.info.splits, split_dict) File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 74, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='validation', num_bytes=38165657946, num_examples=13799838, dataset_name='c4'), 'recorded': SplitInfo(name='validation', num_bytes=37875873, num_examples=13863, dataset_name='c4')}]
## Environment info
- `datasets` version: 1.10.2
- Platform: Linux-5.4.0-58-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 4.0.1 | 20 | Error loading C4 realnewslike dataset
## Describe the bug
Error loading C4 realnewslike dataset. Validation part mismatch
## Steps to reproduce the bug
```python
raw_datasets = load_dataset('c4', 'realnewslike', cache_dir=model_args.cache_dir)
## Expected results
success on data loading
## Actual results
Downloading: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15.3M/15.3M [00:00<00:00, 28.1MB/s]Traceback (most recent call last):
File "run_mlm_tf.py", line 794, in <module>
main()
File "run_mlm_tf.py", line 425, in main
raw_datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir) File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/load.py", line 843, in load_dataset
builder_instance.download_and_prepare(
File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py", line 608, in download_and_prepare
self._download_and_prepare(
File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/builder.py", line 698, in _download_and_prepare verify_splits(self.info.splits, split_dict) File "/home/dshirron/.local/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 74, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='validation', num_bytes=38165657946, num_examples=13799838, dataset_name='c4'), 'recorded': SplitInfo(name='validation', num_bytes=37875873, num_examples=13863, dataset_name='c4')}]
## Environment info
- `datasets` version: 1.10.2
- Platform: Linux-5.4.0-58-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- PyArrow version: 4.0.1
It works. I probably had some issue with the cache. after cleaning it im able to download the dataset. Thanks | [
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] |
https://github.com/huggingface/datasets/issues/2759 | the meteor metric seems not consist with the official version | the issue is caused by the differences between varied meteor versions:
meteor1.0 is for https://aclanthology.org/W07-0734.pdf
meteor1.5 is for https://aclanthology.org/W14-3348.pdf
here is a very similar issue in NLTK
https://github.com/nltk/nltk/issues/2655 | ## Describe the bug
The computed meteor score seems strange because the value is very different from the scores computed by other tools. For example, I use the meteor score computed by [NLGeval](https://github.com/Maluuba/nlg-eval) as the reference (which reuses the official jar file for the computation)
## Steps to reproduce the bug
```python
from datasets import load_metric
from nlgeval import NLGEval, compute_individual_metrics
meteor = load_metric('meteor')
predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
results = meteor.compute(predictions=predictions, references=references)
# print the actual result
print(round(results["meteor"], 4))
metrics_dict = compute_individual_metrics(references, predictions[0])
# print the expected result
print(round(metrics_dict["METEOR"], 4))
```
By the way, you need to install the `nlg-eval` library first. Please check the installation guide [here](https://github.com/Maluuba/nlg-eval#setup), thanks!
## Expected results
`0.4474`
## Actual results
`0.7398`
## Environment info
- `datasets` version: 1.10.2
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.5
- PyArrow version: 4.0.1
| 28 | the meteor metric seems not consist with the official version
## Describe the bug
The computed meteor score seems strange because the value is very different from the scores computed by other tools. For example, I use the meteor score computed by [NLGeval](https://github.com/Maluuba/nlg-eval) as the reference (which reuses the official jar file for the computation)
## Steps to reproduce the bug
```python
from datasets import load_metric
from nlgeval import NLGEval, compute_individual_metrics
meteor = load_metric('meteor')
predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
results = meteor.compute(predictions=predictions, references=references)
# print the actual result
print(round(results["meteor"], 4))
metrics_dict = compute_individual_metrics(references, predictions[0])
# print the expected result
print(round(metrics_dict["METEOR"], 4))
```
By the way, you need to install the `nlg-eval` library first. Please check the installation guide [here](https://github.com/Maluuba/nlg-eval#setup), thanks!
## Expected results
`0.4474`
## Actual results
`0.7398`
## Environment info
- `datasets` version: 1.10.2
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.5
- PyArrow version: 4.0.1
the issue is caused by the differences between varied meteor versions:
meteor1.0 is for https://aclanthology.org/W07-0734.pdf
meteor1.5 is for https://aclanthology.org/W14-3348.pdf
here is a very similar issue in NLTK
https://github.com/nltk/nltk/issues/2655 | [
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https://github.com/huggingface/datasets/issues/2759 | the meteor metric seems not consist with the official version | Hi @jianguda, thanks for reporting.
Currently, at 🤗 `datasets` we are using METEOR 1.0 (indeed using NLTK: `from nltk.translate import meteor_score`): See the [citation here](https://github.com/huggingface/datasets/blob/master/metrics/meteor/meteor.py#L23-L35).
If there is some open source implementation of METEOR 1.5, that could be an interesting contribution! 😉 | ## Describe the bug
The computed meteor score seems strange because the value is very different from the scores computed by other tools. For example, I use the meteor score computed by [NLGeval](https://github.com/Maluuba/nlg-eval) as the reference (which reuses the official jar file for the computation)
## Steps to reproduce the bug
```python
from datasets import load_metric
from nlgeval import NLGEval, compute_individual_metrics
meteor = load_metric('meteor')
predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
results = meteor.compute(predictions=predictions, references=references)
# print the actual result
print(round(results["meteor"], 4))
metrics_dict = compute_individual_metrics(references, predictions[0])
# print the expected result
print(round(metrics_dict["METEOR"], 4))
```
By the way, you need to install the `nlg-eval` library first. Please check the installation guide [here](https://github.com/Maluuba/nlg-eval#setup), thanks!
## Expected results
`0.4474`
## Actual results
`0.7398`
## Environment info
- `datasets` version: 1.10.2
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.5
- PyArrow version: 4.0.1
| 42 | the meteor metric seems not consist with the official version
## Describe the bug
The computed meteor score seems strange because the value is very different from the scores computed by other tools. For example, I use the meteor score computed by [NLGeval](https://github.com/Maluuba/nlg-eval) as the reference (which reuses the official jar file for the computation)
## Steps to reproduce the bug
```python
from datasets import load_metric
from nlgeval import NLGEval, compute_individual_metrics
meteor = load_metric('meteor')
predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
results = meteor.compute(predictions=predictions, references=references)
# print the actual result
print(round(results["meteor"], 4))
metrics_dict = compute_individual_metrics(references, predictions[0])
# print the expected result
print(round(metrics_dict["METEOR"], 4))
```
By the way, you need to install the `nlg-eval` library first. Please check the installation guide [here](https://github.com/Maluuba/nlg-eval#setup), thanks!
## Expected results
`0.4474`
## Actual results
`0.7398`
## Environment info
- `datasets` version: 1.10.2
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.5
- PyArrow version: 4.0.1
Hi @jianguda, thanks for reporting.
Currently, at 🤗 `datasets` we are using METEOR 1.0 (indeed using NLTK: `from nltk.translate import meteor_score`): See the [citation here](https://github.com/huggingface/datasets/blob/master/metrics/meteor/meteor.py#L23-L35).
If there is some open source implementation of METEOR 1.5, that could be an interesting contribution! 😉 | [
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https://github.com/huggingface/datasets/issues/2757 | Unexpected type after `concatenate_datasets` | Hi @JulesBelveze, thanks for your question.
Note that 🤗 `datasets` internally store their data in Apache Arrow format.
However, when accessing dataset columns, by default they are returned as native Python objects (lists in this case).
If you would like their columns to be returned in a more suitable format for your use case (torch arrays), you can use the method `set_format()`:
```python
concat_dataset.set_format(type="torch")
```
You have detailed information in our docs:
- [Using a Dataset with PyTorch/Tensorflow](https://huggingface.co/docs/datasets/torch_tensorflow.html)
- [Dataset.set_format()](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.set_format) | ## Describe the bug
I am trying to concatenate two `Dataset` using `concatenate_datasets` but it turns out that after concatenation the features are casted from `torch.Tensor` to `list`.
It then leads to a weird tensors when trying to convert it to a `DataLoader`. However, if I use each `Dataset` separately everything behave as expected.
## Steps to reproduce the bug
```python
>>> featurized_teacher
Dataset({
features: ['t_labels', 't_input_ids', 't_token_type_ids', 't_attention_mask'],
num_rows: 502
})
>>> for f in featurized_teacher.features:
print(featurized_teacher[f].shape)
torch.Size([502])
torch.Size([502, 300])
torch.Size([502, 300])
torch.Size([502, 300])
>>> featurized_student
Dataset({
features: ['s_features', 's_labels'],
num_rows: 502
})
>>> for f in featurized_student.features:
print(featurized_student[f].shape)
torch.Size([502, 64])
torch.Size([502])
```
The shapes seem alright to me. Then the results after concatenation are as follow:
```python
>>> concat_dataset = datasets.concatenate_datasets([featurized_student, featurized_teacher], axis=1)
>>> type(concat_dataset["t_labels"])
<class 'list'>
```
One would expect to obtain the same type as the one before concatenation.
Am I doing something wrong here? Any idea on how to fix this unexpected behavior?
## Environment info
- `datasets` version: 1.9.0
- Platform: macOS-10.14.6-x86_64-i386-64bit
- Python version: 3.9.5
- PyArrow version: 3.0.0
| 80 | Unexpected type after `concatenate_datasets`
## Describe the bug
I am trying to concatenate two `Dataset` using `concatenate_datasets` but it turns out that after concatenation the features are casted from `torch.Tensor` to `list`.
It then leads to a weird tensors when trying to convert it to a `DataLoader`. However, if I use each `Dataset` separately everything behave as expected.
## Steps to reproduce the bug
```python
>>> featurized_teacher
Dataset({
features: ['t_labels', 't_input_ids', 't_token_type_ids', 't_attention_mask'],
num_rows: 502
})
>>> for f in featurized_teacher.features:
print(featurized_teacher[f].shape)
torch.Size([502])
torch.Size([502, 300])
torch.Size([502, 300])
torch.Size([502, 300])
>>> featurized_student
Dataset({
features: ['s_features', 's_labels'],
num_rows: 502
})
>>> for f in featurized_student.features:
print(featurized_student[f].shape)
torch.Size([502, 64])
torch.Size([502])
```
The shapes seem alright to me. Then the results after concatenation are as follow:
```python
>>> concat_dataset = datasets.concatenate_datasets([featurized_student, featurized_teacher], axis=1)
>>> type(concat_dataset["t_labels"])
<class 'list'>
```
One would expect to obtain the same type as the one before concatenation.
Am I doing something wrong here? Any idea on how to fix this unexpected behavior?
## Environment info
- `datasets` version: 1.9.0
- Platform: macOS-10.14.6-x86_64-i386-64bit
- Python version: 3.9.5
- PyArrow version: 3.0.0
Hi @JulesBelveze, thanks for your question.
Note that 🤗 `datasets` internally store their data in Apache Arrow format.
However, when accessing dataset columns, by default they are returned as native Python objects (lists in this case).
If you would like their columns to be returned in a more suitable format for your use case (torch arrays), you can use the method `set_format()`:
```python
concat_dataset.set_format(type="torch")
```
You have detailed information in our docs:
- [Using a Dataset with PyTorch/Tensorflow](https://huggingface.co/docs/datasets/torch_tensorflow.html)
- [Dataset.set_format()](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.set_format) | [
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] |
https://github.com/huggingface/datasets/issues/2750 | Second concatenation of datasets produces errors | Hi @Aktsvigun! We are planning to address this issue before our next release, in a couple of weeks at most. 😅
In the meantime, if you would like to contribute, feel free to open a Pull Request. You are welcome. Here you can find more information: [How to contribute to Datasets?](CONTRIBUTING.md) | Hi,
I am need to concatenate my dataset with others several times, and after I concatenate it for the second time, the features of features (e.g. tags names) are collapsed. This hinders, for instance, the usage of tokenize function with `data.map`.
```
from datasets import load_dataset, concatenate_datasets
data = load_dataset('trec')['train']
concatenated = concatenate_datasets([data, data])
concatenated_2 = concatenate_datasets([concatenated, concatenated])
print('True features of features:', concatenated.features)
print('\nProduced features of features:', concatenated_2.features)
```
outputs
```
True features of features: {'label-coarse': ClassLabel(num_classes=6, names=['DESC', 'ENTY', 'ABBR', 'HUM', 'NUM', 'LOC'], names_file=None, id=None), 'label-fine': ClassLabel(num_classes=47, names=['manner', 'cremat', 'animal', 'exp', 'ind', 'gr', 'title', 'def', 'date', 'reason', 'event', 'state', 'desc', 'count', 'other', 'letter', 'religion', 'food', 'country', 'color', 'termeq', 'city', 'body', 'dismed', 'mount', 'money', 'product', 'period', 'substance', 'sport', 'plant', 'techmeth', 'volsize', 'instru', 'abb', 'speed', 'word', 'lang', 'perc', 'code', 'dist', 'temp', 'symbol', 'ord', 'veh', 'weight', 'currency'], names_file=None, id=None), 'text': Value(dtype='string', id=None)}
Produced features of features: {'label-coarse': Value(dtype='int64', id=None), 'label-fine': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None)}
```
I am using `datasets` v.1.11.0 | 51 | Second concatenation of datasets produces errors
Hi,
I am need to concatenate my dataset with others several times, and after I concatenate it for the second time, the features of features (e.g. tags names) are collapsed. This hinders, for instance, the usage of tokenize function with `data.map`.
```
from datasets import load_dataset, concatenate_datasets
data = load_dataset('trec')['train']
concatenated = concatenate_datasets([data, data])
concatenated_2 = concatenate_datasets([concatenated, concatenated])
print('True features of features:', concatenated.features)
print('\nProduced features of features:', concatenated_2.features)
```
outputs
```
True features of features: {'label-coarse': ClassLabel(num_classes=6, names=['DESC', 'ENTY', 'ABBR', 'HUM', 'NUM', 'LOC'], names_file=None, id=None), 'label-fine': ClassLabel(num_classes=47, names=['manner', 'cremat', 'animal', 'exp', 'ind', 'gr', 'title', 'def', 'date', 'reason', 'event', 'state', 'desc', 'count', 'other', 'letter', 'religion', 'food', 'country', 'color', 'termeq', 'city', 'body', 'dismed', 'mount', 'money', 'product', 'period', 'substance', 'sport', 'plant', 'techmeth', 'volsize', 'instru', 'abb', 'speed', 'word', 'lang', 'perc', 'code', 'dist', 'temp', 'symbol', 'ord', 'veh', 'weight', 'currency'], names_file=None, id=None), 'text': Value(dtype='string', id=None)}
Produced features of features: {'label-coarse': Value(dtype='int64', id=None), 'label-fine': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None)}
```
I am using `datasets` v.1.11.0
Hi @Aktsvigun! We are planning to address this issue before our next release, in a couple of weeks at most. 😅
In the meantime, if you would like to contribute, feel free to open a Pull Request. You are welcome. Here you can find more information: [How to contribute to Datasets?](CONTRIBUTING.md) | [
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https://github.com/huggingface/datasets/issues/2749 | Raise a proper exception when trying to stream a dataset that requires to manually download files | Hi @severo, thanks for reporting.
As discussed, datasets requiring manual download should be:
- programmatically identifiable
- properly handled with more clear error message when trying to load them with streaming
In relation with programmatically identifiability, note that for datasets requiring manual download, their builder have a property `manual_download_instructions` which is not None:
```python
# Dataset requiring manual download:
builder.manual_download_instructions is not None
``` | ## Describe the bug
At least for 'reclor', 'telugu_books', 'turkish_movie_sentiment', 'ubuntu_dialogs_corpus', 'wikihow', trying to `load_dataset` in streaming mode raises a `TypeError` without any detail about why it fails.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("reclor", streaming=True)
```
## Expected results
Ideally: raise a specific exception, something like `ManualDownloadError`.
Or at least give the reason in the message, as when we load in normal mode:
```python
from datasets import load_dataset
dataset = load_dataset("reclor")
```
```
AssertionError: The dataset reclor with config default requires manual data.
Please follow the manual download instructions: to use ReClor you need to download it manually. Please go to its homepage (http://whyu.me/reclor/) fill the google
form and you will receive a download link and a password to extract it.Please extract all files in one folder and use the path folder in datasets.load_dataset('reclor', data_dir='path/to/folder/folder_name')
.
Manual data can be loaded with `datasets.load_dataset(reclor, data_dir='<path/to/manual/data>')
```
## Actual results
```
TypeError: expected str, bytes or os.PathLike object, not NoneType
```
## Environment info
- `datasets` version: 1.11.0
- Platform: macOS-11.5-x86_64-i386-64bit
- Python version: 3.8.11
- PyArrow version: 4.0.1
| 64 | Raise a proper exception when trying to stream a dataset that requires to manually download files
## Describe the bug
At least for 'reclor', 'telugu_books', 'turkish_movie_sentiment', 'ubuntu_dialogs_corpus', 'wikihow', trying to `load_dataset` in streaming mode raises a `TypeError` without any detail about why it fails.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("reclor", streaming=True)
```
## Expected results
Ideally: raise a specific exception, something like `ManualDownloadError`.
Or at least give the reason in the message, as when we load in normal mode:
```python
from datasets import load_dataset
dataset = load_dataset("reclor")
```
```
AssertionError: The dataset reclor with config default requires manual data.
Please follow the manual download instructions: to use ReClor you need to download it manually. Please go to its homepage (http://whyu.me/reclor/) fill the google
form and you will receive a download link and a password to extract it.Please extract all files in one folder and use the path folder in datasets.load_dataset('reclor', data_dir='path/to/folder/folder_name')
.
Manual data can be loaded with `datasets.load_dataset(reclor, data_dir='<path/to/manual/data>')
```
## Actual results
```
TypeError: expected str, bytes or os.PathLike object, not NoneType
```
## Environment info
- `datasets` version: 1.11.0
- Platform: macOS-11.5-x86_64-i386-64bit
- Python version: 3.8.11
- PyArrow version: 4.0.1
Hi @severo, thanks for reporting.
As discussed, datasets requiring manual download should be:
- programmatically identifiable
- properly handled with more clear error message when trying to load them with streaming
In relation with programmatically identifiability, note that for datasets requiring manual download, their builder have a property `manual_download_instructions` which is not None:
```python
# Dataset requiring manual download:
builder.manual_download_instructions is not None
``` | [
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https://github.com/huggingface/datasets/issues/2746 | Cannot load `few-nerd` dataset | Hi @Mehrad0711,
I'm afraid there is no "canonical" Hugging Face dataset named "few-nerd".
There are 2 kinds of datasets hosted at the Hugging Face Hub:
- canonical datasets (their identifier contains no slash "/"): we, the Hugging Face team, supervise their implementation and we make sure they work correctly by means of our test suite
- community datasets (their identifier contains a slash "/", where before the slash it is the username or the organization name): those datasets are uploaded to the Hub by the community, and we, the Hugging Face team, do not supervise them; it is the responsibility of the user/organization implementing them properly if they want them to be used by other users.
In this specific case, there is no "canonical" dataset named "few-nerd". On the other hand, there are two "community" datasets named "few-nerd":
- ["nbroad/few-nerd"](https://huggingface.co/datasets/nbroad/few-nerd)
- ["dfki-nlp/few-nerd"](https://huggingface.co/datasets/dfki-nlp/few-nerd)
If they were properly implemented, you should be able to load them this way:
```python
# "nbroad/few-nerd" community dataset
ds = load_dataset("nbroad/few-nerd", "supervised")
# "dfki-nlp/few-nerd" community dataset
ds = load_dataset("dfki-nlp/few-nerd", "supervised")
```
However, they are not correctly implemented and both of them give errors:
- "nbroad/few-nerd":
```
TypeError: expected str, bytes or os.PathLike object, not dict
```
- "dfki-nlp/few-nerd":
```
ConnectionError: Couldn't reach https://cloud.tsinghua.edu.cn/f/09265750ae6340429827/?dl=1
```
You could try to contact their users/organizations to inform them about their bugs and ask them if they are planning to fix them. Alternatively you could try to implement your own script for this dataset. | ## Describe the bug
Cannot load `few-nerd` dataset.
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset('few-nerd', 'supervised')
```
## Actual results
Executing above code will give the following error:
```
Using the latest cached version of the module from /Users/Mehrad/.cache/huggingface/modules/datasets_modules/datasets/few-nerd/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53 (last modified on Wed Jun 2 11:34:25 2021) since it couldn't be found locally at /Users/Mehrad/Documents/GitHub/genienlp/few-nerd/few-nerd.py, or remotely (FileNotFoundError).
Downloading and preparing dataset few_nerd/supervised (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /Users/Mehrad/.cache/huggingface/datasets/few_nerd/supervised/0.0.0/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53...
Traceback (most recent call last):
File "/Users/Mehrad/opt/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 693, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/Users/Mehrad/opt/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1107, in _prepare_split
disable=bool(logging.get_verbosity() == logging.NOTSET),
File "/Users/Mehrad/opt/anaconda3/lib/python3.7/site-packages/tqdm/std.py", line 1133, in __iter__
for obj in iterable:
File "/Users/Mehrad/.cache/huggingface/modules/datasets_modules/datasets/few-nerd/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53/few-nerd.py", line 196, in _generate_examples
with open(filepath, encoding="utf-8") as f:
FileNotFoundError: [Errno 2] No such file or directory: '/Users/Mehrad/.cache/huggingface/datasets/downloads/supervised/train.json'
```
The bug is probably in identifying and downloading the dataset. If I download the json splits directly from [link](https://github.com/nbroad1881/few-nerd/tree/main/uncompressed) and put them under the downloads directory, they will be processed into arrow format correctly.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Python version: 3.8
- PyArrow version: 1.0.1
| 242 | Cannot load `few-nerd` dataset
## Describe the bug
Cannot load `few-nerd` dataset.
## Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset('few-nerd', 'supervised')
```
## Actual results
Executing above code will give the following error:
```
Using the latest cached version of the module from /Users/Mehrad/.cache/huggingface/modules/datasets_modules/datasets/few-nerd/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53 (last modified on Wed Jun 2 11:34:25 2021) since it couldn't be found locally at /Users/Mehrad/Documents/GitHub/genienlp/few-nerd/few-nerd.py, or remotely (FileNotFoundError).
Downloading and preparing dataset few_nerd/supervised (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /Users/Mehrad/.cache/huggingface/datasets/few_nerd/supervised/0.0.0/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53...
Traceback (most recent call last):
File "/Users/Mehrad/opt/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 693, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/Users/Mehrad/opt/anaconda3/lib/python3.7/site-packages/datasets/builder.py", line 1107, in _prepare_split
disable=bool(logging.get_verbosity() == logging.NOTSET),
File "/Users/Mehrad/opt/anaconda3/lib/python3.7/site-packages/tqdm/std.py", line 1133, in __iter__
for obj in iterable:
File "/Users/Mehrad/.cache/huggingface/modules/datasets_modules/datasets/few-nerd/62464ace912a40a0f33a11a8310f9041c9dc3590ff2b3c77c14d83ca53cfec53/few-nerd.py", line 196, in _generate_examples
with open(filepath, encoding="utf-8") as f:
FileNotFoundError: [Errno 2] No such file or directory: '/Users/Mehrad/.cache/huggingface/datasets/downloads/supervised/train.json'
```
The bug is probably in identifying and downloading the dataset. If I download the json splits directly from [link](https://github.com/nbroad1881/few-nerd/tree/main/uncompressed) and put them under the downloads directory, they will be processed into arrow format correctly.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Python version: 3.8
- PyArrow version: 1.0.1
Hi @Mehrad0711,
I'm afraid there is no "canonical" Hugging Face dataset named "few-nerd".
There are 2 kinds of datasets hosted at the Hugging Face Hub:
- canonical datasets (their identifier contains no slash "/"): we, the Hugging Face team, supervise their implementation and we make sure they work correctly by means of our test suite
- community datasets (their identifier contains a slash "/", where before the slash it is the username or the organization name): those datasets are uploaded to the Hub by the community, and we, the Hugging Face team, do not supervise them; it is the responsibility of the user/organization implementing them properly if they want them to be used by other users.
In this specific case, there is no "canonical" dataset named "few-nerd". On the other hand, there are two "community" datasets named "few-nerd":
- ["nbroad/few-nerd"](https://huggingface.co/datasets/nbroad/few-nerd)
- ["dfki-nlp/few-nerd"](https://huggingface.co/datasets/dfki-nlp/few-nerd)
If they were properly implemented, you should be able to load them this way:
```python
# "nbroad/few-nerd" community dataset
ds = load_dataset("nbroad/few-nerd", "supervised")
# "dfki-nlp/few-nerd" community dataset
ds = load_dataset("dfki-nlp/few-nerd", "supervised")
```
However, they are not correctly implemented and both of them give errors:
- "nbroad/few-nerd":
```
TypeError: expected str, bytes or os.PathLike object, not dict
```
- "dfki-nlp/few-nerd":
```
ConnectionError: Couldn't reach https://cloud.tsinghua.edu.cn/f/09265750ae6340429827/?dl=1
```
You could try to contact their users/organizations to inform them about their bugs and ask them if they are planning to fix them. Alternatively you could try to implement your own script for this dataset. | [
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https://github.com/huggingface/datasets/issues/2743 | Dataset JSON is incorrect | As discussed, the metadata JSON files must be regenerated because the keys were nor properly generated and they will not be read by the builder:
> Indeed there is some problem/bug while reading the datasets_info.json file: there is a mismatch with the config.name keys in the file...
In the meanwhile, in order to be able to use the datasets_info.json file content, you can create the builder without passing the name :
```
In [25]: builder = datasets.load_dataset_builder("journalists_questions")
In [26]: builder.info.splits
Out[26]: {'train': SplitInfo(name='train', num_bytes=342296, num_examples=10077, dataset_name='journalists_questions')}
```
After regenerating the metadata JSON file for this dataset, I get the right key:
```
{"plain_text": {"description": "The journalists_questions corpus (
``` | ## Describe the bug
The JSON file generated for https://github.com/huggingface/datasets/blob/573f3d35081cee239d1b962878206e9abe6cde91/datasets/journalists_questions/journalists_questions.py is https://github.com/huggingface/datasets/blob/573f3d35081cee239d1b962878206e9abe6cde91/datasets/journalists_questions/dataset_infos.json.
The only config should be `plain_text`, but the first key in the JSON is `journalists_questions` (the dataset id) instead.
```json
{
"journalists_questions": {
"description": "The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\n",
...
```
## Steps to reproduce the bug
Look at the files.
## Expected results
The first key should be `plain_text`:
```json
{
"plain_text": {
"description": "The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\n",
...
```
## Actual results
```json
{
"journalists_questions": {
"description": "The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\n",
...
```
| 109 | Dataset JSON is incorrect
## Describe the bug
The JSON file generated for https://github.com/huggingface/datasets/blob/573f3d35081cee239d1b962878206e9abe6cde91/datasets/journalists_questions/journalists_questions.py is https://github.com/huggingface/datasets/blob/573f3d35081cee239d1b962878206e9abe6cde91/datasets/journalists_questions/dataset_infos.json.
The only config should be `plain_text`, but the first key in the JSON is `journalists_questions` (the dataset id) instead.
```json
{
"journalists_questions": {
"description": "The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\n",
...
```
## Steps to reproduce the bug
Look at the files.
## Expected results
The first key should be `plain_text`:
```json
{
"plain_text": {
"description": "The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\n",
...
```
## Actual results
```json
{
"journalists_questions": {
"description": "The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic\ntweets manually labeled for question identification over Arabic tweets posted by journalists.\n",
...
```
As discussed, the metadata JSON files must be regenerated because the keys were nor properly generated and they will not be read by the builder:
> Indeed there is some problem/bug while reading the datasets_info.json file: there is a mismatch with the config.name keys in the file...
In the meanwhile, in order to be able to use the datasets_info.json file content, you can create the builder without passing the name :
```
In [25]: builder = datasets.load_dataset_builder("journalists_questions")
In [26]: builder.info.splits
Out[26]: {'train': SplitInfo(name='train', num_bytes=342296, num_examples=10077, dataset_name='journalists_questions')}
```
After regenerating the metadata JSON file for this dataset, I get the right key:
```
{"plain_text": {"description": "The journalists_questions corpus (
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https://github.com/huggingface/datasets/issues/2742 | Improve detection of streamable file types | maybe we should rather attempt to download a `Range` from the server and see if it works? | **Is your feature request related to a problem? Please describe.**
```python
from datasets import load_dataset_builder
from datasets.utils.streaming_download_manager import StreamingDownloadManager
builder = load_dataset_builder("journalists_questions", name="plain_text")
builder._split_generators(StreamingDownloadManager(base_path=builder.base_path))
```
raises
```
NotImplementedError: Extraction protocol for file at https://drive.google.com/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U is not implemented yet
```
But the file at https://drive.google.com/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U is a text file and it can be streamed:
```bash
curl --header "Range: bytes=0-100" -L https://drive.google.com/uc\?export\=download\&id\=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U
506938088174940160 yes 1
302221719412830209 yes 1
289761704907268096 yes 1
513820885032378369 yes %
```
Yet, it's wrongly categorized as a file type that cannot be streamed because the test is currently based on 1. the presence of a file extension at the end of the URL (here: no extension), and 2. the inclusion of this extension in a list of supported formats.
**Describe the solution you'd like**
In the case of an URL (instead of a local path), ask for the MIME type, and decide on that value? Note that it would not work in that case, because the value of `content_type` is `text/html; charset=UTF-8`.
**Describe alternatives you've considered**
Add a variable in the dataset script to set the data format by hand.
| 17 | Improve detection of streamable file types
**Is your feature request related to a problem? Please describe.**
```python
from datasets import load_dataset_builder
from datasets.utils.streaming_download_manager import StreamingDownloadManager
builder = load_dataset_builder("journalists_questions", name="plain_text")
builder._split_generators(StreamingDownloadManager(base_path=builder.base_path))
```
raises
```
NotImplementedError: Extraction protocol for file at https://drive.google.com/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U is not implemented yet
```
But the file at https://drive.google.com/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U is a text file and it can be streamed:
```bash
curl --header "Range: bytes=0-100" -L https://drive.google.com/uc\?export\=download\&id\=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U
506938088174940160 yes 1
302221719412830209 yes 1
289761704907268096 yes 1
513820885032378369 yes %
```
Yet, it's wrongly categorized as a file type that cannot be streamed because the test is currently based on 1. the presence of a file extension at the end of the URL (here: no extension), and 2. the inclusion of this extension in a list of supported formats.
**Describe the solution you'd like**
In the case of an URL (instead of a local path), ask for the MIME type, and decide on that value? Note that it would not work in that case, because the value of `content_type` is `text/html; charset=UTF-8`.
**Describe alternatives you've considered**
Add a variable in the dataset script to set the data format by hand.
maybe we should rather attempt to download a `Range` from the server and see if it works? | [
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https://github.com/huggingface/datasets/issues/2737 | SacreBLEU update | Hi @devrimcavusoglu,
I tried your code with latest version of `datasets`and `sacrebleu==1.5.1` and it's running fine after changing one small thing:
```
sacrebleu = datasets.load_metric('sacrebleu')
predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
references = [["It is a guide to action that ensures that the military will forever heed Party commands"]] # double brackets here should do the work
results = sacrebleu.compute(predictions=predictions, references=references)
print(results)
output: {'score': 41.180376356915765, 'counts': [11, 8, 6, 4], 'totals': [18, 17, 16, 15], 'precisions': [61.111111111111114, 47.05882352941177, 37.5, 26.666666666666668], 'bp': 1.0, 'sys_len': 18, 'ref_len': 16}
``` | With the latest release of [sacrebleu](https://github.com/mjpost/sacrebleu), `datasets.metrics.sacrebleu` is broken, and getting error.
AttributeError: module 'sacrebleu' has no attribute 'DEFAULT_TOKENIZER'
this happens since in new version of sacrebleu there is no `DEFAULT_TOKENIZER`, but sacrebleu.py tries to import it anyways. This can be fixed currently with fixing `sacrebleu==1.5.0`
## Steps to reproduce the bug
```python
sacrebleu= datasets.load_metric('sacrebleu')
predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
results = sacrebleu.compute(predictions=predictions, references=references)
print(results)
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: Python 3.8.0
- PyArrow version: 5.0.0
| 101 | SacreBLEU update
With the latest release of [sacrebleu](https://github.com/mjpost/sacrebleu), `datasets.metrics.sacrebleu` is broken, and getting error.
AttributeError: module 'sacrebleu' has no attribute 'DEFAULT_TOKENIZER'
this happens since in new version of sacrebleu there is no `DEFAULT_TOKENIZER`, but sacrebleu.py tries to import it anyways. This can be fixed currently with fixing `sacrebleu==1.5.0`
## Steps to reproduce the bug
```python
sacrebleu= datasets.load_metric('sacrebleu')
predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
results = sacrebleu.compute(predictions=predictions, references=references)
print(results)
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: Python 3.8.0
- PyArrow version: 5.0.0
Hi @devrimcavusoglu,
I tried your code with latest version of `datasets`and `sacrebleu==1.5.1` and it's running fine after changing one small thing:
```
sacrebleu = datasets.load_metric('sacrebleu')
predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
references = [["It is a guide to action that ensures that the military will forever heed Party commands"]] # double brackets here should do the work
results = sacrebleu.compute(predictions=predictions, references=references)
print(results)
output: {'score': 41.180376356915765, 'counts': [11, 8, 6, 4], 'totals': [18, 17, 16, 15], 'precisions': [61.111111111111114, 47.05882352941177, 37.5, 26.666666666666668], 'bp': 1.0, 'sys_len': 18, 'ref_len': 16}
``` | [
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https://github.com/huggingface/datasets/issues/2737 | SacreBLEU update | @bhavitvyamalik hmm. I forgot double brackets, but still didn't work when used it with double brackets. It may be an isseu with platform (using win-10 currently), or versions. What is your platform and your version info for datasets, python, and sacrebleu ? | With the latest release of [sacrebleu](https://github.com/mjpost/sacrebleu), `datasets.metrics.sacrebleu` is broken, and getting error.
AttributeError: module 'sacrebleu' has no attribute 'DEFAULT_TOKENIZER'
this happens since in new version of sacrebleu there is no `DEFAULT_TOKENIZER`, but sacrebleu.py tries to import it anyways. This can be fixed currently with fixing `sacrebleu==1.5.0`
## Steps to reproduce the bug
```python
sacrebleu= datasets.load_metric('sacrebleu')
predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
results = sacrebleu.compute(predictions=predictions, references=references)
print(results)
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: Python 3.8.0
- PyArrow version: 5.0.0
| 42 | SacreBLEU update
With the latest release of [sacrebleu](https://github.com/mjpost/sacrebleu), `datasets.metrics.sacrebleu` is broken, and getting error.
AttributeError: module 'sacrebleu' has no attribute 'DEFAULT_TOKENIZER'
this happens since in new version of sacrebleu there is no `DEFAULT_TOKENIZER`, but sacrebleu.py tries to import it anyways. This can be fixed currently with fixing `sacrebleu==1.5.0`
## Steps to reproduce the bug
```python
sacrebleu= datasets.load_metric('sacrebleu')
predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
results = sacrebleu.compute(predictions=predictions, references=references)
print(results)
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: Python 3.8.0
- PyArrow version: 5.0.0
@bhavitvyamalik hmm. I forgot double brackets, but still didn't work when used it with double brackets. It may be an isseu with platform (using win-10 currently), or versions. What is your platform and your version info for datasets, python, and sacrebleu ? | [
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https://github.com/huggingface/datasets/issues/2737 | SacreBLEU update | You can check that here, I've reproduced your code in [Google colab](https://colab.research.google.com/drive/1X90fHRgMLKczOVgVk7NDEw_ciZFDjaCM?usp=sharing). Looks like there was some issue in `sacrebleu` which was fixed later from what I've found [here](https://github.com/pytorch/fairseq/issues/2049#issuecomment-622367967). Upgrading `sacrebleu` to latest version should work. | With the latest release of [sacrebleu](https://github.com/mjpost/sacrebleu), `datasets.metrics.sacrebleu` is broken, and getting error.
AttributeError: module 'sacrebleu' has no attribute 'DEFAULT_TOKENIZER'
this happens since in new version of sacrebleu there is no `DEFAULT_TOKENIZER`, but sacrebleu.py tries to import it anyways. This can be fixed currently with fixing `sacrebleu==1.5.0`
## Steps to reproduce the bug
```python
sacrebleu= datasets.load_metric('sacrebleu')
predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
results = sacrebleu.compute(predictions=predictions, references=references)
print(results)
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: Python 3.8.0
- PyArrow version: 5.0.0
| 36 | SacreBLEU update
With the latest release of [sacrebleu](https://github.com/mjpost/sacrebleu), `datasets.metrics.sacrebleu` is broken, and getting error.
AttributeError: module 'sacrebleu' has no attribute 'DEFAULT_TOKENIZER'
this happens since in new version of sacrebleu there is no `DEFAULT_TOKENIZER`, but sacrebleu.py tries to import it anyways. This can be fixed currently with fixing `sacrebleu==1.5.0`
## Steps to reproduce the bug
```python
sacrebleu= datasets.load_metric('sacrebleu')
predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
results = sacrebleu.compute(predictions=predictions, references=references)
print(results)
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: Python 3.8.0
- PyArrow version: 5.0.0
You can check that here, I've reproduced your code in [Google colab](https://colab.research.google.com/drive/1X90fHRgMLKczOVgVk7NDEw_ciZFDjaCM?usp=sharing). Looks like there was some issue in `sacrebleu` which was fixed later from what I've found [here](https://github.com/pytorch/fairseq/issues/2049#issuecomment-622367967). Upgrading `sacrebleu` to latest version should work. | [
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https://github.com/huggingface/datasets/issues/2737 | SacreBLEU update | It seems that next release of `sacrebleu` (v2.0.0) will break our `datasets` implementation to compute it. See my Google Colab: https://colab.research.google.com/drive/1SKmvvjQi6k_3OHsX5NPkZdiaJIfXyv9X?usp=sharing
I'm reopening this Issue and making a Pull Request to fix it. | With the latest release of [sacrebleu](https://github.com/mjpost/sacrebleu), `datasets.metrics.sacrebleu` is broken, and getting error.
AttributeError: module 'sacrebleu' has no attribute 'DEFAULT_TOKENIZER'
this happens since in new version of sacrebleu there is no `DEFAULT_TOKENIZER`, but sacrebleu.py tries to import it anyways. This can be fixed currently with fixing `sacrebleu==1.5.0`
## Steps to reproduce the bug
```python
sacrebleu= datasets.load_metric('sacrebleu')
predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
results = sacrebleu.compute(predictions=predictions, references=references)
print(results)
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: Python 3.8.0
- PyArrow version: 5.0.0
| 33 | SacreBLEU update
With the latest release of [sacrebleu](https://github.com/mjpost/sacrebleu), `datasets.metrics.sacrebleu` is broken, and getting error.
AttributeError: module 'sacrebleu' has no attribute 'DEFAULT_TOKENIZER'
this happens since in new version of sacrebleu there is no `DEFAULT_TOKENIZER`, but sacrebleu.py tries to import it anyways. This can be fixed currently with fixing `sacrebleu==1.5.0`
## Steps to reproduce the bug
```python
sacrebleu= datasets.load_metric('sacrebleu')
predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
results = sacrebleu.compute(predictions=predictions, references=references)
print(results)
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.11.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: Python 3.8.0
- PyArrow version: 5.0.0
It seems that next release of `sacrebleu` (v2.0.0) will break our `datasets` implementation to compute it. See my Google Colab: https://colab.research.google.com/drive/1SKmvvjQi6k_3OHsX5NPkZdiaJIfXyv9X?usp=sharing
I'm reopening this Issue and making a Pull Request to fix it. | [
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https://github.com/huggingface/datasets/issues/2736 | Add Microsoft Building Footprints dataset | Motivation: this can be a useful dataset for researchers working on climate change adaptation, urban studies, geography, etc. I'll see if I can figure out how to add it! | ## Adding a Dataset
- **Name:** Microsoft Building Footprints
- **Description:** With the goal to increase the coverage of building footprint data available as open data for OpenStreetMap and humanitarian efforts, we have released millions of building footprints as open data available to download free of charge.
- **Paper:** *link to the dataset paper if available*
- **Data:** https://www.microsoft.com/en-us/maps/building-footprints
- **Motivation:** this can be a useful dataset for researchers working on climate change adaptation, urban studies, geography, etc.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Reported by: @sashavor | 29 | Add Microsoft Building Footprints dataset
## Adding a Dataset
- **Name:** Microsoft Building Footprints
- **Description:** With the goal to increase the coverage of building footprint data available as open data for OpenStreetMap and humanitarian efforts, we have released millions of building footprints as open data available to download free of charge.
- **Paper:** *link to the dataset paper if available*
- **Data:** https://www.microsoft.com/en-us/maps/building-footprints
- **Motivation:** this can be a useful dataset for researchers working on climate change adaptation, urban studies, geography, etc.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Reported by: @sashavor
Motivation: this can be a useful dataset for researchers working on climate change adaptation, urban studies, geography, etc. I'll see if I can figure out how to add it! | [
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https://github.com/huggingface/datasets/issues/2730 | Update CommonVoice with new release | Does anybody know if there is a bundled link, which would allow direct data download instead of manual?
Something similar to: `https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/ab.tar.gz` ? cc @patil-suraj
| ## Adding a Dataset
- **Name:** CommonVoice mid-2021 release
- **Description:** more data in CommonVoice: Languages that have increased the most by percentage are Thai (almost 20x growth, from 12 hours to 250 hours), Luganda (almost 9x growth, from 8 to 80), Esperanto (7x growth, from 100 to 840), and Tamil (almost 8x, from 24 to 220).
- **Paper:** https://discourse.mozilla.org/t/common-voice-2021-mid-year-dataset-release/83812
- **Data:** https://commonvoice.mozilla.org/en/datasets
- **Motivation:** More data and more varied. I think we just need to add configs in the existing dataset script.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
| 25 | Update CommonVoice with new release
## Adding a Dataset
- **Name:** CommonVoice mid-2021 release
- **Description:** more data in CommonVoice: Languages that have increased the most by percentage are Thai (almost 20x growth, from 12 hours to 250 hours), Luganda (almost 9x growth, from 8 to 80), Esperanto (7x growth, from 100 to 840), and Tamil (almost 8x, from 24 to 220).
- **Paper:** https://discourse.mozilla.org/t/common-voice-2021-mid-year-dataset-release/83812
- **Data:** https://commonvoice.mozilla.org/en/datasets
- **Motivation:** More data and more varied. I think we just need to add configs in the existing dataset script.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
Does anybody know if there is a bundled link, which would allow direct data download instead of manual?
Something similar to: `https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/ab.tar.gz` ? cc @patil-suraj
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https://github.com/huggingface/datasets/issues/2728 | Concurrent use of same dataset (already downloaded) | Launching simultaneous job relying on the same datasets try some writing issue. I guess it is unexpected since I only need to load some already downloaded file. | ## Describe the bug
When launching several jobs at the same time loading the same dataset trigger some errors see (last comments).
## Steps to reproduce the bug
export HF_DATASETS_CACHE=/gpfswork/rech/toto/datasets
for MODEL in "bert-base-uncased" "roberta-base" "distilbert-base-cased"; do # "bert-base-uncased" "bert-large-cased" "roberta-large" "albert-base-v1" "albert-large-v1"; do
for TASK_NAME in "mrpc" "rte" 'imdb' "paws" "mnli"; do
export OUTPUT_DIR=${MODEL}_${TASK_NAME}
sbatch --job-name=${OUTPUT_DIR} \
--gres=gpu:1 \
--no-requeue \
--cpus-per-task=10 \
--hint=nomultithread \
--time=1:00:00 \
--output=jobinfo/${OUTPUT_DIR}_%j.out \
--error=jobinfo/${OUTPUT_DIR}_%j.err \
--qos=qos_gpu-t4 \
--wrap="module purge; module load pytorch-gpu/py3/1.7.0 ; export HF_DATASETS_OFFLINE=1; export HF_DATASETS_CACHE=/gpfswork/rech/toto/datasets; python compute_measures.py --seed=$SEED --saving_path=results --batch_size=$BATCH_SIZE --task_name=$TASK_NAME --model_name=/gpfswork/rech/toto/transformers_models/$MODEL"
done
done
```python
# Sample code to reproduce the bug
dataset_train = load_dataset('imdb', split='train', download_mode="reuse_cache_if_exists")
dataset_train = dataset_train.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True).select(list(range(args.filter)))
dataset_val = load_dataset('imdb', split='train', download_mode="reuse_cache_if_exists")
dataset_val = dataset_val.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True).select(list(range(args.filter, args.filter + 5000)))
dataset_test = load_dataset('imdb', split='test', download_mode="reuse_cache_if_exists")
dataset_test = dataset_test.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True)
```
## Expected results
I believe I am doing something wrong with the objects.
## Actual results
Traceback (most recent call last):
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 652, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 983, in _prepare_split
check_duplicates=True,
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/arrow_writer.py", line 192, in __init__
self.stream = pa.OSFile(self._path, "wb")
File "pyarrow/io.pxi", line 829, in pyarrow.lib.OSFile.__cinit__
File "pyarrow/io.pxi", line 844, in pyarrow.lib.OSFile._open_writable
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 97, in pyarrow.lib.check_status
FileNotFoundError: [Errno 2] Failed to open local file '/gpfswork/rech/tts/unm25jp/datasets/paws/labeled_final/1.1.0/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete/paws-test.arrow'. Detail: [errno 2] No such file or directory
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "compute_measures.py", line 181, in <module>
train_loader, val_loader, test_loader = get_dataloader(args)
File "/gpfsdswork/projects/rech/toto/intRAOcular/dataset_utils.py", line 69, in get_dataloader
dataset_train = load_dataset('paws', "labeled_final", split='train', download_mode="reuse_cache_if_exists")
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/load.py", line 748, in load_dataset
use_auth_token=use_auth_token,
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 575, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 658, in _download_and_prepare
+ str(e)
OSError: Cannot find data file.
Original error:
[Errno 2] Failed to open local file '/gpfswork/rech/toto/datasets/paws/labeled_final/1.1.0/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete/paws-test.arrow'. Detail: [errno 2] No such file or directory
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets==1.8.0
- Platform: linux (jeanzay)
- Python version: pyarrow==2.0.0
- PyArrow version: 3.7.8
| 27 | Concurrent use of same dataset (already downloaded)
## Describe the bug
When launching several jobs at the same time loading the same dataset trigger some errors see (last comments).
## Steps to reproduce the bug
export HF_DATASETS_CACHE=/gpfswork/rech/toto/datasets
for MODEL in "bert-base-uncased" "roberta-base" "distilbert-base-cased"; do # "bert-base-uncased" "bert-large-cased" "roberta-large" "albert-base-v1" "albert-large-v1"; do
for TASK_NAME in "mrpc" "rte" 'imdb' "paws" "mnli"; do
export OUTPUT_DIR=${MODEL}_${TASK_NAME}
sbatch --job-name=${OUTPUT_DIR} \
--gres=gpu:1 \
--no-requeue \
--cpus-per-task=10 \
--hint=nomultithread \
--time=1:00:00 \
--output=jobinfo/${OUTPUT_DIR}_%j.out \
--error=jobinfo/${OUTPUT_DIR}_%j.err \
--qos=qos_gpu-t4 \
--wrap="module purge; module load pytorch-gpu/py3/1.7.0 ; export HF_DATASETS_OFFLINE=1; export HF_DATASETS_CACHE=/gpfswork/rech/toto/datasets; python compute_measures.py --seed=$SEED --saving_path=results --batch_size=$BATCH_SIZE --task_name=$TASK_NAME --model_name=/gpfswork/rech/toto/transformers_models/$MODEL"
done
done
```python
# Sample code to reproduce the bug
dataset_train = load_dataset('imdb', split='train', download_mode="reuse_cache_if_exists")
dataset_train = dataset_train.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True).select(list(range(args.filter)))
dataset_val = load_dataset('imdb', split='train', download_mode="reuse_cache_if_exists")
dataset_val = dataset_val.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True).select(list(range(args.filter, args.filter + 5000)))
dataset_test = load_dataset('imdb', split='test', download_mode="reuse_cache_if_exists")
dataset_test = dataset_test.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True)
```
## Expected results
I believe I am doing something wrong with the objects.
## Actual results
Traceback (most recent call last):
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 652, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 983, in _prepare_split
check_duplicates=True,
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/arrow_writer.py", line 192, in __init__
self.stream = pa.OSFile(self._path, "wb")
File "pyarrow/io.pxi", line 829, in pyarrow.lib.OSFile.__cinit__
File "pyarrow/io.pxi", line 844, in pyarrow.lib.OSFile._open_writable
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 97, in pyarrow.lib.check_status
FileNotFoundError: [Errno 2] Failed to open local file '/gpfswork/rech/tts/unm25jp/datasets/paws/labeled_final/1.1.0/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete/paws-test.arrow'. Detail: [errno 2] No such file or directory
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "compute_measures.py", line 181, in <module>
train_loader, val_loader, test_loader = get_dataloader(args)
File "/gpfsdswork/projects/rech/toto/intRAOcular/dataset_utils.py", line 69, in get_dataloader
dataset_train = load_dataset('paws', "labeled_final", split='train', download_mode="reuse_cache_if_exists")
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/load.py", line 748, in load_dataset
use_auth_token=use_auth_token,
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 575, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 658, in _download_and_prepare
+ str(e)
OSError: Cannot find data file.
Original error:
[Errno 2] Failed to open local file '/gpfswork/rech/toto/datasets/paws/labeled_final/1.1.0/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete/paws-test.arrow'. Detail: [errno 2] No such file or directory
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets==1.8.0
- Platform: linux (jeanzay)
- Python version: pyarrow==2.0.0
- PyArrow version: 3.7.8
Launching simultaneous job relying on the same datasets try some writing issue. I guess it is unexpected since I only need to load some already downloaded file. | [
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https://github.com/huggingface/datasets/issues/2728 | Concurrent use of same dataset (already downloaded) | If i have two jobs that use the same dataset. I got :
File "compute_measures.py", line 181, in <module>
train_loader, val_loader, test_loader = get_dataloader(args)
File "/gpfsdswork/projects/rech/toto/intRAOcular/dataset_utils.py", line 69, in get_dataloader
dataset_train = load_dataset('paws', "labeled_final", split='train', download_mode="reuse_cache_if_exists")
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/load.py", line 748, in load_dataset
use_auth_token=use_auth_token,
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 582, in download_and_prepare
self._save_info()
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 690, in _save_info
self.info.write_to_directory(self._cache_dir)
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/info.py", line 195, in write_to_directory
with open(os.path.join(dataset_info_dir, config.LICENSE_FILENAME), "wb") as f:
FileNotFoundError: [Errno 2] No such file or directory: '/gpfswork/rech/toto/datasets/paws/labeled_final/1.1.0/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete/LICENSE' | ## Describe the bug
When launching several jobs at the same time loading the same dataset trigger some errors see (last comments).
## Steps to reproduce the bug
export HF_DATASETS_CACHE=/gpfswork/rech/toto/datasets
for MODEL in "bert-base-uncased" "roberta-base" "distilbert-base-cased"; do # "bert-base-uncased" "bert-large-cased" "roberta-large" "albert-base-v1" "albert-large-v1"; do
for TASK_NAME in "mrpc" "rte" 'imdb' "paws" "mnli"; do
export OUTPUT_DIR=${MODEL}_${TASK_NAME}
sbatch --job-name=${OUTPUT_DIR} \
--gres=gpu:1 \
--no-requeue \
--cpus-per-task=10 \
--hint=nomultithread \
--time=1:00:00 \
--output=jobinfo/${OUTPUT_DIR}_%j.out \
--error=jobinfo/${OUTPUT_DIR}_%j.err \
--qos=qos_gpu-t4 \
--wrap="module purge; module load pytorch-gpu/py3/1.7.0 ; export HF_DATASETS_OFFLINE=1; export HF_DATASETS_CACHE=/gpfswork/rech/toto/datasets; python compute_measures.py --seed=$SEED --saving_path=results --batch_size=$BATCH_SIZE --task_name=$TASK_NAME --model_name=/gpfswork/rech/toto/transformers_models/$MODEL"
done
done
```python
# Sample code to reproduce the bug
dataset_train = load_dataset('imdb', split='train', download_mode="reuse_cache_if_exists")
dataset_train = dataset_train.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True).select(list(range(args.filter)))
dataset_val = load_dataset('imdb', split='train', download_mode="reuse_cache_if_exists")
dataset_val = dataset_val.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True).select(list(range(args.filter, args.filter + 5000)))
dataset_test = load_dataset('imdb', split='test', download_mode="reuse_cache_if_exists")
dataset_test = dataset_test.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True)
```
## Expected results
I believe I am doing something wrong with the objects.
## Actual results
Traceback (most recent call last):
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 652, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 983, in _prepare_split
check_duplicates=True,
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/arrow_writer.py", line 192, in __init__
self.stream = pa.OSFile(self._path, "wb")
File "pyarrow/io.pxi", line 829, in pyarrow.lib.OSFile.__cinit__
File "pyarrow/io.pxi", line 844, in pyarrow.lib.OSFile._open_writable
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 97, in pyarrow.lib.check_status
FileNotFoundError: [Errno 2] Failed to open local file '/gpfswork/rech/tts/unm25jp/datasets/paws/labeled_final/1.1.0/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete/paws-test.arrow'. Detail: [errno 2] No such file or directory
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "compute_measures.py", line 181, in <module>
train_loader, val_loader, test_loader = get_dataloader(args)
File "/gpfsdswork/projects/rech/toto/intRAOcular/dataset_utils.py", line 69, in get_dataloader
dataset_train = load_dataset('paws', "labeled_final", split='train', download_mode="reuse_cache_if_exists")
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/load.py", line 748, in load_dataset
use_auth_token=use_auth_token,
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 575, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 658, in _download_and_prepare
+ str(e)
OSError: Cannot find data file.
Original error:
[Errno 2] Failed to open local file '/gpfswork/rech/toto/datasets/paws/labeled_final/1.1.0/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete/paws-test.arrow'. Detail: [errno 2] No such file or directory
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets==1.8.0
- Platform: linux (jeanzay)
- Python version: pyarrow==2.0.0
- PyArrow version: 3.7.8
| 78 | Concurrent use of same dataset (already downloaded)
## Describe the bug
When launching several jobs at the same time loading the same dataset trigger some errors see (last comments).
## Steps to reproduce the bug
export HF_DATASETS_CACHE=/gpfswork/rech/toto/datasets
for MODEL in "bert-base-uncased" "roberta-base" "distilbert-base-cased"; do # "bert-base-uncased" "bert-large-cased" "roberta-large" "albert-base-v1" "albert-large-v1"; do
for TASK_NAME in "mrpc" "rte" 'imdb' "paws" "mnli"; do
export OUTPUT_DIR=${MODEL}_${TASK_NAME}
sbatch --job-name=${OUTPUT_DIR} \
--gres=gpu:1 \
--no-requeue \
--cpus-per-task=10 \
--hint=nomultithread \
--time=1:00:00 \
--output=jobinfo/${OUTPUT_DIR}_%j.out \
--error=jobinfo/${OUTPUT_DIR}_%j.err \
--qos=qos_gpu-t4 \
--wrap="module purge; module load pytorch-gpu/py3/1.7.0 ; export HF_DATASETS_OFFLINE=1; export HF_DATASETS_CACHE=/gpfswork/rech/toto/datasets; python compute_measures.py --seed=$SEED --saving_path=results --batch_size=$BATCH_SIZE --task_name=$TASK_NAME --model_name=/gpfswork/rech/toto/transformers_models/$MODEL"
done
done
```python
# Sample code to reproduce the bug
dataset_train = load_dataset('imdb', split='train', download_mode="reuse_cache_if_exists")
dataset_train = dataset_train.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True).select(list(range(args.filter)))
dataset_val = load_dataset('imdb', split='train', download_mode="reuse_cache_if_exists")
dataset_val = dataset_val.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True).select(list(range(args.filter, args.filter + 5000)))
dataset_test = load_dataset('imdb', split='test', download_mode="reuse_cache_if_exists")
dataset_test = dataset_test.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True)
```
## Expected results
I believe I am doing something wrong with the objects.
## Actual results
Traceback (most recent call last):
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 652, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 983, in _prepare_split
check_duplicates=True,
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/arrow_writer.py", line 192, in __init__
self.stream = pa.OSFile(self._path, "wb")
File "pyarrow/io.pxi", line 829, in pyarrow.lib.OSFile.__cinit__
File "pyarrow/io.pxi", line 844, in pyarrow.lib.OSFile._open_writable
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 97, in pyarrow.lib.check_status
FileNotFoundError: [Errno 2] Failed to open local file '/gpfswork/rech/tts/unm25jp/datasets/paws/labeled_final/1.1.0/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete/paws-test.arrow'. Detail: [errno 2] No such file or directory
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "compute_measures.py", line 181, in <module>
train_loader, val_loader, test_loader = get_dataloader(args)
File "/gpfsdswork/projects/rech/toto/intRAOcular/dataset_utils.py", line 69, in get_dataloader
dataset_train = load_dataset('paws', "labeled_final", split='train', download_mode="reuse_cache_if_exists")
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/load.py", line 748, in load_dataset
use_auth_token=use_auth_token,
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 575, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 658, in _download_and_prepare
+ str(e)
OSError: Cannot find data file.
Original error:
[Errno 2] Failed to open local file '/gpfswork/rech/toto/datasets/paws/labeled_final/1.1.0/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete/paws-test.arrow'. Detail: [errno 2] No such file or directory
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets==1.8.0
- Platform: linux (jeanzay)
- Python version: pyarrow==2.0.0
- PyArrow version: 3.7.8
If i have two jobs that use the same dataset. I got :
File "compute_measures.py", line 181, in <module>
train_loader, val_loader, test_loader = get_dataloader(args)
File "/gpfsdswork/projects/rech/toto/intRAOcular/dataset_utils.py", line 69, in get_dataloader
dataset_train = load_dataset('paws', "labeled_final", split='train', download_mode="reuse_cache_if_exists")
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/load.py", line 748, in load_dataset
use_auth_token=use_auth_token,
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 582, in download_and_prepare
self._save_info()
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 690, in _save_info
self.info.write_to_directory(self._cache_dir)
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/info.py", line 195, in write_to_directory
with open(os.path.join(dataset_info_dir, config.LICENSE_FILENAME), "wb") as f:
FileNotFoundError: [Errno 2] No such file or directory: '/gpfswork/rech/toto/datasets/paws/labeled_final/1.1.0/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete/LICENSE' | [
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https://github.com/huggingface/datasets/issues/2728 | Concurrent use of same dataset (already downloaded) | You can probably have a solution much faster than me (first time I use the library). But I suspect some write function are used when loading the dataset from cache. | ## Describe the bug
When launching several jobs at the same time loading the same dataset trigger some errors see (last comments).
## Steps to reproduce the bug
export HF_DATASETS_CACHE=/gpfswork/rech/toto/datasets
for MODEL in "bert-base-uncased" "roberta-base" "distilbert-base-cased"; do # "bert-base-uncased" "bert-large-cased" "roberta-large" "albert-base-v1" "albert-large-v1"; do
for TASK_NAME in "mrpc" "rte" 'imdb' "paws" "mnli"; do
export OUTPUT_DIR=${MODEL}_${TASK_NAME}
sbatch --job-name=${OUTPUT_DIR} \
--gres=gpu:1 \
--no-requeue \
--cpus-per-task=10 \
--hint=nomultithread \
--time=1:00:00 \
--output=jobinfo/${OUTPUT_DIR}_%j.out \
--error=jobinfo/${OUTPUT_DIR}_%j.err \
--qos=qos_gpu-t4 \
--wrap="module purge; module load pytorch-gpu/py3/1.7.0 ; export HF_DATASETS_OFFLINE=1; export HF_DATASETS_CACHE=/gpfswork/rech/toto/datasets; python compute_measures.py --seed=$SEED --saving_path=results --batch_size=$BATCH_SIZE --task_name=$TASK_NAME --model_name=/gpfswork/rech/toto/transformers_models/$MODEL"
done
done
```python
# Sample code to reproduce the bug
dataset_train = load_dataset('imdb', split='train', download_mode="reuse_cache_if_exists")
dataset_train = dataset_train.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True).select(list(range(args.filter)))
dataset_val = load_dataset('imdb', split='train', download_mode="reuse_cache_if_exists")
dataset_val = dataset_val.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True).select(list(range(args.filter, args.filter + 5000)))
dataset_test = load_dataset('imdb', split='test', download_mode="reuse_cache_if_exists")
dataset_test = dataset_test.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True)
```
## Expected results
I believe I am doing something wrong with the objects.
## Actual results
Traceback (most recent call last):
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 652, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 983, in _prepare_split
check_duplicates=True,
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/arrow_writer.py", line 192, in __init__
self.stream = pa.OSFile(self._path, "wb")
File "pyarrow/io.pxi", line 829, in pyarrow.lib.OSFile.__cinit__
File "pyarrow/io.pxi", line 844, in pyarrow.lib.OSFile._open_writable
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 97, in pyarrow.lib.check_status
FileNotFoundError: [Errno 2] Failed to open local file '/gpfswork/rech/tts/unm25jp/datasets/paws/labeled_final/1.1.0/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete/paws-test.arrow'. Detail: [errno 2] No such file or directory
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "compute_measures.py", line 181, in <module>
train_loader, val_loader, test_loader = get_dataloader(args)
File "/gpfsdswork/projects/rech/toto/intRAOcular/dataset_utils.py", line 69, in get_dataloader
dataset_train = load_dataset('paws', "labeled_final", split='train', download_mode="reuse_cache_if_exists")
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/load.py", line 748, in load_dataset
use_auth_token=use_auth_token,
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 575, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 658, in _download_and_prepare
+ str(e)
OSError: Cannot find data file.
Original error:
[Errno 2] Failed to open local file '/gpfswork/rech/toto/datasets/paws/labeled_final/1.1.0/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete/paws-test.arrow'. Detail: [errno 2] No such file or directory
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets==1.8.0
- Platform: linux (jeanzay)
- Python version: pyarrow==2.0.0
- PyArrow version: 3.7.8
| 30 | Concurrent use of same dataset (already downloaded)
## Describe the bug
When launching several jobs at the same time loading the same dataset trigger some errors see (last comments).
## Steps to reproduce the bug
export HF_DATASETS_CACHE=/gpfswork/rech/toto/datasets
for MODEL in "bert-base-uncased" "roberta-base" "distilbert-base-cased"; do # "bert-base-uncased" "bert-large-cased" "roberta-large" "albert-base-v1" "albert-large-v1"; do
for TASK_NAME in "mrpc" "rte" 'imdb' "paws" "mnli"; do
export OUTPUT_DIR=${MODEL}_${TASK_NAME}
sbatch --job-name=${OUTPUT_DIR} \
--gres=gpu:1 \
--no-requeue \
--cpus-per-task=10 \
--hint=nomultithread \
--time=1:00:00 \
--output=jobinfo/${OUTPUT_DIR}_%j.out \
--error=jobinfo/${OUTPUT_DIR}_%j.err \
--qos=qos_gpu-t4 \
--wrap="module purge; module load pytorch-gpu/py3/1.7.0 ; export HF_DATASETS_OFFLINE=1; export HF_DATASETS_CACHE=/gpfswork/rech/toto/datasets; python compute_measures.py --seed=$SEED --saving_path=results --batch_size=$BATCH_SIZE --task_name=$TASK_NAME --model_name=/gpfswork/rech/toto/transformers_models/$MODEL"
done
done
```python
# Sample code to reproduce the bug
dataset_train = load_dataset('imdb', split='train', download_mode="reuse_cache_if_exists")
dataset_train = dataset_train.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True).select(list(range(args.filter)))
dataset_val = load_dataset('imdb', split='train', download_mode="reuse_cache_if_exists")
dataset_val = dataset_val.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True).select(list(range(args.filter, args.filter + 5000)))
dataset_test = load_dataset('imdb', split='test', download_mode="reuse_cache_if_exists")
dataset_test = dataset_test.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True)
```
## Expected results
I believe I am doing something wrong with the objects.
## Actual results
Traceback (most recent call last):
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 652, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 983, in _prepare_split
check_duplicates=True,
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/arrow_writer.py", line 192, in __init__
self.stream = pa.OSFile(self._path, "wb")
File "pyarrow/io.pxi", line 829, in pyarrow.lib.OSFile.__cinit__
File "pyarrow/io.pxi", line 844, in pyarrow.lib.OSFile._open_writable
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 97, in pyarrow.lib.check_status
FileNotFoundError: [Errno 2] Failed to open local file '/gpfswork/rech/tts/unm25jp/datasets/paws/labeled_final/1.1.0/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete/paws-test.arrow'. Detail: [errno 2] No such file or directory
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "compute_measures.py", line 181, in <module>
train_loader, val_loader, test_loader = get_dataloader(args)
File "/gpfsdswork/projects/rech/toto/intRAOcular/dataset_utils.py", line 69, in get_dataloader
dataset_train = load_dataset('paws', "labeled_final", split='train', download_mode="reuse_cache_if_exists")
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/load.py", line 748, in load_dataset
use_auth_token=use_auth_token,
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 575, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 658, in _download_and_prepare
+ str(e)
OSError: Cannot find data file.
Original error:
[Errno 2] Failed to open local file '/gpfswork/rech/toto/datasets/paws/labeled_final/1.1.0/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete/paws-test.arrow'. Detail: [errno 2] No such file or directory
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets==1.8.0
- Platform: linux (jeanzay)
- Python version: pyarrow==2.0.0
- PyArrow version: 3.7.8
You can probably have a solution much faster than me (first time I use the library). But I suspect some write function are used when loading the dataset from cache. | [
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] |
https://github.com/huggingface/datasets/issues/2728 | Concurrent use of same dataset (already downloaded) | I have the same issue:
```
Traceback (most recent call last):
File "/dccstor/tslm/envs/anaconda3/envs/trf-a100/lib/python3.9/site-packages/datasets/builder.py", line 652, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/dccstor/tslm/envs/anaconda3/envs/trf-a100/lib/python3.9/site-packages/datasets/builder.py", line 1040, in _prepare_split
with ArrowWriter(features=self.info.features, path=fpath) as writer:
File "/dccstor/tslm/envs/anaconda3/envs/trf-a100/lib/python3.9/site-packages/datasets/arrow_writer.py", line 192, in __init__
self.stream = pa.OSFile(self._path, "wb")
File "pyarrow/io.pxi", line 829, in pyarrow.lib.OSFile.__cinit__
File "pyarrow/io.pxi", line 844, in pyarrow.lib.OSFile._open_writable
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 97, in pyarrow.lib.check_status
FileNotFoundError: [Errno 2] Failed to open local file '/dccstor/tslm-gen/.cache/csv/default-387f1f95c084d4df/0.0.0/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0.incomplete/csv-validation.arrow'. Detail: [errno 2] No such file or directory
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/dccstor/tslm/elron/tslm-gen/train.py", line 510, in <module>
main()
File "/dccstor/tslm/elron/tslm-gen/train.py", line 246, in main
datasets = prepare_dataset(dataset_args, logger)
File "/dccstor/tslm/elron/tslm-gen/data.py", line 157, in prepare_dataset
datasets = load_dataset(extension, data_files=data_files, split=dataset_split, cache_dir=dataset_args.dataset_cache_dir, na_filter=False, download_mode=dataset_args.dataset_generate_mode)
File "/dccstor/tslm/envs/anaconda3/envs/trf-a100/lib/python3.9/site-packages/datasets/load.py", line 742, in load_dataset
builder_instance.download_and_prepare(
File "/dccstor/tslm/envs/anaconda3/envs/trf-a100/lib/python3.9/site-packages/datasets/builder.py", line 574, in download_and_prepare
self._download_and_prepare(
File "/dccstor/tslm/envs/anaconda3/envs/trf-a100/lib/python3.9/site-packages/datasets/builder.py", line 654, in _download_and_prepare
raise OSError(
OSError: Cannot find data file.
Original error:
[Errno 2] Failed to open local file '/dccstor/tslm-gen/.cache/csv/default-387f1f95c084d4df/0.0.0/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0.incomplete/csv-validation.arrow'. Detail: [errno 2] No such file or directory
``` | ## Describe the bug
When launching several jobs at the same time loading the same dataset trigger some errors see (last comments).
## Steps to reproduce the bug
export HF_DATASETS_CACHE=/gpfswork/rech/toto/datasets
for MODEL in "bert-base-uncased" "roberta-base" "distilbert-base-cased"; do # "bert-base-uncased" "bert-large-cased" "roberta-large" "albert-base-v1" "albert-large-v1"; do
for TASK_NAME in "mrpc" "rte" 'imdb' "paws" "mnli"; do
export OUTPUT_DIR=${MODEL}_${TASK_NAME}
sbatch --job-name=${OUTPUT_DIR} \
--gres=gpu:1 \
--no-requeue \
--cpus-per-task=10 \
--hint=nomultithread \
--time=1:00:00 \
--output=jobinfo/${OUTPUT_DIR}_%j.out \
--error=jobinfo/${OUTPUT_DIR}_%j.err \
--qos=qos_gpu-t4 \
--wrap="module purge; module load pytorch-gpu/py3/1.7.0 ; export HF_DATASETS_OFFLINE=1; export HF_DATASETS_CACHE=/gpfswork/rech/toto/datasets; python compute_measures.py --seed=$SEED --saving_path=results --batch_size=$BATCH_SIZE --task_name=$TASK_NAME --model_name=/gpfswork/rech/toto/transformers_models/$MODEL"
done
done
```python
# Sample code to reproduce the bug
dataset_train = load_dataset('imdb', split='train', download_mode="reuse_cache_if_exists")
dataset_train = dataset_train.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True).select(list(range(args.filter)))
dataset_val = load_dataset('imdb', split='train', download_mode="reuse_cache_if_exists")
dataset_val = dataset_val.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True).select(list(range(args.filter, args.filter + 5000)))
dataset_test = load_dataset('imdb', split='test', download_mode="reuse_cache_if_exists")
dataset_test = dataset_test.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True)
```
## Expected results
I believe I am doing something wrong with the objects.
## Actual results
Traceback (most recent call last):
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 652, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 983, in _prepare_split
check_duplicates=True,
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/arrow_writer.py", line 192, in __init__
self.stream = pa.OSFile(self._path, "wb")
File "pyarrow/io.pxi", line 829, in pyarrow.lib.OSFile.__cinit__
File "pyarrow/io.pxi", line 844, in pyarrow.lib.OSFile._open_writable
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 97, in pyarrow.lib.check_status
FileNotFoundError: [Errno 2] Failed to open local file '/gpfswork/rech/tts/unm25jp/datasets/paws/labeled_final/1.1.0/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete/paws-test.arrow'. Detail: [errno 2] No such file or directory
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "compute_measures.py", line 181, in <module>
train_loader, val_loader, test_loader = get_dataloader(args)
File "/gpfsdswork/projects/rech/toto/intRAOcular/dataset_utils.py", line 69, in get_dataloader
dataset_train = load_dataset('paws', "labeled_final", split='train', download_mode="reuse_cache_if_exists")
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/load.py", line 748, in load_dataset
use_auth_token=use_auth_token,
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 575, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 658, in _download_and_prepare
+ str(e)
OSError: Cannot find data file.
Original error:
[Errno 2] Failed to open local file '/gpfswork/rech/toto/datasets/paws/labeled_final/1.1.0/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete/paws-test.arrow'. Detail: [errno 2] No such file or directory
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets==1.8.0
- Platform: linux (jeanzay)
- Python version: pyarrow==2.0.0
- PyArrow version: 3.7.8
| 172 | Concurrent use of same dataset (already downloaded)
## Describe the bug
When launching several jobs at the same time loading the same dataset trigger some errors see (last comments).
## Steps to reproduce the bug
export HF_DATASETS_CACHE=/gpfswork/rech/toto/datasets
for MODEL in "bert-base-uncased" "roberta-base" "distilbert-base-cased"; do # "bert-base-uncased" "bert-large-cased" "roberta-large" "albert-base-v1" "albert-large-v1"; do
for TASK_NAME in "mrpc" "rte" 'imdb' "paws" "mnli"; do
export OUTPUT_DIR=${MODEL}_${TASK_NAME}
sbatch --job-name=${OUTPUT_DIR} \
--gres=gpu:1 \
--no-requeue \
--cpus-per-task=10 \
--hint=nomultithread \
--time=1:00:00 \
--output=jobinfo/${OUTPUT_DIR}_%j.out \
--error=jobinfo/${OUTPUT_DIR}_%j.err \
--qos=qos_gpu-t4 \
--wrap="module purge; module load pytorch-gpu/py3/1.7.0 ; export HF_DATASETS_OFFLINE=1; export HF_DATASETS_CACHE=/gpfswork/rech/toto/datasets; python compute_measures.py --seed=$SEED --saving_path=results --batch_size=$BATCH_SIZE --task_name=$TASK_NAME --model_name=/gpfswork/rech/toto/transformers_models/$MODEL"
done
done
```python
# Sample code to reproduce the bug
dataset_train = load_dataset('imdb', split='train', download_mode="reuse_cache_if_exists")
dataset_train = dataset_train.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True).select(list(range(args.filter)))
dataset_val = load_dataset('imdb', split='train', download_mode="reuse_cache_if_exists")
dataset_val = dataset_val.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True).select(list(range(args.filter, args.filter + 5000)))
dataset_test = load_dataset('imdb', split='test', download_mode="reuse_cache_if_exists")
dataset_test = dataset_test.map(lambda e: tokenizer(e['text'], truncation=True, padding='max_length'),
batched=True)
```
## Expected results
I believe I am doing something wrong with the objects.
## Actual results
Traceback (most recent call last):
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 652, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 983, in _prepare_split
check_duplicates=True,
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/arrow_writer.py", line 192, in __init__
self.stream = pa.OSFile(self._path, "wb")
File "pyarrow/io.pxi", line 829, in pyarrow.lib.OSFile.__cinit__
File "pyarrow/io.pxi", line 844, in pyarrow.lib.OSFile._open_writable
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 97, in pyarrow.lib.check_status
FileNotFoundError: [Errno 2] Failed to open local file '/gpfswork/rech/tts/unm25jp/datasets/paws/labeled_final/1.1.0/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete/paws-test.arrow'. Detail: [errno 2] No such file or directory
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "compute_measures.py", line 181, in <module>
train_loader, val_loader, test_loader = get_dataloader(args)
File "/gpfsdswork/projects/rech/toto/intRAOcular/dataset_utils.py", line 69, in get_dataloader
dataset_train = load_dataset('paws', "labeled_final", split='train', download_mode="reuse_cache_if_exists")
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/load.py", line 748, in load_dataset
use_auth_token=use_auth_token,
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 575, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/pytorch-gpu-1.7.0/lib/python3.7/site-packages/datasets/builder.py", line 658, in _download_and_prepare
+ str(e)
OSError: Cannot find data file.
Original error:
[Errno 2] Failed to open local file '/gpfswork/rech/toto/datasets/paws/labeled_final/1.1.0/09d8fae989bb569009a8f5b879ccf2924d3e5cd55bfe2e89e6dab1c0b50ecd34.incomplete/paws-test.arrow'. Detail: [errno 2] No such file or directory
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: datasets==1.8.0
- Platform: linux (jeanzay)
- Python version: pyarrow==2.0.0
- PyArrow version: 3.7.8
I have the same issue:
```
Traceback (most recent call last):
File "/dccstor/tslm/envs/anaconda3/envs/trf-a100/lib/python3.9/site-packages/datasets/builder.py", line 652, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/dccstor/tslm/envs/anaconda3/envs/trf-a100/lib/python3.9/site-packages/datasets/builder.py", line 1040, in _prepare_split
with ArrowWriter(features=self.info.features, path=fpath) as writer:
File "/dccstor/tslm/envs/anaconda3/envs/trf-a100/lib/python3.9/site-packages/datasets/arrow_writer.py", line 192, in __init__
self.stream = pa.OSFile(self._path, "wb")
File "pyarrow/io.pxi", line 829, in pyarrow.lib.OSFile.__cinit__
File "pyarrow/io.pxi", line 844, in pyarrow.lib.OSFile._open_writable
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 97, in pyarrow.lib.check_status
FileNotFoundError: [Errno 2] Failed to open local file '/dccstor/tslm-gen/.cache/csv/default-387f1f95c084d4df/0.0.0/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0.incomplete/csv-validation.arrow'. Detail: [errno 2] No such file or directory
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/dccstor/tslm/elron/tslm-gen/train.py", line 510, in <module>
main()
File "/dccstor/tslm/elron/tslm-gen/train.py", line 246, in main
datasets = prepare_dataset(dataset_args, logger)
File "/dccstor/tslm/elron/tslm-gen/data.py", line 157, in prepare_dataset
datasets = load_dataset(extension, data_files=data_files, split=dataset_split, cache_dir=dataset_args.dataset_cache_dir, na_filter=False, download_mode=dataset_args.dataset_generate_mode)
File "/dccstor/tslm/envs/anaconda3/envs/trf-a100/lib/python3.9/site-packages/datasets/load.py", line 742, in load_dataset
builder_instance.download_and_prepare(
File "/dccstor/tslm/envs/anaconda3/envs/trf-a100/lib/python3.9/site-packages/datasets/builder.py", line 574, in download_and_prepare
self._download_and_prepare(
File "/dccstor/tslm/envs/anaconda3/envs/trf-a100/lib/python3.9/site-packages/datasets/builder.py", line 654, in _download_and_prepare
raise OSError(
OSError: Cannot find data file.
Original error:
[Errno 2] Failed to open local file '/dccstor/tslm-gen/.cache/csv/default-387f1f95c084d4df/0.0.0/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0.incomplete/csv-validation.arrow'. Detail: [errno 2] No such file or directory
``` | [
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https://github.com/huggingface/datasets/issues/2727 | Error in loading the Arabic Billion Words Corpus | I modified the dataset loading script to catch the `IndexError` and inspect the records at which the error is happening, and I found this:
For the `Techreen` config, the error happens in 36 records when trying to find the `Text` or `Dateline` tags. All these 36 records look something like:
```
<Techreen>
<ID>TRN_ARB_0248167</ID>
<URL>http://tishreen.news.sy/tishreen/public/read/248240</URL>
<Headline>Removed, because the original articles was in English</Headline>
</Techreen>
```
and all the 288 faulty records in the `Almustaqbal` config look like:
```
<Almustaqbal>
<ID>MTL_ARB_0028398</ID>
<URL>http://www.almustaqbal.com/v4/article.aspx?type=NP&ArticleID=179015</URL>
<Headline> Removed because it is not available in the original site</Headline>
</Almustaqbal>
```
so the error is happening because the articles were removed and so the associated records lack the `Text` tag.
In this case, I think we just need to catch the `IndexError` and ignore (pass) it.
| ## Describe the bug
I get `IndexError: list index out of range` when trying to load the `Techreen` and `Almustaqbal` configs of the dataset.
## Steps to reproduce the bug
```python
load_dataset("arabic_billion_words", "Techreen")
load_dataset("arabic_billion_words", "Almustaqbal")
```
## Expected results
The datasets load succefully.
## Actual results
```python
_extract_tags(self, sample, tag)
139 if len(out) > 0:
140 break
--> 141 return out[0]
142
143 def _clean_text(self, text):
IndexError: list index out of range
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.2
- Platform: Ubuntu 18.04.5 LTS
- Python version: 3.7.11
- PyArrow version: 3.0.0
| 128 | Error in loading the Arabic Billion Words Corpus
## Describe the bug
I get `IndexError: list index out of range` when trying to load the `Techreen` and `Almustaqbal` configs of the dataset.
## Steps to reproduce the bug
```python
load_dataset("arabic_billion_words", "Techreen")
load_dataset("arabic_billion_words", "Almustaqbal")
```
## Expected results
The datasets load succefully.
## Actual results
```python
_extract_tags(self, sample, tag)
139 if len(out) > 0:
140 break
--> 141 return out[0]
142
143 def _clean_text(self, text):
IndexError: list index out of range
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.2
- Platform: Ubuntu 18.04.5 LTS
- Python version: 3.7.11
- PyArrow version: 3.0.0
I modified the dataset loading script to catch the `IndexError` and inspect the records at which the error is happening, and I found this:
For the `Techreen` config, the error happens in 36 records when trying to find the `Text` or `Dateline` tags. All these 36 records look something like:
```
<Techreen>
<ID>TRN_ARB_0248167</ID>
<URL>http://tishreen.news.sy/tishreen/public/read/248240</URL>
<Headline>Removed, because the original articles was in English</Headline>
</Techreen>
```
and all the 288 faulty records in the `Almustaqbal` config look like:
```
<Almustaqbal>
<ID>MTL_ARB_0028398</ID>
<URL>http://www.almustaqbal.com/v4/article.aspx?type=NP&ArticleID=179015</URL>
<Headline> Removed because it is not available in the original site</Headline>
</Almustaqbal>
```
so the error is happening because the articles were removed and so the associated records lack the `Text` tag.
In this case, I think we just need to catch the `IndexError` and ignore (pass) it.
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https://github.com/huggingface/datasets/issues/2727 | Error in loading the Arabic Billion Words Corpus | Thanks @M-Salti for reporting this issue and for your investigation.
Indeed, those `IndexError` should be catched and the corresponding record should be ignored.
I'm opening a Pull Request to fix it. | ## Describe the bug
I get `IndexError: list index out of range` when trying to load the `Techreen` and `Almustaqbal` configs of the dataset.
## Steps to reproduce the bug
```python
load_dataset("arabic_billion_words", "Techreen")
load_dataset("arabic_billion_words", "Almustaqbal")
```
## Expected results
The datasets load succefully.
## Actual results
```python
_extract_tags(self, sample, tag)
139 if len(out) > 0:
140 break
--> 141 return out[0]
142
143 def _clean_text(self, text):
IndexError: list index out of range
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.2
- Platform: Ubuntu 18.04.5 LTS
- Python version: 3.7.11
- PyArrow version: 3.0.0
| 31 | Error in loading the Arabic Billion Words Corpus
## Describe the bug
I get `IndexError: list index out of range` when trying to load the `Techreen` and `Almustaqbal` configs of the dataset.
## Steps to reproduce the bug
```python
load_dataset("arabic_billion_words", "Techreen")
load_dataset("arabic_billion_words", "Almustaqbal")
```
## Expected results
The datasets load succefully.
## Actual results
```python
_extract_tags(self, sample, tag)
139 if len(out) > 0:
140 break
--> 141 return out[0]
142
143 def _clean_text(self, text):
IndexError: list index out of range
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.2
- Platform: Ubuntu 18.04.5 LTS
- Python version: 3.7.11
- PyArrow version: 3.0.0
Thanks @M-Salti for reporting this issue and for your investigation.
Indeed, those `IndexError` should be catched and the corresponding record should be ignored.
I'm opening a Pull Request to fix it. | [
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https://github.com/huggingface/datasets/issues/2724 | 404 Error when loading remote data files from private repo | I guess the issue is when computing the ETags of the remote files. Indeed `use_auth_token` must be passed to `request_etags` here:
https://github.com/huggingface/datasets/blob/35b5e4bc0cb2ed896e40f3eb2a4aa3de1cb1a6c5/src/datasets/builder.py#L160-L160 | ## Describe the bug
When loading remote data files from a private repo, a 404 error is raised.
## Steps to reproduce the bug
```python
url = hf_hub_url("lewtun/asr-preds-test", "preds.jsonl", repo_type="dataset")
dset = load_dataset("json", data_files=url, use_auth_token=True)
# HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/datasets/lewtun/asr-preds-test/resolve/main/preds.jsonl
```
## Expected results
Load dataset.
## Actual results
404 Error.
| 22 | 404 Error when loading remote data files from private repo
## Describe the bug
When loading remote data files from a private repo, a 404 error is raised.
## Steps to reproduce the bug
```python
url = hf_hub_url("lewtun/asr-preds-test", "preds.jsonl", repo_type="dataset")
dset = load_dataset("json", data_files=url, use_auth_token=True)
# HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/datasets/lewtun/asr-preds-test/resolve/main/preds.jsonl
```
## Expected results
Load dataset.
## Actual results
404 Error.
I guess the issue is when computing the ETags of the remote files. Indeed `use_auth_token` must be passed to `request_etags` here:
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https://github.com/huggingface/datasets/issues/2724 | 404 Error when loading remote data files from private repo | Yes, I remember having properly implemented that:
- https://github.com/huggingface/datasets/commit/7a9c62f7cef9ecc293f629f859d4375a6bd26dc8#diff-f933ce41f71c6c0d1ce658e27de62cbe0b45d777e9e68056dd012ac3eb9324f7R160
- https://github.com/huggingface/datasets/pull/2628/commits/6350a03b4b830339a745f7b1da46ece784ca734c
But a subsequent refactoring accidentally removed it... | ## Describe the bug
When loading remote data files from a private repo, a 404 error is raised.
## Steps to reproduce the bug
```python
url = hf_hub_url("lewtun/asr-preds-test", "preds.jsonl", repo_type="dataset")
dset = load_dataset("json", data_files=url, use_auth_token=True)
# HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/datasets/lewtun/asr-preds-test/resolve/main/preds.jsonl
```
## Expected results
Load dataset.
## Actual results
404 Error.
| 18 | 404 Error when loading remote data files from private repo
## Describe the bug
When loading remote data files from a private repo, a 404 error is raised.
## Steps to reproduce the bug
```python
url = hf_hub_url("lewtun/asr-preds-test", "preds.jsonl", repo_type="dataset")
dset = load_dataset("json", data_files=url, use_auth_token=True)
# HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/datasets/lewtun/asr-preds-test/resolve/main/preds.jsonl
```
## Expected results
Load dataset.
## Actual results
404 Error.
Yes, I remember having properly implemented that:
- https://github.com/huggingface/datasets/commit/7a9c62f7cef9ecc293f629f859d4375a6bd26dc8#diff-f933ce41f71c6c0d1ce658e27de62cbe0b45d777e9e68056dd012ac3eb9324f7R160
- https://github.com/huggingface/datasets/pull/2628/commits/6350a03b4b830339a745f7b1da46ece784ca734c
But a subsequent refactoring accidentally removed it... | [
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https://github.com/huggingface/datasets/issues/2722 | Missing cache file | This could be solved by going to the glue/ directory and delete sst2 directory, then load the dataset again will help you redownload the dataset. | Strangely missing cache file after I restart my program again.
`glue_dataset = datasets.load_dataset('glue', 'sst2')`
`FileNotFoundError: [Errno 2] No such file or directory: /Users/chris/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96d6053ad/dataset_info.json'`
| 25 | Missing cache file
Strangely missing cache file after I restart my program again.
`glue_dataset = datasets.load_dataset('glue', 'sst2')`
`FileNotFoundError: [Errno 2] No such file or directory: /Users/chris/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96d6053ad/dataset_info.json'`
This could be solved by going to the glue/ directory and delete sst2 directory, then load the dataset again will help you redownload the dataset. | [
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https://github.com/huggingface/datasets/issues/2722 | Missing cache file | Hi ! Not sure why this file was missing, but yes the way to fix this is to delete the sst2 directory and to reload the dataset | Strangely missing cache file after I restart my program again.
`glue_dataset = datasets.load_dataset('glue', 'sst2')`
`FileNotFoundError: [Errno 2] No such file or directory: /Users/chris/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96d6053ad/dataset_info.json'`
| 27 | Missing cache file
Strangely missing cache file after I restart my program again.
`glue_dataset = datasets.load_dataset('glue', 'sst2')`
`FileNotFoundError: [Errno 2] No such file or directory: /Users/chris/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96d6053ad/dataset_info.json'`
Hi ! Not sure why this file was missing, but yes the way to fix this is to delete the sst2 directory and to reload the dataset | [
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https://github.com/huggingface/datasets/issues/2716 | Calling shuffle on IterableDataset will disable batching in case any functions were mapped | Hi :) Good catch ! Feel free to open a PR if you want to contribute, this would be very welcome ;) | When using dataset in streaming mode, if one applies `shuffle` method on the dataset and `map` method for which `batched=True` than the batching operation will not happen, instead `batched` will be set to `False`
I did RCA on the dataset codebase, the problem is emerging from [this line of code](https://github.com/huggingface/datasets/blob/d25a0bf94d9f9a9aa6cabdf5b450b9c327d19729/src/datasets/iterable_dataset.py#L197) here as it is
`self.ex_iterable.shuffle_data_sources(seed), function=self.function, batch_size=self.batch_size`, as one can see it is missing batched argument, which means that the iterator fallsback to default constructor value, which in this case is `False`.
To remedy the problem we can change this line to
`self.ex_iterable.shuffle_data_sources(seed), function=self.function, batched=self.batched, batch_size=self.batch_size`
| 22 | Calling shuffle on IterableDataset will disable batching in case any functions were mapped
When using dataset in streaming mode, if one applies `shuffle` method on the dataset and `map` method for which `batched=True` than the batching operation will not happen, instead `batched` will be set to `False`
I did RCA on the dataset codebase, the problem is emerging from [this line of code](https://github.com/huggingface/datasets/blob/d25a0bf94d9f9a9aa6cabdf5b450b9c327d19729/src/datasets/iterable_dataset.py#L197) here as it is
`self.ex_iterable.shuffle_data_sources(seed), function=self.function, batch_size=self.batch_size`, as one can see it is missing batched argument, which means that the iterator fallsback to default constructor value, which in this case is `False`.
To remedy the problem we can change this line to
`self.ex_iterable.shuffle_data_sources(seed), function=self.function, batched=self.batched, batch_size=self.batch_size`
Hi :) Good catch ! Feel free to open a PR if you want to contribute, this would be very welcome ;) | [
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https://github.com/huggingface/datasets/issues/2714 | add more precise information for size | We already have this information in the dataset_infos.json files of each dataset.
Maybe we can parse these files in the backend to return their content with the endpoint at huggingface.co/api/datasets
For now if you want to access this info you have to load the json for each dataset. For example:
- for a dataset on github like `squad`
- https://raw.githubusercontent.com/huggingface/datasets/master/datasets/squad/dataset_infos.json
- for a community dataset on the hub like `lhoestq/squad`:
https://huggingface.co/datasets/lhoestq/squad/resolve/main/dataset_infos.json | For the import into ELG, we would like a more precise description of the size of the dataset, instead of the current size categories. The size can be expressed in bytes, or any other preferred size unit. As suggested in the slack channel, perhaps this could be computed with a regex for existing datasets. | 71 | add more precise information for size
For the import into ELG, we would like a more precise description of the size of the dataset, instead of the current size categories. The size can be expressed in bytes, or any other preferred size unit. As suggested in the slack channel, perhaps this could be computed with a regex for existing datasets.
We already have this information in the dataset_infos.json files of each dataset.
Maybe we can parse these files in the backend to return their content with the endpoint at huggingface.co/api/datasets
For now if you want to access this info you have to load the json for each dataset. For example:
- for a dataset on github like `squad`
- https://raw.githubusercontent.com/huggingface/datasets/master/datasets/squad/dataset_infos.json
- for a community dataset on the hub like `lhoestq/squad`:
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https://github.com/huggingface/datasets/issues/2709 | Missing documentation for wnut_17 (ner_tags) | Hi @maxpel, thanks for reporting this issue.
Indeed, the documentation in the dataset card is not complete. I’m opening a Pull Request to fix it.
As the paper explains, there are 6 entity types and we have ordered them alphabetically: `corporation`, `creative-work`, `group`, `location`, `person` and `product`.
Each of these entity types has 2 possible IOB2 format tags:
- `B-`: to indicate that the token is the beginning of an entity name, and the
- `I-`: to indicate that the token is inside an entity name.
Additionally, there is the standalone IOB2 tag
- `O`: that indicates that the token belongs to no named entity.
In total there are 13 possible tags, which correspond to the following integer numbers:
0. `O`
1. `B-corporation`
2. `I-corporation`
3. `B-creative-work`
4. `I-creative-work`
5. `B-group`
6. `I-group`
7. `B-location`
8. `I-location`
9. `B-person`
10. `I-person`
11. `B-product`
12. `I-product` | On the info page of the wnut_17 data set (https://huggingface.co/datasets/wnut_17), the model output of ner-tags is only documented for these 5 cases:
`ner_tags: a list of classification labels, with possible values including O (0), B-corporation (1), I-corporation (2), B-creative-work (3), I-creative-work (4).`
I trained a model with the data and it gives me 13 classes:
```
"id2label": {
"0": 0,
"1": 1,
"2": 2,
"3": 3,
"4": 4,
"5": 5,
"6": 6,
"7": 7,
"8": 8,
"9": 9,
"10": 10,
"11": 11,
"12": 12
}
"label2id": {
"0": 0,
"1": 1,
"10": 10,
"11": 11,
"12": 12,
"2": 2,
"3": 3,
"4": 4,
"5": 5,
"6": 6,
"7": 7,
"8": 8,
"9": 9
}
```
The paper (https://www.aclweb.org/anthology/W17-4418.pdf) explains those 6 categories, but the ordering does not match:
```
1. person
2. location (including GPE, facility)
3. corporation
4. product (tangible goods, or well-defined
services)
5. creative-work (song, movie, book and
so on)
6. group (subsuming music band, sports team,
and non-corporate organisations)
```
I would be very helpful for me, if somebody could clarify the model ouputs and explain the "B-" and "I-" prefixes to me.
Really great work with that and the other packages, I couldn't believe that training the model with that data was basically a one-liner! | 145 | Missing documentation for wnut_17 (ner_tags)
On the info page of the wnut_17 data set (https://huggingface.co/datasets/wnut_17), the model output of ner-tags is only documented for these 5 cases:
`ner_tags: a list of classification labels, with possible values including O (0), B-corporation (1), I-corporation (2), B-creative-work (3), I-creative-work (4).`
I trained a model with the data and it gives me 13 classes:
```
"id2label": {
"0": 0,
"1": 1,
"2": 2,
"3": 3,
"4": 4,
"5": 5,
"6": 6,
"7": 7,
"8": 8,
"9": 9,
"10": 10,
"11": 11,
"12": 12
}
"label2id": {
"0": 0,
"1": 1,
"10": 10,
"11": 11,
"12": 12,
"2": 2,
"3": 3,
"4": 4,
"5": 5,
"6": 6,
"7": 7,
"8": 8,
"9": 9
}
```
The paper (https://www.aclweb.org/anthology/W17-4418.pdf) explains those 6 categories, but the ordering does not match:
```
1. person
2. location (including GPE, facility)
3. corporation
4. product (tangible goods, or well-defined
services)
5. creative-work (song, movie, book and
so on)
6. group (subsuming music band, sports team,
and non-corporate organisations)
```
I would be very helpful for me, if somebody could clarify the model ouputs and explain the "B-" and "I-" prefixes to me.
Really great work with that and the other packages, I couldn't believe that training the model with that data was basically a one-liner!
Hi @maxpel, thanks for reporting this issue.
Indeed, the documentation in the dataset card is not complete. I’m opening a Pull Request to fix it.
As the paper explains, there are 6 entity types and we have ordered them alphabetically: `corporation`, `creative-work`, `group`, `location`, `person` and `product`.
Each of these entity types has 2 possible IOB2 format tags:
- `B-`: to indicate that the token is the beginning of an entity name, and the
- `I-`: to indicate that the token is inside an entity name.
Additionally, there is the standalone IOB2 tag
- `O`: that indicates that the token belongs to no named entity.
In total there are 13 possible tags, which correspond to the following integer numbers:
0. `O`
1. `B-corporation`
2. `I-corporation`
3. `B-creative-work`
4. `I-creative-work`
5. `B-group`
6. `I-group`
7. `B-location`
8. `I-location`
9. `B-person`
10. `I-person`
11. `B-product`
12. `I-product` | [
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] |
https://github.com/huggingface/datasets/issues/2708 | QASC: incomplete training set | Hi @danyaljj, thanks for reporting.
Unfortunately, I have not been able to reproduce your problem. My train split has 8134 examples:
```ipython
In [10]: ds["train"]
Out[10]:
Dataset({
features: ['id', 'question', 'choices', 'answerKey', 'fact1', 'fact2', 'combinedfact', 'formatted_question'],
num_rows: 8134
})
In [11]: ds["train"].shape
Out[11]: (8134, 8)
```
and the content of the last 5 examples is:
```ipython
In [12]: for i in range(8129, 8134):
...: print(json.dumps(ds["train"][i]))
...:
{"id": "3KAKFY4PGU1LGXM77JAK2700NGCI3X", "question": "Chitin can be used for protection by whom?", "choices": {"text": ["Fungi", "People", "Man", "Fish", "trees", "Dogs", "animal", "Birds"], "label": ["A", "B",
"C", "D", "E", "F", "G", "H"]}, "answerKey": "D", "fact1": "scales are used for protection by scaled animals", "fact2": "Fish scales are also composed of chitin.", "combinedfact": "Chitin can be used for prote
ction by fish.", "formatted_question": "Chitin can be used for protection by whom? (A) Fungi (B) People (C) Man (D) Fish (E) trees (F) Dogs (G) animal (H) Birds"}
{"id": "336YQZE83VDAQVZ26HW59X51JZ9M5M", "question": "Which type of animal uses plates for protection?", "choices": {"text": ["squids", "reptiles", "sea urchins", "fish", "amphibians", "Frogs", "mammals", "salm
on"], "label": ["A", "B", "C", "D", "E", "F", "G", "H"]}, "answerKey": "B", "fact1": "scales are used for protection by scaled animals", "fact2": "Reptiles have scales or plates.", "combinedfact": "Reptiles use
their plates for protection.", "formatted_question": "Which type of animal uses plates for protection? (A) squids (B) reptiles (C) sea urchins (D) fish (E) amphibians (F) Frogs (G) mammals (H) salmon"}
{"id": "3WZ36BJEV3FGS66VGOOUYX0LN8GTBU", "question": "What are used for protection by fish?", "choices": {"text": ["scales", "fins", "streams.", "coral", "gills", "Collagen", "mussels", "whiskers"], "label": ["
A", "B", "C", "D", "E", "F", "G", "H"]}, "answerKey": "A", "fact1": "scales are used for protection by scaled animals", "fact2": "Fish are backboned aquatic animals.", "combinedfact": "scales are used for prote
ction by fish ", "formatted_question": "What are used for protection by fish? (A) scales (B) fins (C) streams. (D) coral (E) gills (F) Collagen (G) mussels (H) whiskers"}
{"id": "3Z2R0DQ0JHDKFAO2706OYIXGNA4E28", "question": "What are pangolins covered in?", "choices": {"text": ["tunicates", "Echinoids", "shells", "exoskeleton", "blastoids", "barrel-shaped", "protection", "white"
], "label": ["A", "B", "C", "D", "E", "F", "G", "H"]}, "answerKey": "G", "fact1": "scales are used for protection by scaled animals", "fact2": "Pangolins have an elongate and tapering body covered above with ov
erlapping scales.", "combinedfact": "Pangolins are covered in overlapping protection.", "formatted_question": "What are pangolins covered in? (A) tunicates (B) Echinoids (C) shells (D) exoskeleton (E) blastoids
(F) barrel-shaped (G) protection (H) white"}
{"id": "3PMBY0YE272GIWPNWIF8IH5RBHVC9S", "question": "What are covered with protection?", "choices": {"text": ["apples", "trees", "coral", "clams", "roses", "wings", "hats", "fish"], "label": ["A", "B", "C", "D
", "E", "F", "G", "H"]}, "answerKey": "H", "fact1": "scales are used for protection by scaled animals", "fact2": "Fish are covered with scales.", "combinedfact": "Fish are covered with protection", "formatted_q
uestion": "What are covered with protection? (A) apples (B) trees (C) coral (D) clams (E) roses (F) wings (G) hats (H) fish"}
```
Could you please load again your dataset and print its shape, like this:
```python
ds = load_dataset("qasc", split="train)
print(ds.shape)
```
and confirm which is your output? | ## Describe the bug
The training instances are not loaded properly.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("qasc", script_version='1.10.2')
def load_instances(split):
instances = dataset[split]
print(f"split: {split} - size: {len(instances)}")
for x in instances:
print(json.dumps(x))
load_instances('test')
load_instances('validation')
load_instances('train')
```
## results
For test and validation, we can see the examples in the output (which is good!):
```
split: test - size: 920
{"answerKey": "", "choices": {"label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["Anthax", "under water", "uterus", "wombs", "two", "moles", "live", "embryo"]}, "combinedfact": "", "fact1": "", "fact2": "", "formatted_question": "What type of birth do therian mammals have? (A) Anthax (B) under water (C) uterus (D) wombs (E) two (F) moles (G) live (H) embryo", "id": "3C44YUNSI1OBFBB8D36GODNOZN9DPA", "question": "What type of birth do therian mammals have?"}
{"answerKey": "", "choices": {"label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["Corvidae", "arthropods", "birds", "backbones", "keratin", "Jurassic", "front paws", "Parakeets."]}, "combinedfact": "", "fact1": "", "fact2": "", "formatted_question": "By what time had mouse-sized viviparous mammals evolved? (A) Corvidae (B) arthropods (C) birds (D) backbones (E) keratin (F) Jurassic (G) front paws (H) Parakeets.", "id": "3B1NLC6UGZVERVLZFT7OUYQLD1SGPZ", "question": "By what time had mouse-sized viviparous mammals evolved?"}
{"answerKey": "", "choices": {"label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["Reduced friction", "causes infection", "vital to a good life", "prevents water loss", "camouflage from consumers", "Protection against predators", "spur the growth of the plant", "a smooth surface"]}, "combinedfact": "", "fact1": "", "fact2": "", "formatted_question": "What does a plant's skin do? (A) Reduced friction (B) causes infection (C) vital to a good life (D) prevents water loss (E) camouflage from consumers (F) Protection against predators (G) spur the growth of the plant (H) a smooth surface", "id": "3QRYMNZ7FYGITFVSJET3PS0F4S0NT9", "question": "What does a plant's skin do?"}
...
```
However, only a few instances are loaded for the training split, which is not correct.
## Environment info
- `datasets` version: '1.10.2'
- Platform: MaxOS
- Python version:3.7
- PyArrow version: 3.0.0
| 496 | QASC: incomplete training set
## Describe the bug
The training instances are not loaded properly.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("qasc", script_version='1.10.2')
def load_instances(split):
instances = dataset[split]
print(f"split: {split} - size: {len(instances)}")
for x in instances:
print(json.dumps(x))
load_instances('test')
load_instances('validation')
load_instances('train')
```
## results
For test and validation, we can see the examples in the output (which is good!):
```
split: test - size: 920
{"answerKey": "", "choices": {"label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["Anthax", "under water", "uterus", "wombs", "two", "moles", "live", "embryo"]}, "combinedfact": "", "fact1": "", "fact2": "", "formatted_question": "What type of birth do therian mammals have? (A) Anthax (B) under water (C) uterus (D) wombs (E) two (F) moles (G) live (H) embryo", "id": "3C44YUNSI1OBFBB8D36GODNOZN9DPA", "question": "What type of birth do therian mammals have?"}
{"answerKey": "", "choices": {"label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["Corvidae", "arthropods", "birds", "backbones", "keratin", "Jurassic", "front paws", "Parakeets."]}, "combinedfact": "", "fact1": "", "fact2": "", "formatted_question": "By what time had mouse-sized viviparous mammals evolved? (A) Corvidae (B) arthropods (C) birds (D) backbones (E) keratin (F) Jurassic (G) front paws (H) Parakeets.", "id": "3B1NLC6UGZVERVLZFT7OUYQLD1SGPZ", "question": "By what time had mouse-sized viviparous mammals evolved?"}
{"answerKey": "", "choices": {"label": ["A", "B", "C", "D", "E", "F", "G", "H"], "text": ["Reduced friction", "causes infection", "vital to a good life", "prevents water loss", "camouflage from consumers", "Protection against predators", "spur the growth of the plant", "a smooth surface"]}, "combinedfact": "", "fact1": "", "fact2": "", "formatted_question": "What does a plant's skin do? (A) Reduced friction (B) causes infection (C) vital to a good life (D) prevents water loss (E) camouflage from consumers (F) Protection against predators (G) spur the growth of the plant (H) a smooth surface", "id": "3QRYMNZ7FYGITFVSJET3PS0F4S0NT9", "question": "What does a plant's skin do?"}
...
```
However, only a few instances are loaded for the training split, which is not correct.
## Environment info
- `datasets` version: '1.10.2'
- Platform: MaxOS
- Python version:3.7
- PyArrow version: 3.0.0
Hi @danyaljj, thanks for reporting.
Unfortunately, I have not been able to reproduce your problem. My train split has 8134 examples:
```ipython
In [10]: ds["train"]
Out[10]:
Dataset({
features: ['id', 'question', 'choices', 'answerKey', 'fact1', 'fact2', 'combinedfact', 'formatted_question'],
num_rows: 8134
})
In [11]: ds["train"].shape
Out[11]: (8134, 8)
```
and the content of the last 5 examples is:
```ipython
In [12]: for i in range(8129, 8134):
...: print(json.dumps(ds["train"][i]))
...:
{"id": "3KAKFY4PGU1LGXM77JAK2700NGCI3X", "question": "Chitin can be used for protection by whom?", "choices": {"text": ["Fungi", "People", "Man", "Fish", "trees", "Dogs", "animal", "Birds"], "label": ["A", "B",
"C", "D", "E", "F", "G", "H"]}, "answerKey": "D", "fact1": "scales are used for protection by scaled animals", "fact2": "Fish scales are also composed of chitin.", "combinedfact": "Chitin can be used for prote
ction by fish.", "formatted_question": "Chitin can be used for protection by whom? (A) Fungi (B) People (C) Man (D) Fish (E) trees (F) Dogs (G) animal (H) Birds"}
{"id": "336YQZE83VDAQVZ26HW59X51JZ9M5M", "question": "Which type of animal uses plates for protection?", "choices": {"text": ["squids", "reptiles", "sea urchins", "fish", "amphibians", "Frogs", "mammals", "salm
on"], "label": ["A", "B", "C", "D", "E", "F", "G", "H"]}, "answerKey": "B", "fact1": "scales are used for protection by scaled animals", "fact2": "Reptiles have scales or plates.", "combinedfact": "Reptiles use
their plates for protection.", "formatted_question": "Which type of animal uses plates for protection? (A) squids (B) reptiles (C) sea urchins (D) fish (E) amphibians (F) Frogs (G) mammals (H) salmon"}
{"id": "3WZ36BJEV3FGS66VGOOUYX0LN8GTBU", "question": "What are used for protection by fish?", "choices": {"text": ["scales", "fins", "streams.", "coral", "gills", "Collagen", "mussels", "whiskers"], "label": ["
A", "B", "C", "D", "E", "F", "G", "H"]}, "answerKey": "A", "fact1": "scales are used for protection by scaled animals", "fact2": "Fish are backboned aquatic animals.", "combinedfact": "scales are used for prote
ction by fish ", "formatted_question": "What are used for protection by fish? (A) scales (B) fins (C) streams. (D) coral (E) gills (F) Collagen (G) mussels (H) whiskers"}
{"id": "3Z2R0DQ0JHDKFAO2706OYIXGNA4E28", "question": "What are pangolins covered in?", "choices": {"text": ["tunicates", "Echinoids", "shells", "exoskeleton", "blastoids", "barrel-shaped", "protection", "white"
], "label": ["A", "B", "C", "D", "E", "F", "G", "H"]}, "answerKey": "G", "fact1": "scales are used for protection by scaled animals", "fact2": "Pangolins have an elongate and tapering body covered above with ov
erlapping scales.", "combinedfact": "Pangolins are covered in overlapping protection.", "formatted_question": "What are pangolins covered in? (A) tunicates (B) Echinoids (C) shells (D) exoskeleton (E) blastoids
(F) barrel-shaped (G) protection (H) white"}
{"id": "3PMBY0YE272GIWPNWIF8IH5RBHVC9S", "question": "What are covered with protection?", "choices": {"text": ["apples", "trees", "coral", "clams", "roses", "wings", "hats", "fish"], "label": ["A", "B", "C", "D
", "E", "F", "G", "H"]}, "answerKey": "H", "fact1": "scales are used for protection by scaled animals", "fact2": "Fish are covered with scales.", "combinedfact": "Fish are covered with protection", "formatted_q
uestion": "What are covered with protection? (A) apples (B) trees (C) coral (D) clams (E) roses (F) wings (G) hats (H) fish"}
```
Could you please load again your dataset and print its shape, like this:
```python
ds = load_dataset("qasc", split="train)
print(ds.shape)
```
and confirm which is your output? | [
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https://github.com/huggingface/datasets/issues/2707 | 404 Not Found Error when loading LAMA dataset | Hi @dwil2444! I was able to reproduce your error when I downgraded to v1.1.2. Updating to the latest version of Datasets fixed the error for me :) | The [LAMA](https://huggingface.co/datasets/viewer/?dataset=lama) probing dataset is not available for download:
Steps to Reproduce:
1. `from datasets import load_dataset`
2. `dataset = load_dataset('lama', 'trex')`.
Results:
`FileNotFoundError: Couldn't find file locally at lama/lama.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/lama/lama.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/lama/lama.py` | 27 | 404 Not Found Error when loading LAMA dataset
The [LAMA](https://huggingface.co/datasets/viewer/?dataset=lama) probing dataset is not available for download:
Steps to Reproduce:
1. `from datasets import load_dataset`
2. `dataset = load_dataset('lama', 'trex')`.
Results:
`FileNotFoundError: Couldn't find file locally at lama/lama.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/lama/lama.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/lama/lama.py`
Hi @dwil2444! I was able to reproduce your error when I downgraded to v1.1.2. Updating to the latest version of Datasets fixed the error for me :) | [
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https://github.com/huggingface/datasets/issues/2707 | 404 Not Found Error when loading LAMA dataset | Hi @dwil2444, thanks for reporting.
Could you please confirm which `datasets` version you were using and if the problem persists after you update it to the latest version: `pip install -U datasets`?
Thanks @stevhliu for the hint to fix this! ;) | The [LAMA](https://huggingface.co/datasets/viewer/?dataset=lama) probing dataset is not available for download:
Steps to Reproduce:
1. `from datasets import load_dataset`
2. `dataset = load_dataset('lama', 'trex')`.
Results:
`FileNotFoundError: Couldn't find file locally at lama/lama.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/lama/lama.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/lama/lama.py` | 41 | 404 Not Found Error when loading LAMA dataset
The [LAMA](https://huggingface.co/datasets/viewer/?dataset=lama) probing dataset is not available for download:
Steps to Reproduce:
1. `from datasets import load_dataset`
2. `dataset = load_dataset('lama', 'trex')`.
Results:
`FileNotFoundError: Couldn't find file locally at lama/lama.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/lama/lama.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/lama/lama.py`
Hi @dwil2444, thanks for reporting.
Could you please confirm which `datasets` version you were using and if the problem persists after you update it to the latest version: `pip install -U datasets`?
Thanks @stevhliu for the hint to fix this! ;) | [
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] |
https://github.com/huggingface/datasets/issues/2707 | 404 Not Found Error when loading LAMA dataset | @stevhliu @albertvillanova updating to the latest version of datasets did in fact fix this issue. Thanks a lot for your help! | The [LAMA](https://huggingface.co/datasets/viewer/?dataset=lama) probing dataset is not available for download:
Steps to Reproduce:
1. `from datasets import load_dataset`
2. `dataset = load_dataset('lama', 'trex')`.
Results:
`FileNotFoundError: Couldn't find file locally at lama/lama.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/lama/lama.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/lama/lama.py` | 21 | 404 Not Found Error when loading LAMA dataset
The [LAMA](https://huggingface.co/datasets/viewer/?dataset=lama) probing dataset is not available for download:
Steps to Reproduce:
1. `from datasets import load_dataset`
2. `dataset = load_dataset('lama', 'trex')`.
Results:
`FileNotFoundError: Couldn't find file locally at lama/lama.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/lama/lama.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/lama/lama.py`
@stevhliu @albertvillanova updating to the latest version of datasets did in fact fix this issue. Thanks a lot for your help! | [
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https://github.com/huggingface/datasets/issues/2705 | 404 not found error on loading WIKIANN dataset | Hi @ronbutan, thanks for reporting.
You are right: we have recently found that the link to the original PAN-X dataset (also called WikiANN), hosted at Dropbox, is no longer working.
We have opened an issue in the GitHub repository of the original dataset (afshinrahimi/mmner#4) and we have also contacted the author by email to ask if they are planning to fix this issue. See the details here: https://github.com/huggingface/datasets/issues/2691#issuecomment-885463027
I close this issue because it is the same as in #2691. Feel free to subscribe to that other issue to be informed about any updates. | ## Describe the bug
Unable to retreive wikiann English dataset
## Steps to reproduce the bug
```python
from datasets import list_datasets, load_dataset, list_metrics, load_metric
WIKIANN = load_dataset("wikiann","en")
```
## Expected results
Colab notebook should display successful download status
## Actual results
FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/12h3qqog6q4bjve/panx_dataset.tar?dl=1
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.1
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.11
- PyArrow version: 3.0.0
| 94 | 404 not found error on loading WIKIANN dataset
## Describe the bug
Unable to retreive wikiann English dataset
## Steps to reproduce the bug
```python
from datasets import list_datasets, load_dataset, list_metrics, load_metric
WIKIANN = load_dataset("wikiann","en")
```
## Expected results
Colab notebook should display successful download status
## Actual results
FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/12h3qqog6q4bjve/panx_dataset.tar?dl=1
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.1
- Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.11
- PyArrow version: 3.0.0
Hi @ronbutan, thanks for reporting.
You are right: we have recently found that the link to the original PAN-X dataset (also called WikiANN), hosted at Dropbox, is no longer working.
We have opened an issue in the GitHub repository of the original dataset (afshinrahimi/mmner#4) and we have also contacted the author by email to ask if they are planning to fix this issue. See the details here: https://github.com/huggingface/datasets/issues/2691#issuecomment-885463027
I close this issue because it is the same as in #2691. Feel free to subscribe to that other issue to be informed about any updates. | [
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https://github.com/huggingface/datasets/issues/2700 | from datasets import Dataset is failing | Hi @kswamy15, thanks for reporting.
We are fixing this critical issue and making an urgent patch release of the `datasets` library today.
In the meantime, you can circumvent this issue by updating the `tqdm` library: `!pip install -U tqdm` | ## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets import Dataset
```
## Expected results
A clear and concise description of the expected results.
## Actual results
Specify the actual results or traceback.
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in <module>()
25 import posixpath
26 import requests
---> 27 from tqdm.contrib.concurrent import thread_map
28
29 from .. import __version__, config, utils
ModuleNotFoundError: No module named 'tqdm.contrib.concurrent'
---------------------------------------------------------------------------
NOTE: If your import is failing due to a missing package, you can
manually install dependencies using either !pip or !apt.
To view examples of installing some common dependencies, click the
"Open Examples" button below.
---------------------------------------------------------------------------
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: latest version as of 07/21/2021
- Platform: Google Colab
- Python version: 3.7
- PyArrow version:
| 39 | from datasets import Dataset is failing
## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from datasets import Dataset
```
## Expected results
A clear and concise description of the expected results.
## Actual results
Specify the actual results or traceback.
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in <module>()
25 import posixpath
26 import requests
---> 27 from tqdm.contrib.concurrent import thread_map
28
29 from .. import __version__, config, utils
ModuleNotFoundError: No module named 'tqdm.contrib.concurrent'
---------------------------------------------------------------------------
NOTE: If your import is failing due to a missing package, you can
manually install dependencies using either !pip or !apt.
To view examples of installing some common dependencies, click the
"Open Examples" button below.
---------------------------------------------------------------------------
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: latest version as of 07/21/2021
- Platform: Google Colab
- Python version: 3.7
- PyArrow version:
Hi @kswamy15, thanks for reporting.
We are fixing this critical issue and making an urgent patch release of the `datasets` library today.
In the meantime, you can circumvent this issue by updating the `tqdm` library: `!pip install -U tqdm` | [
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] |
https://github.com/huggingface/datasets/issues/2699 | cannot combine splits merging and streaming? | Hi ! That's missing indeed. We'll try to implement this for the next version :)
I guess we just need to implement #2564 first, and then we should be able to add support for splits combinations | this does not work:
`dataset = datasets.load_dataset('mc4','iw',split='train+validation',streaming=True)`
with error:
`ValueError: Bad split: train+validation. Available splits: ['train', 'validation']`
these work:
`dataset = datasets.load_dataset('mc4','iw',split='train+validation')`
`dataset = datasets.load_dataset('mc4','iw',split='train',streaming=True)`
`dataset = datasets.load_dataset('mc4','iw',split='validation',streaming=True)`
i could not find a reference to this in the documentation and the error message is confusing. also would be nice to allow streaming for the merged splits | 36 | cannot combine splits merging and streaming?
this does not work:
`dataset = datasets.load_dataset('mc4','iw',split='train+validation',streaming=True)`
with error:
`ValueError: Bad split: train+validation. Available splits: ['train', 'validation']`
these work:
`dataset = datasets.load_dataset('mc4','iw',split='train+validation')`
`dataset = datasets.load_dataset('mc4','iw',split='train',streaming=True)`
`dataset = datasets.load_dataset('mc4','iw',split='validation',streaming=True)`
i could not find a reference to this in the documentation and the error message is confusing. also would be nice to allow streaming for the merged splits
Hi ! That's missing indeed. We'll try to implement this for the next version :)
I guess we just need to implement #2564 first, and then we should be able to add support for splits combinations | [
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https://github.com/huggingface/datasets/issues/2695 | Cannot import load_dataset on Colab | I'm facing the same issue on Colab today too.
```
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-4-5833ac0f5437> in <module>()
3
4 from ray import tune
----> 5 from datasets import DatasetDict, Dataset
6 from datasets import load_dataset, load_metric
7 from dataclasses import dataclass
7 frames
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in <module>()
25 import posixpath
26 import requests
---> 27 from tqdm.contrib.concurrent import thread_map
28
29 from .. import __version__, config, utils
ModuleNotFoundError: No module named 'tqdm.contrib.concurrent'
---------------------------------------------------------------------------
NOTE: If your import is failing due to a missing package, you can
manually install dependencies using either !pip or !apt.
To view examples of installing some common dependencies, click the
"Open Examples" button below.
---------------------------------------------------------------------------
``` | ## Describe the bug
Got tqdm concurrent module not found error during importing load_dataset from datasets.
## Steps to reproduce the bug
Here [colab notebook](https://colab.research.google.com/drive/1pErWWnVP4P4mVHjSFUtkePd8Na_Qirg4?usp=sharing) to reproduce the error
On colab:
```python
!pip install datasets
from datasets import load_dataset
```
## Expected results
Works without error
## Actual results
Specify the actual results or traceback.
```
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-2-8cc7de4c69eb> in <module>()
----> 1 from datasets import load_dataset, load_metric, Metric, MetricInfo, Features, Value
2 from sklearn.metrics import mean_squared_error
/usr/local/lib/python3.7/dist-packages/datasets/__init__.py in <module>()
31 )
32
---> 33 from .arrow_dataset import Dataset, concatenate_datasets
34 from .arrow_reader import ArrowReader, ReadInstruction
35 from .arrow_writer import ArrowWriter
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in <module>()
40 from tqdm.auto import tqdm
41
---> 42 from datasets.tasks.text_classification import TextClassification
43
44 from . import config, utils
/usr/local/lib/python3.7/dist-packages/datasets/tasks/__init__.py in <module>()
1 from typing import Optional
2
----> 3 from ..utils.logging import get_logger
4 from .automatic_speech_recognition import AutomaticSpeechRecognition
5 from .base import TaskTemplate
/usr/local/lib/python3.7/dist-packages/datasets/utils/__init__.py in <module>()
19
20 from . import logging
---> 21 from .download_manager import DownloadManager, GenerateMode
22 from .file_utils import DownloadConfig, cached_path, hf_bucket_url, is_remote_url, temp_seed
23 from .mock_download_manager import MockDownloadManager
/usr/local/lib/python3.7/dist-packages/datasets/utils/download_manager.py in <module>()
24
25 from .. import config
---> 26 from .file_utils import (
27 DownloadConfig,
28 cached_path,
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in <module>()
25 import posixpath
26 import requests
---> 27 from tqdm.contrib.concurrent import thread_map
28
29 from .. import __version__, config, utils
ModuleNotFoundError: No module named 'tqdm.contrib.concurrent'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.0
- Platform: Colab
- Python version: 3.7.11
- PyArrow version: 3.0.0
| 111 | Cannot import load_dataset on Colab
## Describe the bug
Got tqdm concurrent module not found error during importing load_dataset from datasets.
## Steps to reproduce the bug
Here [colab notebook](https://colab.research.google.com/drive/1pErWWnVP4P4mVHjSFUtkePd8Na_Qirg4?usp=sharing) to reproduce the error
On colab:
```python
!pip install datasets
from datasets import load_dataset
```
## Expected results
Works without error
## Actual results
Specify the actual results or traceback.
```
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-2-8cc7de4c69eb> in <module>()
----> 1 from datasets import load_dataset, load_metric, Metric, MetricInfo, Features, Value
2 from sklearn.metrics import mean_squared_error
/usr/local/lib/python3.7/dist-packages/datasets/__init__.py in <module>()
31 )
32
---> 33 from .arrow_dataset import Dataset, concatenate_datasets
34 from .arrow_reader import ArrowReader, ReadInstruction
35 from .arrow_writer import ArrowWriter
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in <module>()
40 from tqdm.auto import tqdm
41
---> 42 from datasets.tasks.text_classification import TextClassification
43
44 from . import config, utils
/usr/local/lib/python3.7/dist-packages/datasets/tasks/__init__.py in <module>()
1 from typing import Optional
2
----> 3 from ..utils.logging import get_logger
4 from .automatic_speech_recognition import AutomaticSpeechRecognition
5 from .base import TaskTemplate
/usr/local/lib/python3.7/dist-packages/datasets/utils/__init__.py in <module>()
19
20 from . import logging
---> 21 from .download_manager import DownloadManager, GenerateMode
22 from .file_utils import DownloadConfig, cached_path, hf_bucket_url, is_remote_url, temp_seed
23 from .mock_download_manager import MockDownloadManager
/usr/local/lib/python3.7/dist-packages/datasets/utils/download_manager.py in <module>()
24
25 from .. import config
---> 26 from .file_utils import (
27 DownloadConfig,
28 cached_path,
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in <module>()
25 import posixpath
26 import requests
---> 27 from tqdm.contrib.concurrent import thread_map
28
29 from .. import __version__, config, utils
ModuleNotFoundError: No module named 'tqdm.contrib.concurrent'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.0
- Platform: Colab
- Python version: 3.7.11
- PyArrow version: 3.0.0
I'm facing the same issue on Colab today too.
```
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-4-5833ac0f5437> in <module>()
3
4 from ray import tune
----> 5 from datasets import DatasetDict, Dataset
6 from datasets import load_dataset, load_metric
7 from dataclasses import dataclass
7 frames
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in <module>()
25 import posixpath
26 import requests
---> 27 from tqdm.contrib.concurrent import thread_map
28
29 from .. import __version__, config, utils
ModuleNotFoundError: No module named 'tqdm.contrib.concurrent'
---------------------------------------------------------------------------
NOTE: If your import is failing due to a missing package, you can
manually install dependencies using either !pip or !apt.
To view examples of installing some common dependencies, click the
"Open Examples" button below.
---------------------------------------------------------------------------
``` | [
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https://github.com/huggingface/datasets/issues/2695 | Cannot import load_dataset on Colab | @phosseini
I think it is related to [1.10.0](https://github.com/huggingface/datasets/actions/runs/1052653701) release done 3 hours ago. (cc: @lhoestq )
For now I just downgraded to 1.9.0 and it is working fine. | ## Describe the bug
Got tqdm concurrent module not found error during importing load_dataset from datasets.
## Steps to reproduce the bug
Here [colab notebook](https://colab.research.google.com/drive/1pErWWnVP4P4mVHjSFUtkePd8Na_Qirg4?usp=sharing) to reproduce the error
On colab:
```python
!pip install datasets
from datasets import load_dataset
```
## Expected results
Works without error
## Actual results
Specify the actual results or traceback.
```
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-2-8cc7de4c69eb> in <module>()
----> 1 from datasets import load_dataset, load_metric, Metric, MetricInfo, Features, Value
2 from sklearn.metrics import mean_squared_error
/usr/local/lib/python3.7/dist-packages/datasets/__init__.py in <module>()
31 )
32
---> 33 from .arrow_dataset import Dataset, concatenate_datasets
34 from .arrow_reader import ArrowReader, ReadInstruction
35 from .arrow_writer import ArrowWriter
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in <module>()
40 from tqdm.auto import tqdm
41
---> 42 from datasets.tasks.text_classification import TextClassification
43
44 from . import config, utils
/usr/local/lib/python3.7/dist-packages/datasets/tasks/__init__.py in <module>()
1 from typing import Optional
2
----> 3 from ..utils.logging import get_logger
4 from .automatic_speech_recognition import AutomaticSpeechRecognition
5 from .base import TaskTemplate
/usr/local/lib/python3.7/dist-packages/datasets/utils/__init__.py in <module>()
19
20 from . import logging
---> 21 from .download_manager import DownloadManager, GenerateMode
22 from .file_utils import DownloadConfig, cached_path, hf_bucket_url, is_remote_url, temp_seed
23 from .mock_download_manager import MockDownloadManager
/usr/local/lib/python3.7/dist-packages/datasets/utils/download_manager.py in <module>()
24
25 from .. import config
---> 26 from .file_utils import (
27 DownloadConfig,
28 cached_path,
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in <module>()
25 import posixpath
26 import requests
---> 27 from tqdm.contrib.concurrent import thread_map
28
29 from .. import __version__, config, utils
ModuleNotFoundError: No module named 'tqdm.contrib.concurrent'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.0
- Platform: Colab
- Python version: 3.7.11
- PyArrow version: 3.0.0
| 28 | Cannot import load_dataset on Colab
## Describe the bug
Got tqdm concurrent module not found error during importing load_dataset from datasets.
## Steps to reproduce the bug
Here [colab notebook](https://colab.research.google.com/drive/1pErWWnVP4P4mVHjSFUtkePd8Na_Qirg4?usp=sharing) to reproduce the error
On colab:
```python
!pip install datasets
from datasets import load_dataset
```
## Expected results
Works without error
## Actual results
Specify the actual results or traceback.
```
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-2-8cc7de4c69eb> in <module>()
----> 1 from datasets import load_dataset, load_metric, Metric, MetricInfo, Features, Value
2 from sklearn.metrics import mean_squared_error
/usr/local/lib/python3.7/dist-packages/datasets/__init__.py in <module>()
31 )
32
---> 33 from .arrow_dataset import Dataset, concatenate_datasets
34 from .arrow_reader import ArrowReader, ReadInstruction
35 from .arrow_writer import ArrowWriter
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in <module>()
40 from tqdm.auto import tqdm
41
---> 42 from datasets.tasks.text_classification import TextClassification
43
44 from . import config, utils
/usr/local/lib/python3.7/dist-packages/datasets/tasks/__init__.py in <module>()
1 from typing import Optional
2
----> 3 from ..utils.logging import get_logger
4 from .automatic_speech_recognition import AutomaticSpeechRecognition
5 from .base import TaskTemplate
/usr/local/lib/python3.7/dist-packages/datasets/utils/__init__.py in <module>()
19
20 from . import logging
---> 21 from .download_manager import DownloadManager, GenerateMode
22 from .file_utils import DownloadConfig, cached_path, hf_bucket_url, is_remote_url, temp_seed
23 from .mock_download_manager import MockDownloadManager
/usr/local/lib/python3.7/dist-packages/datasets/utils/download_manager.py in <module>()
24
25 from .. import config
---> 26 from .file_utils import (
27 DownloadConfig,
28 cached_path,
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in <module>()
25 import posixpath
26 import requests
---> 27 from tqdm.contrib.concurrent import thread_map
28
29 from .. import __version__, config, utils
ModuleNotFoundError: No module named 'tqdm.contrib.concurrent'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.0
- Platform: Colab
- Python version: 3.7.11
- PyArrow version: 3.0.0
@phosseini
I think it is related to [1.10.0](https://github.com/huggingface/datasets/actions/runs/1052653701) release done 3 hours ago. (cc: @lhoestq )
For now I just downgraded to 1.9.0 and it is working fine. | [
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https://github.com/huggingface/datasets/issues/2695 | Cannot import load_dataset on Colab | > @phosseini
> I think it is related to [1.10.0](https://github.com/huggingface/datasets/actions/runs/1052653701) release done 3 hours ago. (cc: @lhoestq )
> For now I just downgraded to 1.9.0 and it is working fine.
Same here, downgraded to 1.9.0 for now and works fine. | ## Describe the bug
Got tqdm concurrent module not found error during importing load_dataset from datasets.
## Steps to reproduce the bug
Here [colab notebook](https://colab.research.google.com/drive/1pErWWnVP4P4mVHjSFUtkePd8Na_Qirg4?usp=sharing) to reproduce the error
On colab:
```python
!pip install datasets
from datasets import load_dataset
```
## Expected results
Works without error
## Actual results
Specify the actual results or traceback.
```
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-2-8cc7de4c69eb> in <module>()
----> 1 from datasets import load_dataset, load_metric, Metric, MetricInfo, Features, Value
2 from sklearn.metrics import mean_squared_error
/usr/local/lib/python3.7/dist-packages/datasets/__init__.py in <module>()
31 )
32
---> 33 from .arrow_dataset import Dataset, concatenate_datasets
34 from .arrow_reader import ArrowReader, ReadInstruction
35 from .arrow_writer import ArrowWriter
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in <module>()
40 from tqdm.auto import tqdm
41
---> 42 from datasets.tasks.text_classification import TextClassification
43
44 from . import config, utils
/usr/local/lib/python3.7/dist-packages/datasets/tasks/__init__.py in <module>()
1 from typing import Optional
2
----> 3 from ..utils.logging import get_logger
4 from .automatic_speech_recognition import AutomaticSpeechRecognition
5 from .base import TaskTemplate
/usr/local/lib/python3.7/dist-packages/datasets/utils/__init__.py in <module>()
19
20 from . import logging
---> 21 from .download_manager import DownloadManager, GenerateMode
22 from .file_utils import DownloadConfig, cached_path, hf_bucket_url, is_remote_url, temp_seed
23 from .mock_download_manager import MockDownloadManager
/usr/local/lib/python3.7/dist-packages/datasets/utils/download_manager.py in <module>()
24
25 from .. import config
---> 26 from .file_utils import (
27 DownloadConfig,
28 cached_path,
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in <module>()
25 import posixpath
26 import requests
---> 27 from tqdm.contrib.concurrent import thread_map
28
29 from .. import __version__, config, utils
ModuleNotFoundError: No module named 'tqdm.contrib.concurrent'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.0
- Platform: Colab
- Python version: 3.7.11
- PyArrow version: 3.0.0
| 41 | Cannot import load_dataset on Colab
## Describe the bug
Got tqdm concurrent module not found error during importing load_dataset from datasets.
## Steps to reproduce the bug
Here [colab notebook](https://colab.research.google.com/drive/1pErWWnVP4P4mVHjSFUtkePd8Na_Qirg4?usp=sharing) to reproduce the error
On colab:
```python
!pip install datasets
from datasets import load_dataset
```
## Expected results
Works without error
## Actual results
Specify the actual results or traceback.
```
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-2-8cc7de4c69eb> in <module>()
----> 1 from datasets import load_dataset, load_metric, Metric, MetricInfo, Features, Value
2 from sklearn.metrics import mean_squared_error
/usr/local/lib/python3.7/dist-packages/datasets/__init__.py in <module>()
31 )
32
---> 33 from .arrow_dataset import Dataset, concatenate_datasets
34 from .arrow_reader import ArrowReader, ReadInstruction
35 from .arrow_writer import ArrowWriter
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in <module>()
40 from tqdm.auto import tqdm
41
---> 42 from datasets.tasks.text_classification import TextClassification
43
44 from . import config, utils
/usr/local/lib/python3.7/dist-packages/datasets/tasks/__init__.py in <module>()
1 from typing import Optional
2
----> 3 from ..utils.logging import get_logger
4 from .automatic_speech_recognition import AutomaticSpeechRecognition
5 from .base import TaskTemplate
/usr/local/lib/python3.7/dist-packages/datasets/utils/__init__.py in <module>()
19
20 from . import logging
---> 21 from .download_manager import DownloadManager, GenerateMode
22 from .file_utils import DownloadConfig, cached_path, hf_bucket_url, is_remote_url, temp_seed
23 from .mock_download_manager import MockDownloadManager
/usr/local/lib/python3.7/dist-packages/datasets/utils/download_manager.py in <module>()
24
25 from .. import config
---> 26 from .file_utils import (
27 DownloadConfig,
28 cached_path,
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in <module>()
25 import posixpath
26 import requests
---> 27 from tqdm.contrib.concurrent import thread_map
28
29 from .. import __version__, config, utils
ModuleNotFoundError: No module named 'tqdm.contrib.concurrent'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.0
- Platform: Colab
- Python version: 3.7.11
- PyArrow version: 3.0.0
> @phosseini
> I think it is related to [1.10.0](https://github.com/huggingface/datasets/actions/runs/1052653701) release done 3 hours ago. (cc: @lhoestq )
> For now I just downgraded to 1.9.0 and it is working fine.
Same here, downgraded to 1.9.0 for now and works fine. | [
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https://github.com/huggingface/datasets/issues/2695 | Cannot import load_dataset on Colab | Hi,
updating tqdm to the newest version resolves the issue for me. You can do this as follows in Colab:
```
!pip install tqdm --upgrade
``` | ## Describe the bug
Got tqdm concurrent module not found error during importing load_dataset from datasets.
## Steps to reproduce the bug
Here [colab notebook](https://colab.research.google.com/drive/1pErWWnVP4P4mVHjSFUtkePd8Na_Qirg4?usp=sharing) to reproduce the error
On colab:
```python
!pip install datasets
from datasets import load_dataset
```
## Expected results
Works without error
## Actual results
Specify the actual results or traceback.
```
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-2-8cc7de4c69eb> in <module>()
----> 1 from datasets import load_dataset, load_metric, Metric, MetricInfo, Features, Value
2 from sklearn.metrics import mean_squared_error
/usr/local/lib/python3.7/dist-packages/datasets/__init__.py in <module>()
31 )
32
---> 33 from .arrow_dataset import Dataset, concatenate_datasets
34 from .arrow_reader import ArrowReader, ReadInstruction
35 from .arrow_writer import ArrowWriter
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in <module>()
40 from tqdm.auto import tqdm
41
---> 42 from datasets.tasks.text_classification import TextClassification
43
44 from . import config, utils
/usr/local/lib/python3.7/dist-packages/datasets/tasks/__init__.py in <module>()
1 from typing import Optional
2
----> 3 from ..utils.logging import get_logger
4 from .automatic_speech_recognition import AutomaticSpeechRecognition
5 from .base import TaskTemplate
/usr/local/lib/python3.7/dist-packages/datasets/utils/__init__.py in <module>()
19
20 from . import logging
---> 21 from .download_manager import DownloadManager, GenerateMode
22 from .file_utils import DownloadConfig, cached_path, hf_bucket_url, is_remote_url, temp_seed
23 from .mock_download_manager import MockDownloadManager
/usr/local/lib/python3.7/dist-packages/datasets/utils/download_manager.py in <module>()
24
25 from .. import config
---> 26 from .file_utils import (
27 DownloadConfig,
28 cached_path,
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in <module>()
25 import posixpath
26 import requests
---> 27 from tqdm.contrib.concurrent import thread_map
28
29 from .. import __version__, config, utils
ModuleNotFoundError: No module named 'tqdm.contrib.concurrent'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.0
- Platform: Colab
- Python version: 3.7.11
- PyArrow version: 3.0.0
| 26 | Cannot import load_dataset on Colab
## Describe the bug
Got tqdm concurrent module not found error during importing load_dataset from datasets.
## Steps to reproduce the bug
Here [colab notebook](https://colab.research.google.com/drive/1pErWWnVP4P4mVHjSFUtkePd8Na_Qirg4?usp=sharing) to reproduce the error
On colab:
```python
!pip install datasets
from datasets import load_dataset
```
## Expected results
Works without error
## Actual results
Specify the actual results or traceback.
```
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-2-8cc7de4c69eb> in <module>()
----> 1 from datasets import load_dataset, load_metric, Metric, MetricInfo, Features, Value
2 from sklearn.metrics import mean_squared_error
/usr/local/lib/python3.7/dist-packages/datasets/__init__.py in <module>()
31 )
32
---> 33 from .arrow_dataset import Dataset, concatenate_datasets
34 from .arrow_reader import ArrowReader, ReadInstruction
35 from .arrow_writer import ArrowWriter
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in <module>()
40 from tqdm.auto import tqdm
41
---> 42 from datasets.tasks.text_classification import TextClassification
43
44 from . import config, utils
/usr/local/lib/python3.7/dist-packages/datasets/tasks/__init__.py in <module>()
1 from typing import Optional
2
----> 3 from ..utils.logging import get_logger
4 from .automatic_speech_recognition import AutomaticSpeechRecognition
5 from .base import TaskTemplate
/usr/local/lib/python3.7/dist-packages/datasets/utils/__init__.py in <module>()
19
20 from . import logging
---> 21 from .download_manager import DownloadManager, GenerateMode
22 from .file_utils import DownloadConfig, cached_path, hf_bucket_url, is_remote_url, temp_seed
23 from .mock_download_manager import MockDownloadManager
/usr/local/lib/python3.7/dist-packages/datasets/utils/download_manager.py in <module>()
24
25 from .. import config
---> 26 from .file_utils import (
27 DownloadConfig,
28 cached_path,
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in <module>()
25 import posixpath
26 import requests
---> 27 from tqdm.contrib.concurrent import thread_map
28
29 from .. import __version__, config, utils
ModuleNotFoundError: No module named 'tqdm.contrib.concurrent'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.0
- Platform: Colab
- Python version: 3.7.11
- PyArrow version: 3.0.0
Hi,
updating tqdm to the newest version resolves the issue for me. You can do this as follows in Colab:
```
!pip install tqdm --upgrade
``` | [
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https://github.com/huggingface/datasets/issues/2695 | Cannot import load_dataset on Colab | Hi @bayartsogt-ya and @phosseini, thanks for reporting.
We are fixing this critical issue and making an urgent patch release of the `datasets` library today.
In the meantime, as pointed out by @mariosasko, you can circumvent this issue by updating the `tqdm` library:
```
!pip install -U tqdm
``` | ## Describe the bug
Got tqdm concurrent module not found error during importing load_dataset from datasets.
## Steps to reproduce the bug
Here [colab notebook](https://colab.research.google.com/drive/1pErWWnVP4P4mVHjSFUtkePd8Na_Qirg4?usp=sharing) to reproduce the error
On colab:
```python
!pip install datasets
from datasets import load_dataset
```
## Expected results
Works without error
## Actual results
Specify the actual results or traceback.
```
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-2-8cc7de4c69eb> in <module>()
----> 1 from datasets import load_dataset, load_metric, Metric, MetricInfo, Features, Value
2 from sklearn.metrics import mean_squared_error
/usr/local/lib/python3.7/dist-packages/datasets/__init__.py in <module>()
31 )
32
---> 33 from .arrow_dataset import Dataset, concatenate_datasets
34 from .arrow_reader import ArrowReader, ReadInstruction
35 from .arrow_writer import ArrowWriter
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in <module>()
40 from tqdm.auto import tqdm
41
---> 42 from datasets.tasks.text_classification import TextClassification
43
44 from . import config, utils
/usr/local/lib/python3.7/dist-packages/datasets/tasks/__init__.py in <module>()
1 from typing import Optional
2
----> 3 from ..utils.logging import get_logger
4 from .automatic_speech_recognition import AutomaticSpeechRecognition
5 from .base import TaskTemplate
/usr/local/lib/python3.7/dist-packages/datasets/utils/__init__.py in <module>()
19
20 from . import logging
---> 21 from .download_manager import DownloadManager, GenerateMode
22 from .file_utils import DownloadConfig, cached_path, hf_bucket_url, is_remote_url, temp_seed
23 from .mock_download_manager import MockDownloadManager
/usr/local/lib/python3.7/dist-packages/datasets/utils/download_manager.py in <module>()
24
25 from .. import config
---> 26 from .file_utils import (
27 DownloadConfig,
28 cached_path,
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in <module>()
25 import posixpath
26 import requests
---> 27 from tqdm.contrib.concurrent import thread_map
28
29 from .. import __version__, config, utils
ModuleNotFoundError: No module named 'tqdm.contrib.concurrent'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.0
- Platform: Colab
- Python version: 3.7.11
- PyArrow version: 3.0.0
| 48 | Cannot import load_dataset on Colab
## Describe the bug
Got tqdm concurrent module not found error during importing load_dataset from datasets.
## Steps to reproduce the bug
Here [colab notebook](https://colab.research.google.com/drive/1pErWWnVP4P4mVHjSFUtkePd8Na_Qirg4?usp=sharing) to reproduce the error
On colab:
```python
!pip install datasets
from datasets import load_dataset
```
## Expected results
Works without error
## Actual results
Specify the actual results or traceback.
```
ModuleNotFoundError Traceback (most recent call last)
<ipython-input-2-8cc7de4c69eb> in <module>()
----> 1 from datasets import load_dataset, load_metric, Metric, MetricInfo, Features, Value
2 from sklearn.metrics import mean_squared_error
/usr/local/lib/python3.7/dist-packages/datasets/__init__.py in <module>()
31 )
32
---> 33 from .arrow_dataset import Dataset, concatenate_datasets
34 from .arrow_reader import ArrowReader, ReadInstruction
35 from .arrow_writer import ArrowWriter
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in <module>()
40 from tqdm.auto import tqdm
41
---> 42 from datasets.tasks.text_classification import TextClassification
43
44 from . import config, utils
/usr/local/lib/python3.7/dist-packages/datasets/tasks/__init__.py in <module>()
1 from typing import Optional
2
----> 3 from ..utils.logging import get_logger
4 from .automatic_speech_recognition import AutomaticSpeechRecognition
5 from .base import TaskTemplate
/usr/local/lib/python3.7/dist-packages/datasets/utils/__init__.py in <module>()
19
20 from . import logging
---> 21 from .download_manager import DownloadManager, GenerateMode
22 from .file_utils import DownloadConfig, cached_path, hf_bucket_url, is_remote_url, temp_seed
23 from .mock_download_manager import MockDownloadManager
/usr/local/lib/python3.7/dist-packages/datasets/utils/download_manager.py in <module>()
24
25 from .. import config
---> 26 from .file_utils import (
27 DownloadConfig,
28 cached_path,
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in <module>()
25 import posixpath
26 import requests
---> 27 from tqdm.contrib.concurrent import thread_map
28
29 from .. import __version__, config, utils
ModuleNotFoundError: No module named 'tqdm.contrib.concurrent'
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.10.0
- Platform: Colab
- Python version: 3.7.11
- PyArrow version: 3.0.0
Hi @bayartsogt-ya and @phosseini, thanks for reporting.
We are fixing this critical issue and making an urgent patch release of the `datasets` library today.
In the meantime, as pointed out by @mariosasko, you can circumvent this issue by updating the `tqdm` library:
```
!pip install -U tqdm
``` | [
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https://github.com/huggingface/datasets/issues/2691 | xtreme / pan-x cannot be downloaded | Hi @severo, thanks for reporting.
However I have not been able to reproduce this issue. Could you please confirm if the problem persists for you?
Maybe Dropbox (where the data source is hosted) was temporarily unavailable when you tried. | ## Describe the bug
Dataset xtreme / pan-x cannot be loaded
Seems related to https://github.com/huggingface/datasets/pull/2326
## Steps to reproduce the bug
```python
dataset = load_dataset("xtreme", "PAN-X.fr")
```
## Expected results
Load the dataset
## Actual results
```
FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/12h3qqog6q4bjve/panx_dataset.tar?dl=1
```
## Environment info
- `datasets` version: 1.9.0
- Platform: macOS-11.4-x86_64-i386-64bit
- Python version: 3.8.11
- PyArrow version: 4.0.1
| 39 | xtreme / pan-x cannot be downloaded
## Describe the bug
Dataset xtreme / pan-x cannot be loaded
Seems related to https://github.com/huggingface/datasets/pull/2326
## Steps to reproduce the bug
```python
dataset = load_dataset("xtreme", "PAN-X.fr")
```
## Expected results
Load the dataset
## Actual results
```
FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/12h3qqog6q4bjve/panx_dataset.tar?dl=1
```
## Environment info
- `datasets` version: 1.9.0
- Platform: macOS-11.4-x86_64-i386-64bit
- Python version: 3.8.11
- PyArrow version: 4.0.1
Hi @severo, thanks for reporting.
However I have not been able to reproduce this issue. Could you please confirm if the problem persists for you?
Maybe Dropbox (where the data source is hosted) was temporarily unavailable when you tried. | [
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https://github.com/huggingface/datasets/issues/2691 | xtreme / pan-x cannot be downloaded | Hmmm, the file (https://www.dropbox.com/s/dl/12h3qqog6q4bjve/panx_dataset.tar) really seems to be unavailable... I tried from various connexions and machines and got the same 404 error. Maybe the dataset has been loaded from the cache in your case? | ## Describe the bug
Dataset xtreme / pan-x cannot be loaded
Seems related to https://github.com/huggingface/datasets/pull/2326
## Steps to reproduce the bug
```python
dataset = load_dataset("xtreme", "PAN-X.fr")
```
## Expected results
Load the dataset
## Actual results
```
FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/12h3qqog6q4bjve/panx_dataset.tar?dl=1
```
## Environment info
- `datasets` version: 1.9.0
- Platform: macOS-11.4-x86_64-i386-64bit
- Python version: 3.8.11
- PyArrow version: 4.0.1
| 34 | xtreme / pan-x cannot be downloaded
## Describe the bug
Dataset xtreme / pan-x cannot be loaded
Seems related to https://github.com/huggingface/datasets/pull/2326
## Steps to reproduce the bug
```python
dataset = load_dataset("xtreme", "PAN-X.fr")
```
## Expected results
Load the dataset
## Actual results
```
FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/12h3qqog6q4bjve/panx_dataset.tar?dl=1
```
## Environment info
- `datasets` version: 1.9.0
- Platform: macOS-11.4-x86_64-i386-64bit
- Python version: 3.8.11
- PyArrow version: 4.0.1
Hmmm, the file (https://www.dropbox.com/s/dl/12h3qqog6q4bjve/panx_dataset.tar) really seems to be unavailable... I tried from various connexions and machines and got the same 404 error. Maybe the dataset has been loaded from the cache in your case? | [
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https://github.com/huggingface/datasets/issues/2691 | xtreme / pan-x cannot be downloaded | Yes @severo, weird... I could access the file when I answered to you, but now I cannot longer access it either... Maybe it was from the cache as you point out.
Anyway, I have opened an issue in the GitHub repository responsible for the original dataset: https://github.com/afshinrahimi/mmner/issues/4
I have also contacted the maintainer by email.
I'll keep you informed with their answer. | ## Describe the bug
Dataset xtreme / pan-x cannot be loaded
Seems related to https://github.com/huggingface/datasets/pull/2326
## Steps to reproduce the bug
```python
dataset = load_dataset("xtreme", "PAN-X.fr")
```
## Expected results
Load the dataset
## Actual results
```
FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/12h3qqog6q4bjve/panx_dataset.tar?dl=1
```
## Environment info
- `datasets` version: 1.9.0
- Platform: macOS-11.4-x86_64-i386-64bit
- Python version: 3.8.11
- PyArrow version: 4.0.1
| 62 | xtreme / pan-x cannot be downloaded
## Describe the bug
Dataset xtreme / pan-x cannot be loaded
Seems related to https://github.com/huggingface/datasets/pull/2326
## Steps to reproduce the bug
```python
dataset = load_dataset("xtreme", "PAN-X.fr")
```
## Expected results
Load the dataset
## Actual results
```
FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/12h3qqog6q4bjve/panx_dataset.tar?dl=1
```
## Environment info
- `datasets` version: 1.9.0
- Platform: macOS-11.4-x86_64-i386-64bit
- Python version: 3.8.11
- PyArrow version: 4.0.1
Yes @severo, weird... I could access the file when I answered to you, but now I cannot longer access it either... Maybe it was from the cache as you point out.
Anyway, I have opened an issue in the GitHub repository responsible for the original dataset: https://github.com/afshinrahimi/mmner/issues/4
I have also contacted the maintainer by email.
I'll keep you informed with their answer. | [
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https://github.com/huggingface/datasets/issues/2691 | xtreme / pan-x cannot be downloaded | Reply from the author/maintainer:
> Will fix the issue and let you know during the weekend. | ## Describe the bug
Dataset xtreme / pan-x cannot be loaded
Seems related to https://github.com/huggingface/datasets/pull/2326
## Steps to reproduce the bug
```python
dataset = load_dataset("xtreme", "PAN-X.fr")
```
## Expected results
Load the dataset
## Actual results
```
FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/12h3qqog6q4bjve/panx_dataset.tar?dl=1
```
## Environment info
- `datasets` version: 1.9.0
- Platform: macOS-11.4-x86_64-i386-64bit
- Python version: 3.8.11
- PyArrow version: 4.0.1
| 16 | xtreme / pan-x cannot be downloaded
## Describe the bug
Dataset xtreme / pan-x cannot be loaded
Seems related to https://github.com/huggingface/datasets/pull/2326
## Steps to reproduce the bug
```python
dataset = load_dataset("xtreme", "PAN-X.fr")
```
## Expected results
Load the dataset
## Actual results
```
FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/12h3qqog6q4bjve/panx_dataset.tar?dl=1
```
## Environment info
- `datasets` version: 1.9.0
- Platform: macOS-11.4-x86_64-i386-64bit
- Python version: 3.8.11
- PyArrow version: 4.0.1
Reply from the author/maintainer:
> Will fix the issue and let you know during the weekend. | [
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https://github.com/huggingface/datasets/issues/2691 | xtreme / pan-x cannot be downloaded | The author told that apparently Dropbox has changed their policy and no longer allow downloading the file without having signed in first. The author asked Hugging Face to host their dataset. | ## Describe the bug
Dataset xtreme / pan-x cannot be loaded
Seems related to https://github.com/huggingface/datasets/pull/2326
## Steps to reproduce the bug
```python
dataset = load_dataset("xtreme", "PAN-X.fr")
```
## Expected results
Load the dataset
## Actual results
```
FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/12h3qqog6q4bjve/panx_dataset.tar?dl=1
```
## Environment info
- `datasets` version: 1.9.0
- Platform: macOS-11.4-x86_64-i386-64bit
- Python version: 3.8.11
- PyArrow version: 4.0.1
| 31 | xtreme / pan-x cannot be downloaded
## Describe the bug
Dataset xtreme / pan-x cannot be loaded
Seems related to https://github.com/huggingface/datasets/pull/2326
## Steps to reproduce the bug
```python
dataset = load_dataset("xtreme", "PAN-X.fr")
```
## Expected results
Load the dataset
## Actual results
```
FileNotFoundError: Couldn't find file at https://www.dropbox.com/s/12h3qqog6q4bjve/panx_dataset.tar?dl=1
```
## Environment info
- `datasets` version: 1.9.0
- Platform: macOS-11.4-x86_64-i386-64bit
- Python version: 3.8.11
- PyArrow version: 4.0.1
The author told that apparently Dropbox has changed their policy and no longer allow downloading the file without having signed in first. The author asked Hugging Face to host their dataset. | [
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https://github.com/huggingface/datasets/issues/2689 | cannot save the dataset to disk after rename_column | Hi ! That's because you are trying to overwrite a file that is already open and being used.
Indeed `foo/dataset.arrow` is open and used by your `dataset` object.
When you do `rename_column`, the resulting dataset reads the data from the same arrow file.
In other cases like when using `map` on the other hand, the resulting dataset reads the data from another arrow file that is the result of the map transform.
Therefore overwriting a dataset after `rename_column` is not possible, but it is possible after `map`, since `rename_column` doesn't switch to using another arrow file (the actual data stay the same). | ## Describe the bug
If you use `rename_column` and do no other modification, you will be unable to save the dataset using `save_to_disk`
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
In [1]: from datasets import Dataset, load_from_disk
In [5]: dataset=Dataset.from_dict({'foo': [0]})
In [7]: dataset.save_to_disk('foo')
In [8]: dataset=load_from_disk('foo')
In [10]: dataset=dataset.rename_column('foo', 'bar')
In [11]: dataset.save_to_disk('foo')
---------------------------------------------------------------------------
PermissionError Traceback (most recent call last)
<ipython-input-11-a3bc0d4fc339> in <module>
----> 1 dataset.save_to_disk('foo')
/mnt/beegfs/projects/meerqat/anaconda3/envs/meerqat/lib/python3.7/site-packages/datasets/arrow_dataset.py in save_to_disk(self, dataset_path
, fs)
597 if Path(dataset_path, config.DATASET_ARROW_FILENAME) in cache_files_paths:
598 raise PermissionError(
--> 599 f"Tried to overwrite {Path(dataset_path, config.DATASET_ARROW_FILENAME)} but a dataset can't overwrite itself."
600 )
601 if Path(dataset_path, config.DATASET_INDICES_FILENAME) in cache_files_paths:
PermissionError: Tried to overwrite foo/dataset.arrow but a dataset can't overwrite itself.
```
N. B. I created the dataset from dict to enable easy reproduction but the same happens if you load an existing dataset (e.g. starting from `In [8]`)
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.8.0
- Platform: Linux-3.10.0-1160.11.1.el7.x86_64-x86_64-with-centos-7.9.2009-Core
- Python version: 3.7.10
- PyArrow version: 3.0.0
| 102 | cannot save the dataset to disk after rename_column
## Describe the bug
If you use `rename_column` and do no other modification, you will be unable to save the dataset using `save_to_disk`
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
In [1]: from datasets import Dataset, load_from_disk
In [5]: dataset=Dataset.from_dict({'foo': [0]})
In [7]: dataset.save_to_disk('foo')
In [8]: dataset=load_from_disk('foo')
In [10]: dataset=dataset.rename_column('foo', 'bar')
In [11]: dataset.save_to_disk('foo')
---------------------------------------------------------------------------
PermissionError Traceback (most recent call last)
<ipython-input-11-a3bc0d4fc339> in <module>
----> 1 dataset.save_to_disk('foo')
/mnt/beegfs/projects/meerqat/anaconda3/envs/meerqat/lib/python3.7/site-packages/datasets/arrow_dataset.py in save_to_disk(self, dataset_path
, fs)
597 if Path(dataset_path, config.DATASET_ARROW_FILENAME) in cache_files_paths:
598 raise PermissionError(
--> 599 f"Tried to overwrite {Path(dataset_path, config.DATASET_ARROW_FILENAME)} but a dataset can't overwrite itself."
600 )
601 if Path(dataset_path, config.DATASET_INDICES_FILENAME) in cache_files_paths:
PermissionError: Tried to overwrite foo/dataset.arrow but a dataset can't overwrite itself.
```
N. B. I created the dataset from dict to enable easy reproduction but the same happens if you load an existing dataset (e.g. starting from `In [8]`)
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.8.0
- Platform: Linux-3.10.0-1160.11.1.el7.x86_64-x86_64-with-centos-7.9.2009-Core
- Python version: 3.7.10
- PyArrow version: 3.0.0
Hi ! That's because you are trying to overwrite a file that is already open and being used.
Indeed `foo/dataset.arrow` is open and used by your `dataset` object.
When you do `rename_column`, the resulting dataset reads the data from the same arrow file.
In other cases like when using `map` on the other hand, the resulting dataset reads the data from another arrow file that is the result of the map transform.
Therefore overwriting a dataset after `rename_column` is not possible, but it is possible after `map`, since `rename_column` doesn't switch to using another arrow file (the actual data stay the same). | [
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https://github.com/huggingface/datasets/issues/2688 | hebrew language codes he and iw should be treated as aliases | Hi @eyaler, thanks for reporting.
While you are true with respect the Hebrew language tag ("iw" is deprecated and "he" is the preferred value), in the "mc4" dataset (which is a derived dataset) we have kept the language tags present in the original dataset: [Google C4](https://www.tensorflow.org/datasets/catalog/c4). | https://huggingface.co/datasets/mc4 not listed when searching for hebrew datasets (he) as it uses the older language code iw, preventing discoverability. | 46 | hebrew language codes he and iw should be treated as aliases
https://huggingface.co/datasets/mc4 not listed when searching for hebrew datasets (he) as it uses the older language code iw, preventing discoverability.
Hi @eyaler, thanks for reporting.
While you are true with respect the Hebrew language tag ("iw" is deprecated and "he" is the preferred value), in the "mc4" dataset (which is a derived dataset) we have kept the language tags present in the original dataset: [Google C4](https://www.tensorflow.org/datasets/catalog/c4). | [
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] |
https://github.com/huggingface/datasets/issues/2688 | hebrew language codes he and iw should be treated as aliases | For discoverability on the website I updated the YAML tags at the top of the mC4 dataset card https://github.com/huggingface/datasets/commit/38288087b1b02f97586e0346e8f28f4960f1fd37
Once the website is updated, mC4 will be listed in https://huggingface.co/datasets?filter=languages:he
| https://huggingface.co/datasets/mc4 not listed when searching for hebrew datasets (he) as it uses the older language code iw, preventing discoverability. | 30 | hebrew language codes he and iw should be treated as aliases
https://huggingface.co/datasets/mc4 not listed when searching for hebrew datasets (he) as it uses the older language code iw, preventing discoverability.
For discoverability on the website I updated the YAML tags at the top of the mC4 dataset card https://github.com/huggingface/datasets/commit/38288087b1b02f97586e0346e8f28f4960f1fd37
Once the website is updated, mC4 will be listed in https://huggingface.co/datasets?filter=languages:he
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https://github.com/huggingface/datasets/issues/2681 | 5 duplicate datasets | Yes this was documented in the PR that added this hf->paperswithcode mapping (https://github.com/huggingface/datasets/pull/2404) and AFAICT those are slightly distinct datasets so I think it's a wontfix
For context on the paperswithcode mapping you can also refer to https://github.com/huggingface/huggingface_hub/pull/43 which contains a lot of background discussion | ## Describe the bug
In 5 cases, I could find a dataset on Paperswithcode which references two Hugging Face datasets as dataset loaders. They are:
- https://paperswithcode.com/dataset/multinli -> https://huggingface.co/datasets/multi_nli and https://huggingface.co/datasets/multi_nli_mismatch
<img width="838" alt="Capture d’écran 2021-07-20 à 16 33 58" src="https://user-images.githubusercontent.com/1676121/126342757-4625522a-f788-41a3-bd1f-2a8b9817bbf5.png">
- https://paperswithcode.com/dataset/squad -> https://huggingface.co/datasets/squad and https://huggingface.co/datasets/squad_v2
- https://paperswithcode.com/dataset/narrativeqa -> https://huggingface.co/datasets/narrativeqa and https://huggingface.co/datasets/narrativeqa_manual
- https://paperswithcode.com/dataset/hate-speech-and-offensive-language -> https://huggingface.co/datasets/hate_offensive and https://huggingface.co/datasets/hate_speech_offensive
- https://paperswithcode.com/dataset/newsph-nli -> https://huggingface.co/datasets/newsph and https://huggingface.co/datasets/newsph_nli
Possible solutions:
- don't fix (it works)
- for each pair of duplicate datasets, remove one, and create an alias to the other.
## Steps to reproduce the bug
Visit the Paperswithcode links, and look at the "Dataset Loaders" section
## Expected results
There should only be one reference to a Hugging Face dataset loader
## Actual results
Two Hugging Face dataset loaders
| 45 | 5 duplicate datasets
## Describe the bug
In 5 cases, I could find a dataset on Paperswithcode which references two Hugging Face datasets as dataset loaders. They are:
- https://paperswithcode.com/dataset/multinli -> https://huggingface.co/datasets/multi_nli and https://huggingface.co/datasets/multi_nli_mismatch
<img width="838" alt="Capture d’écran 2021-07-20 à 16 33 58" src="https://user-images.githubusercontent.com/1676121/126342757-4625522a-f788-41a3-bd1f-2a8b9817bbf5.png">
- https://paperswithcode.com/dataset/squad -> https://huggingface.co/datasets/squad and https://huggingface.co/datasets/squad_v2
- https://paperswithcode.com/dataset/narrativeqa -> https://huggingface.co/datasets/narrativeqa and https://huggingface.co/datasets/narrativeqa_manual
- https://paperswithcode.com/dataset/hate-speech-and-offensive-language -> https://huggingface.co/datasets/hate_offensive and https://huggingface.co/datasets/hate_speech_offensive
- https://paperswithcode.com/dataset/newsph-nli -> https://huggingface.co/datasets/newsph and https://huggingface.co/datasets/newsph_nli
Possible solutions:
- don't fix (it works)
- for each pair of duplicate datasets, remove one, and create an alias to the other.
## Steps to reproduce the bug
Visit the Paperswithcode links, and look at the "Dataset Loaders" section
## Expected results
There should only be one reference to a Hugging Face dataset loader
## Actual results
Two Hugging Face dataset loaders
Yes this was documented in the PR that added this hf->paperswithcode mapping (https://github.com/huggingface/datasets/pull/2404) and AFAICT those are slightly distinct datasets so I think it's a wontfix
For context on the paperswithcode mapping you can also refer to https://github.com/huggingface/huggingface_hub/pull/43 which contains a lot of background discussion | [
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https://github.com/huggingface/datasets/issues/2679 | Cannot load the blog_authorship_corpus due to codec errors | Hi @izaskr, thanks for reporting.
However the traceback you joined does not correspond to the codec error message: it is about other error `NonMatchingSplitsSizesError`. Maybe you missed some important part of your traceback...
I'm going to have a look at the dataset anyway... | ## Describe the bug
A codec error is raised while loading the blog_authorship_corpus.
## Steps to reproduce the bug
```
from datasets import load_dataset
raw_datasets = load_dataset("blog_authorship_corpus")
```
## Expected results
Loading the dataset without errors.
## Actual results
An error similar to the one below was raised for (what seems like) every XML file.
/home/izaskr/.cache/huggingface/datasets/downloads/extracted/7cf52524f6517e168604b41c6719292e8f97abbe8f731e638b13423f4212359a/blogs/788358.male.24.Arts.Libra.xml cannot be loaded. Error message: 'utf-8' codec can't decode byte 0xe7 in position 7551: invalid continuation byte
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/load.py", line 856, in load_dataset
builder_instance.download_and_prepare(
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/builder.py", line 583, in download_and_prepare
self._download_and_prepare(
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/builder.py", line 671, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 74, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.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')}]
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Linux-4.15.0-132-generic-x86_64-with-glibc2.10
- Python version: 3.8.8
- PyArrow version: 4.0.1
| 43 | Cannot load the blog_authorship_corpus due to codec errors
## Describe the bug
A codec error is raised while loading the blog_authorship_corpus.
## Steps to reproduce the bug
```
from datasets import load_dataset
raw_datasets = load_dataset("blog_authorship_corpus")
```
## Expected results
Loading the dataset without errors.
## Actual results
An error similar to the one below was raised for (what seems like) every XML file.
/home/izaskr/.cache/huggingface/datasets/downloads/extracted/7cf52524f6517e168604b41c6719292e8f97abbe8f731e638b13423f4212359a/blogs/788358.male.24.Arts.Libra.xml cannot be loaded. Error message: 'utf-8' codec can't decode byte 0xe7 in position 7551: invalid continuation byte
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/load.py", line 856, in load_dataset
builder_instance.download_and_prepare(
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/builder.py", line 583, in download_and_prepare
self._download_and_prepare(
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/builder.py", line 671, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 74, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.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')}]
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Linux-4.15.0-132-generic-x86_64-with-glibc2.10
- Python version: 3.8.8
- PyArrow version: 4.0.1
Hi @izaskr, thanks for reporting.
However the traceback you joined does not correspond to the codec error message: it is about other error `NonMatchingSplitsSizesError`. Maybe you missed some important part of your traceback...
I'm going to have a look at the dataset anyway... | [
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https://github.com/huggingface/datasets/issues/2679 | Cannot load the blog_authorship_corpus due to codec errors | Hi @izaskr, thanks again for having reported this issue.
After investigation, I have created a Pull Request (#2685) to fix several issues with this dataset:
- the `NonMatchingSplitsSizesError`
- the `UnicodeDecodeError`
Once the Pull Request merged into master, you will be able to load this dataset if you install `datasets` from our GitHub repository master branch. Otherwise, you will be able to use it after our next release, by updating `datasets`: `pip install -U datasets`. | ## Describe the bug
A codec error is raised while loading the blog_authorship_corpus.
## Steps to reproduce the bug
```
from datasets import load_dataset
raw_datasets = load_dataset("blog_authorship_corpus")
```
## Expected results
Loading the dataset without errors.
## Actual results
An error similar to the one below was raised for (what seems like) every XML file.
/home/izaskr/.cache/huggingface/datasets/downloads/extracted/7cf52524f6517e168604b41c6719292e8f97abbe8f731e638b13423f4212359a/blogs/788358.male.24.Arts.Libra.xml cannot be loaded. Error message: 'utf-8' codec can't decode byte 0xe7 in position 7551: invalid continuation byte
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/load.py", line 856, in load_dataset
builder_instance.download_and_prepare(
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/builder.py", line 583, in download_and_prepare
self._download_and_prepare(
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/builder.py", line 671, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 74, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.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')}]
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Linux-4.15.0-132-generic-x86_64-with-glibc2.10
- Python version: 3.8.8
- PyArrow version: 4.0.1
| 75 | Cannot load the blog_authorship_corpus due to codec errors
## Describe the bug
A codec error is raised while loading the blog_authorship_corpus.
## Steps to reproduce the bug
```
from datasets import load_dataset
raw_datasets = load_dataset("blog_authorship_corpus")
```
## Expected results
Loading the dataset without errors.
## Actual results
An error similar to the one below was raised for (what seems like) every XML file.
/home/izaskr/.cache/huggingface/datasets/downloads/extracted/7cf52524f6517e168604b41c6719292e8f97abbe8f731e638b13423f4212359a/blogs/788358.male.24.Arts.Libra.xml cannot be loaded. Error message: 'utf-8' codec can't decode byte 0xe7 in position 7551: invalid continuation byte
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/load.py", line 856, in load_dataset
builder_instance.download_and_prepare(
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/builder.py", line 583, in download_and_prepare
self._download_and_prepare(
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/builder.py", line 671, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 74, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.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')}]
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Linux-4.15.0-132-generic-x86_64-with-glibc2.10
- Python version: 3.8.8
- PyArrow version: 4.0.1
Hi @izaskr, thanks again for having reported this issue.
After investigation, I have created a Pull Request (#2685) to fix several issues with this dataset:
- the `NonMatchingSplitsSizesError`
- the `UnicodeDecodeError`
Once the Pull Request merged into master, you will be able to load this dataset if you install `datasets` from our GitHub repository master branch. Otherwise, you will be able to use it after our next release, by updating `datasets`: `pip install -U datasets`. | [
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https://github.com/huggingface/datasets/issues/2679 | Cannot load the blog_authorship_corpus due to codec errors | @albertvillanova
Can you shed light on how this fix works?
We're experiencing a similar issue.
If we run several runs (eg in a Wandb sweep) the first run "works" but then we get `NonMatchingSplitsSizesError`
| run num | actual train examples # | expected example # | recorded example # |
| ------- | -------------- | ----------------- | -------- |
| 1 | 100 | 100 | 100 |
| 2 | 102 | 100 | 102 |
| 3 | 100 | 100 | 202 |
| 4 | 40 | 100 | 40 |
| 5 | 40 | 100 | 40 |
| 6 | 40 | 100 | 40 |
The second through the nth all crash with
```
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=19980970, num_examples=100, dataset_name='cies'), 'recorded': SplitInfo(name='train', num_bytes=40163811, num_examples=202, dataset_name='cies')}]
``` | ## Describe the bug
A codec error is raised while loading the blog_authorship_corpus.
## Steps to reproduce the bug
```
from datasets import load_dataset
raw_datasets = load_dataset("blog_authorship_corpus")
```
## Expected results
Loading the dataset without errors.
## Actual results
An error similar to the one below was raised for (what seems like) every XML file.
/home/izaskr/.cache/huggingface/datasets/downloads/extracted/7cf52524f6517e168604b41c6719292e8f97abbe8f731e638b13423f4212359a/blogs/788358.male.24.Arts.Libra.xml cannot be loaded. Error message: 'utf-8' codec can't decode byte 0xe7 in position 7551: invalid continuation byte
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/load.py", line 856, in load_dataset
builder_instance.download_and_prepare(
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/builder.py", line 583, in download_and_prepare
self._download_and_prepare(
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/builder.py", line 671, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 74, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.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')}]
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Linux-4.15.0-132-generic-x86_64-with-glibc2.10
- Python version: 3.8.8
- PyArrow version: 4.0.1
| 135 | Cannot load the blog_authorship_corpus due to codec errors
## Describe the bug
A codec error is raised while loading the blog_authorship_corpus.
## Steps to reproduce the bug
```
from datasets import load_dataset
raw_datasets = load_dataset("blog_authorship_corpus")
```
## Expected results
Loading the dataset without errors.
## Actual results
An error similar to the one below was raised for (what seems like) every XML file.
/home/izaskr/.cache/huggingface/datasets/downloads/extracted/7cf52524f6517e168604b41c6719292e8f97abbe8f731e638b13423f4212359a/blogs/788358.male.24.Arts.Libra.xml cannot be loaded. Error message: 'utf-8' codec can't decode byte 0xe7 in position 7551: invalid continuation byte
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/load.py", line 856, in load_dataset
builder_instance.download_and_prepare(
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/builder.py", line 583, in download_and_prepare
self._download_and_prepare(
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/builder.py", line 671, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/home/izaskr/anaconda3/envs/local_vae_older/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 74, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
datasets.utils.info_utils.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')}]
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Linux-4.15.0-132-generic-x86_64-with-glibc2.10
- Python version: 3.8.8
- PyArrow version: 4.0.1
@albertvillanova
Can you shed light on how this fix works?
We're experiencing a similar issue.
If we run several runs (eg in a Wandb sweep) the first run "works" but then we get `NonMatchingSplitsSizesError`
| run num | actual train examples # | expected example # | recorded example # |
| ------- | -------------- | ----------------- | -------- |
| 1 | 100 | 100 | 100 |
| 2 | 102 | 100 | 102 |
| 3 | 100 | 100 | 202 |
| 4 | 40 | 100 | 40 |
| 5 | 40 | 100 | 40 |
| 6 | 40 | 100 | 40 |
The second through the nth all crash with
```
datasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=19980970, num_examples=100, dataset_name='cies'), 'recorded': SplitInfo(name='train', num_bytes=40163811, num_examples=202, dataset_name='cies')}]
``` | [
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0.24273759126663208,
0.0948258489370346,
-0.20157089829444885,
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-0.12395967543125153,
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0.0875852108001709,
0.2611960172653198,
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0.16412353515625,
-0.40806102752685547
] |
https://github.com/huggingface/datasets/issues/2678 | Import Error in Kaggle notebook | @lhoestq I did, and then let pip handle the installation in `pip import datasets`. I also tried using conda but it gives the same error.
Edit: pyarrow version on kaggle is 4.0.0, it gets replaced with 4.0.1. So, I don't think uninstalling will change anything.
```
Install Trace of datasets:
Collecting datasets
Downloading datasets-1.9.0-py3-none-any.whl (262 kB)
|████████████████████████████████| 262 kB 834 kB/s eta 0:00:01
Requirement already satisfied: dill in /opt/conda/lib/python3.7/site-packages (from datasets) (0.3.4)
Collecting pyarrow!=4.0.0,>=1.0.0
Downloading pyarrow-4.0.1-cp37-cp37m-manylinux2014_x86_64.whl (21.8 MB)
|████████████████████████████████| 21.8 MB 6.2 MB/s eta 0:00:01
Requirement already satisfied: importlib-metadata in /opt/conda/lib/python3.7/site-packages (from datasets) (3.4.0)
Requirement already satisfied: huggingface-hub<0.1.0 in /opt/conda/lib/python3.7/site-packages (from datasets) (0.0.8)
Requirement already satisfied: pandas in /opt/conda/lib/python3.7/site-packages (from datasets) (1.2.4)
Requirement already satisfied: requests>=2.19.0 in /opt/conda/lib/python3.7/site-packages (from datasets) (2.25.1)
Requirement already satisfied: fsspec>=2021.05.0 in /opt/conda/lib/python3.7/site-packages (from datasets) (2021.6.1)
Requirement already satisfied: multiprocess in /opt/conda/lib/python3.7/site-packages (from datasets) (0.70.12.2)
Requirement already satisfied: packaging in /opt/conda/lib/python3.7/site-packages (from datasets) (20.9)
Collecting xxhash
Downloading xxhash-2.0.2-cp37-cp37m-manylinux2010_x86_64.whl (243 kB)
|████████████████████████████████| 243 kB 23.7 MB/s eta 0:00:01
Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.7/site-packages (from datasets) (1.19.5)
Requirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.7/site-packages (from datasets) (4.61.1)
Requirement already satisfied: filelock in /opt/conda/lib/python3.7/site-packages (from huggingface-hub<0.1.0->datasets) (3.0.12)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->datasets) (1.26.5)
Requirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->datasets) (2.10)
Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->datasets) (2021.5.30)
Requirement already satisfied: chardet<5,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->datasets) (4.0.0)
Requirement already satisfied: typing-extensions>=3.6.4 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->datasets) (3.7.4.3)
Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->datasets) (3.4.1)
Requirement already satisfied: pyparsing>=2.0.2 in /opt/conda/lib/python3.7/site-packages (from packaging->datasets) (2.4.7)
Requirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/lib/python3.7/site-packages (from pandas->datasets) (2.8.1)
Requirement already satisfied: pytz>=2017.3 in /opt/conda/lib/python3.7/site-packages (from pandas->datasets) (2021.1)
Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)
Installing collected packages: xxhash, pyarrow, datasets
Attempting uninstall: pyarrow
Found existing installation: pyarrow 4.0.0
Uninstalling pyarrow-4.0.0:
Successfully uninstalled pyarrow-4.0.0
Successfully installed datasets-1.9.0 pyarrow-4.0.1 xxhash-2.0.2
WARNING: Running pip as root will break packages and permissions. You should install packages reliably by using venv: https://pip.pypa.io/warnings/venv
``` | ## Describe the bug
Not able to import datasets library in kaggle notebooks
## Steps to reproduce the bug
```python
!pip install datasets
import datasets
```
## Expected results
No such error
## Actual results
```
ImportError Traceback (most recent call last)
<ipython-input-9-652e886d387f> in <module>
----> 1 import datasets
/opt/conda/lib/python3.7/site-packages/datasets/__init__.py in <module>
31 )
32
---> 33 from .arrow_dataset import Dataset, concatenate_datasets
34 from .arrow_reader import ArrowReader, ReadInstruction
35 from .arrow_writer import ArrowWriter
/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in <module>
36 import pandas as pd
37 import pyarrow as pa
---> 38 import pyarrow.compute as pc
39 from multiprocess import Pool, RLock
40 from tqdm.auto import tqdm
/opt/conda/lib/python3.7/site-packages/pyarrow/compute.py in <module>
16 # under the License.
17
---> 18 from pyarrow._compute import ( # noqa
19 Function,
20 FunctionOptions,
ImportError: /opt/conda/lib/python3.7/site-packages/pyarrow/_compute.cpython-37m-x86_64-linux-gnu.so: undefined symbol: _ZNK5arrow7compute15KernelSignature8ToStringEv
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Kaggle
- Python version: 3.7.10
- PyArrow version: 4.0.1
| 322 | Import Error in Kaggle notebook
## Describe the bug
Not able to import datasets library in kaggle notebooks
## Steps to reproduce the bug
```python
!pip install datasets
import datasets
```
## Expected results
No such error
## Actual results
```
ImportError Traceback (most recent call last)
<ipython-input-9-652e886d387f> in <module>
----> 1 import datasets
/opt/conda/lib/python3.7/site-packages/datasets/__init__.py in <module>
31 )
32
---> 33 from .arrow_dataset import Dataset, concatenate_datasets
34 from .arrow_reader import ArrowReader, ReadInstruction
35 from .arrow_writer import ArrowWriter
/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in <module>
36 import pandas as pd
37 import pyarrow as pa
---> 38 import pyarrow.compute as pc
39 from multiprocess import Pool, RLock
40 from tqdm.auto import tqdm
/opt/conda/lib/python3.7/site-packages/pyarrow/compute.py in <module>
16 # under the License.
17
---> 18 from pyarrow._compute import ( # noqa
19 Function,
20 FunctionOptions,
ImportError: /opt/conda/lib/python3.7/site-packages/pyarrow/_compute.cpython-37m-x86_64-linux-gnu.so: undefined symbol: _ZNK5arrow7compute15KernelSignature8ToStringEv
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Kaggle
- Python version: 3.7.10
- PyArrow version: 4.0.1
@lhoestq I did, and then let pip handle the installation in `pip import datasets`. I also tried using conda but it gives the same error.
Edit: pyarrow version on kaggle is 4.0.0, it gets replaced with 4.0.1. So, I don't think uninstalling will change anything.
```
Install Trace of datasets:
Collecting datasets
Downloading datasets-1.9.0-py3-none-any.whl (262 kB)
|████████████████████████████████| 262 kB 834 kB/s eta 0:00:01
Requirement already satisfied: dill in /opt/conda/lib/python3.7/site-packages (from datasets) (0.3.4)
Collecting pyarrow!=4.0.0,>=1.0.0
Downloading pyarrow-4.0.1-cp37-cp37m-manylinux2014_x86_64.whl (21.8 MB)
|████████████████████████████████| 21.8 MB 6.2 MB/s eta 0:00:01
Requirement already satisfied: importlib-metadata in /opt/conda/lib/python3.7/site-packages (from datasets) (3.4.0)
Requirement already satisfied: huggingface-hub<0.1.0 in /opt/conda/lib/python3.7/site-packages (from datasets) (0.0.8)
Requirement already satisfied: pandas in /opt/conda/lib/python3.7/site-packages (from datasets) (1.2.4)
Requirement already satisfied: requests>=2.19.0 in /opt/conda/lib/python3.7/site-packages (from datasets) (2.25.1)
Requirement already satisfied: fsspec>=2021.05.0 in /opt/conda/lib/python3.7/site-packages (from datasets) (2021.6.1)
Requirement already satisfied: multiprocess in /opt/conda/lib/python3.7/site-packages (from datasets) (0.70.12.2)
Requirement already satisfied: packaging in /opt/conda/lib/python3.7/site-packages (from datasets) (20.9)
Collecting xxhash
Downloading xxhash-2.0.2-cp37-cp37m-manylinux2010_x86_64.whl (243 kB)
|████████████████████████████████| 243 kB 23.7 MB/s eta 0:00:01
Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.7/site-packages (from datasets) (1.19.5)
Requirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.7/site-packages (from datasets) (4.61.1)
Requirement already satisfied: filelock in /opt/conda/lib/python3.7/site-packages (from huggingface-hub<0.1.0->datasets) (3.0.12)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->datasets) (1.26.5)
Requirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->datasets) (2.10)
Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->datasets) (2021.5.30)
Requirement already satisfied: chardet<5,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests>=2.19.0->datasets) (4.0.0)
Requirement already satisfied: typing-extensions>=3.6.4 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->datasets) (3.7.4.3)
Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->datasets) (3.4.1)
Requirement already satisfied: pyparsing>=2.0.2 in /opt/conda/lib/python3.7/site-packages (from packaging->datasets) (2.4.7)
Requirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/lib/python3.7/site-packages (from pandas->datasets) (2.8.1)
Requirement already satisfied: pytz>=2017.3 in /opt/conda/lib/python3.7/site-packages (from pandas->datasets) (2021.1)
Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)
Installing collected packages: xxhash, pyarrow, datasets
Attempting uninstall: pyarrow
Found existing installation: pyarrow 4.0.0
Uninstalling pyarrow-4.0.0:
Successfully uninstalled pyarrow-4.0.0
Successfully installed datasets-1.9.0 pyarrow-4.0.1 xxhash-2.0.2
WARNING: Running pip as root will break packages and permissions. You should install packages reliably by using venv: https://pip.pypa.io/warnings/venv
``` | [
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https://github.com/huggingface/datasets/issues/2678 | Import Error in Kaggle notebook | You may need to restart your kaggle notebook after installing a newer version of `pyarrow`.
If it doesn't work we'll probably have to create an issue on [arrow's JIRA](https://issues.apache.org/jira/projects/ARROW/issues/), and maybe ask kaggle why it could fail | ## Describe the bug
Not able to import datasets library in kaggle notebooks
## Steps to reproduce the bug
```python
!pip install datasets
import datasets
```
## Expected results
No such error
## Actual results
```
ImportError Traceback (most recent call last)
<ipython-input-9-652e886d387f> in <module>
----> 1 import datasets
/opt/conda/lib/python3.7/site-packages/datasets/__init__.py in <module>
31 )
32
---> 33 from .arrow_dataset import Dataset, concatenate_datasets
34 from .arrow_reader import ArrowReader, ReadInstruction
35 from .arrow_writer import ArrowWriter
/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in <module>
36 import pandas as pd
37 import pyarrow as pa
---> 38 import pyarrow.compute as pc
39 from multiprocess import Pool, RLock
40 from tqdm.auto import tqdm
/opt/conda/lib/python3.7/site-packages/pyarrow/compute.py in <module>
16 # under the License.
17
---> 18 from pyarrow._compute import ( # noqa
19 Function,
20 FunctionOptions,
ImportError: /opt/conda/lib/python3.7/site-packages/pyarrow/_compute.cpython-37m-x86_64-linux-gnu.so: undefined symbol: _ZNK5arrow7compute15KernelSignature8ToStringEv
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Kaggle
- Python version: 3.7.10
- PyArrow version: 4.0.1
| 37 | Import Error in Kaggle notebook
## Describe the bug
Not able to import datasets library in kaggle notebooks
## Steps to reproduce the bug
```python
!pip install datasets
import datasets
```
## Expected results
No such error
## Actual results
```
ImportError Traceback (most recent call last)
<ipython-input-9-652e886d387f> in <module>
----> 1 import datasets
/opt/conda/lib/python3.7/site-packages/datasets/__init__.py in <module>
31 )
32
---> 33 from .arrow_dataset import Dataset, concatenate_datasets
34 from .arrow_reader import ArrowReader, ReadInstruction
35 from .arrow_writer import ArrowWriter
/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in <module>
36 import pandas as pd
37 import pyarrow as pa
---> 38 import pyarrow.compute as pc
39 from multiprocess import Pool, RLock
40 from tqdm.auto import tqdm
/opt/conda/lib/python3.7/site-packages/pyarrow/compute.py in <module>
16 # under the License.
17
---> 18 from pyarrow._compute import ( # noqa
19 Function,
20 FunctionOptions,
ImportError: /opt/conda/lib/python3.7/site-packages/pyarrow/_compute.cpython-37m-x86_64-linux-gnu.so: undefined symbol: _ZNK5arrow7compute15KernelSignature8ToStringEv
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Kaggle
- Python version: 3.7.10
- PyArrow version: 4.0.1
You may need to restart your kaggle notebook after installing a newer version of `pyarrow`.
If it doesn't work we'll probably have to create an issue on [arrow's JIRA](https://issues.apache.org/jira/projects/ARROW/issues/), and maybe ask kaggle why it could fail | [
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https://github.com/huggingface/datasets/issues/2678 | Import Error in Kaggle notebook | > You may need to restart your kaggle notebook before after installing a newer version of `pyarrow`.
>
> If it doesn't work we'll probably have to create an issue on [arrow's JIRA](https://issues.apache.org/jira/projects/ARROW/issues/), and maybe ask kaggle why it could fail
It works after restarting.
My bad, I forgot to restart the notebook. Sorry for the trouble! | ## Describe the bug
Not able to import datasets library in kaggle notebooks
## Steps to reproduce the bug
```python
!pip install datasets
import datasets
```
## Expected results
No such error
## Actual results
```
ImportError Traceback (most recent call last)
<ipython-input-9-652e886d387f> in <module>
----> 1 import datasets
/opt/conda/lib/python3.7/site-packages/datasets/__init__.py in <module>
31 )
32
---> 33 from .arrow_dataset import Dataset, concatenate_datasets
34 from .arrow_reader import ArrowReader, ReadInstruction
35 from .arrow_writer import ArrowWriter
/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in <module>
36 import pandas as pd
37 import pyarrow as pa
---> 38 import pyarrow.compute as pc
39 from multiprocess import Pool, RLock
40 from tqdm.auto import tqdm
/opt/conda/lib/python3.7/site-packages/pyarrow/compute.py in <module>
16 # under the License.
17
---> 18 from pyarrow._compute import ( # noqa
19 Function,
20 FunctionOptions,
ImportError: /opt/conda/lib/python3.7/site-packages/pyarrow/_compute.cpython-37m-x86_64-linux-gnu.so: undefined symbol: _ZNK5arrow7compute15KernelSignature8ToStringEv
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Kaggle
- Python version: 3.7.10
- PyArrow version: 4.0.1
| 57 | Import Error in Kaggle notebook
## Describe the bug
Not able to import datasets library in kaggle notebooks
## Steps to reproduce the bug
```python
!pip install datasets
import datasets
```
## Expected results
No such error
## Actual results
```
ImportError Traceback (most recent call last)
<ipython-input-9-652e886d387f> in <module>
----> 1 import datasets
/opt/conda/lib/python3.7/site-packages/datasets/__init__.py in <module>
31 )
32
---> 33 from .arrow_dataset import Dataset, concatenate_datasets
34 from .arrow_reader import ArrowReader, ReadInstruction
35 from .arrow_writer import ArrowWriter
/opt/conda/lib/python3.7/site-packages/datasets/arrow_dataset.py in <module>
36 import pandas as pd
37 import pyarrow as pa
---> 38 import pyarrow.compute as pc
39 from multiprocess import Pool, RLock
40 from tqdm.auto import tqdm
/opt/conda/lib/python3.7/site-packages/pyarrow/compute.py in <module>
16 # under the License.
17
---> 18 from pyarrow._compute import ( # noqa
19 Function,
20 FunctionOptions,
ImportError: /opt/conda/lib/python3.7/site-packages/pyarrow/_compute.cpython-37m-x86_64-linux-gnu.so: undefined symbol: _ZNK5arrow7compute15KernelSignature8ToStringEv
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Kaggle
- Python version: 3.7.10
- PyArrow version: 4.0.1
> You may need to restart your kaggle notebook before after installing a newer version of `pyarrow`.
>
> If it doesn't work we'll probably have to create an issue on [arrow's JIRA](https://issues.apache.org/jira/projects/ARROW/issues/), and maybe ask kaggle why it could fail
It works after restarting.
My bad, I forgot to restart the notebook. Sorry for the trouble! | [
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https://github.com/huggingface/datasets/issues/2677 | Error when downloading C4 | Hi Thanks for reporting !
It looks like these files are not correctly reported in the list of expected files to download, let me fix that ;) | Hi,
I am trying to download `en` corpus from C4 dataset. However, I get an error caused by validation files download (see image). My code is very primitive:
`datasets.load_dataset('c4', 'en')`
Is this a bug or do I have some configurations missing on my server?
Thanks!
<img width="1014" alt="Снимок экрана 2021-07-20 в 11 37 17" src="https://user-images.githubusercontent.com/36672861/126289448-6e0db402-5f3f-485a-bf74-eb6e0271fc25.png"> | 27 | Error when downloading C4
Hi,
I am trying to download `en` corpus from C4 dataset. However, I get an error caused by validation files download (see image). My code is very primitive:
`datasets.load_dataset('c4', 'en')`
Is this a bug or do I have some configurations missing on my server?
Thanks!
<img width="1014" alt="Снимок экрана 2021-07-20 в 11 37 17" src="https://user-images.githubusercontent.com/36672861/126289448-6e0db402-5f3f-485a-bf74-eb6e0271fc25.png">
Hi Thanks for reporting !
It looks like these files are not correctly reported in the list of expected files to download, let me fix that ;) | [
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https://github.com/huggingface/datasets/issues/2677 | Error when downloading C4 | Alright this is fixed now. We'll do a new release soon to make the fix available.
In the meantime feel free to simply pass `ignore_verifications=True` to `load_dataset` to skip this error | Hi,
I am trying to download `en` corpus from C4 dataset. However, I get an error caused by validation files download (see image). My code is very primitive:
`datasets.load_dataset('c4', 'en')`
Is this a bug or do I have some configurations missing on my server?
Thanks!
<img width="1014" alt="Снимок экрана 2021-07-20 в 11 37 17" src="https://user-images.githubusercontent.com/36672861/126289448-6e0db402-5f3f-485a-bf74-eb6e0271fc25.png"> | 31 | Error when downloading C4
Hi,
I am trying to download `en` corpus from C4 dataset. However, I get an error caused by validation files download (see image). My code is very primitive:
`datasets.load_dataset('c4', 'en')`
Is this a bug or do I have some configurations missing on my server?
Thanks!
<img width="1014" alt="Снимок экрана 2021-07-20 в 11 37 17" src="https://user-images.githubusercontent.com/36672861/126289448-6e0db402-5f3f-485a-bf74-eb6e0271fc25.png">
Alright this is fixed now. We'll do a new release soon to make the fix available.
In the meantime feel free to simply pass `ignore_verifications=True` to `load_dataset` to skip this error | [
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https://github.com/huggingface/datasets/issues/2669 | Metric kwargs are not passed to underlying external metric f1_score | Hi @BramVanroy, thanks for reporting.
First, note that `"min"` is not an allowed value for `average`. According to scikit-learn [documentation](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html), `average` can only take the values: `{"micro", "macro", "samples", "weighted", "binary"} or None, default="binary"`.
Second, you should take into account that all additional metric-specific argument should be passed in the method `compute` (and not in the method `load_metric`). You can find more information in our documentation: https://huggingface.co/docs/datasets/using_metrics.html#computing-the-metric-scores
So for example, if you would like to calculate the macro-averaged F1 score, you should use:
```python
import datasets
f1 = datasets.load_metric("f1", keep_in_memory=True)
f1.add_batch(predictions=[0,2,3], references=[1, 2, 3])
f1.compute(average="macro")
``` | ## Describe the bug
When I want to use F1 score with average="min", this keyword argument does not seem to be passed through to the underlying sklearn metric. This is evident because [sklearn](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) throws an error telling me so.
## Steps to reproduce the bug
```python
import datasets
f1 = datasets.load_metric("f1", keep_in_memory=True, average="min")
f1.add_batch(predictions=[0,2,3], references=[1, 2, 3])
f1.compute()
```
## Expected results
No error, because `average="min"` should be passed correctly to f1_score in sklearn.
## Actual results
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\datasets\metric.py", line 402, in compute
output = self._compute(predictions=predictions, references=references, **kwargs)
File "C:\Users\bramv\.cache\huggingface\modules\datasets_modules\metrics\f1\82177930a325d4c28342bba0f116d73f6d92fb0c44cd67be32a07c1262b61cfe\f1.py", line 97, in _compute
"f1": f1_score(
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1071, in f1_score
return fbeta_score(y_true, y_pred, beta=1, labels=labels,
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1195, in fbeta_score
_, _, f, _ = precision_recall_fscore_support(y_true, y_pred,
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1464, in precision_recall_fscore_support
labels = _check_set_wise_labels(y_true, y_pred, average, labels,
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1294, in _check_set_wise_labels
raise ValueError("Target is %s but average='binary'. Please "
ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.9.2
- PyArrow version: 4.0.1 | 96 | Metric kwargs are not passed to underlying external metric f1_score
## Describe the bug
When I want to use F1 score with average="min", this keyword argument does not seem to be passed through to the underlying sklearn metric. This is evident because [sklearn](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) throws an error telling me so.
## Steps to reproduce the bug
```python
import datasets
f1 = datasets.load_metric("f1", keep_in_memory=True, average="min")
f1.add_batch(predictions=[0,2,3], references=[1, 2, 3])
f1.compute()
```
## Expected results
No error, because `average="min"` should be passed correctly to f1_score in sklearn.
## Actual results
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\datasets\metric.py", line 402, in compute
output = self._compute(predictions=predictions, references=references, **kwargs)
File "C:\Users\bramv\.cache\huggingface\modules\datasets_modules\metrics\f1\82177930a325d4c28342bba0f116d73f6d92fb0c44cd67be32a07c1262b61cfe\f1.py", line 97, in _compute
"f1": f1_score(
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1071, in f1_score
return fbeta_score(y_true, y_pred, beta=1, labels=labels,
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1195, in fbeta_score
_, _, f, _ = precision_recall_fscore_support(y_true, y_pred,
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1464, in precision_recall_fscore_support
labels = _check_set_wise_labels(y_true, y_pred, average, labels,
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1294, in _check_set_wise_labels
raise ValueError("Target is %s but average='binary'. Please "
ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.9.2
- PyArrow version: 4.0.1
Hi @BramVanroy, thanks for reporting.
First, note that `"min"` is not an allowed value for `average`. According to scikit-learn [documentation](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html), `average` can only take the values: `{"micro", "macro", "samples", "weighted", "binary"} or None, default="binary"`.
Second, you should take into account that all additional metric-specific argument should be passed in the method `compute` (and not in the method `load_metric`). You can find more information in our documentation: https://huggingface.co/docs/datasets/using_metrics.html#computing-the-metric-scores
So for example, if you would like to calculate the macro-averaged F1 score, you should use:
```python
import datasets
f1 = datasets.load_metric("f1", keep_in_memory=True)
f1.add_batch(predictions=[0,2,3], references=[1, 2, 3])
f1.compute(average="macro")
``` | [
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https://github.com/huggingface/datasets/issues/2669 | Metric kwargs are not passed to underlying external metric f1_score | Thanks, that was it. A bit strange though, since `load_metric` had an argument `metric_init_kwargs`. I assume that that's for specific initialisation arguments whereas `average` is for the function itself. | ## Describe the bug
When I want to use F1 score with average="min", this keyword argument does not seem to be passed through to the underlying sklearn metric. This is evident because [sklearn](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) throws an error telling me so.
## Steps to reproduce the bug
```python
import datasets
f1 = datasets.load_metric("f1", keep_in_memory=True, average="min")
f1.add_batch(predictions=[0,2,3], references=[1, 2, 3])
f1.compute()
```
## Expected results
No error, because `average="min"` should be passed correctly to f1_score in sklearn.
## Actual results
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\datasets\metric.py", line 402, in compute
output = self._compute(predictions=predictions, references=references, **kwargs)
File "C:\Users\bramv\.cache\huggingface\modules\datasets_modules\metrics\f1\82177930a325d4c28342bba0f116d73f6d92fb0c44cd67be32a07c1262b61cfe\f1.py", line 97, in _compute
"f1": f1_score(
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1071, in f1_score
return fbeta_score(y_true, y_pred, beta=1, labels=labels,
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1195, in fbeta_score
_, _, f, _ = precision_recall_fscore_support(y_true, y_pred,
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1464, in precision_recall_fscore_support
labels = _check_set_wise_labels(y_true, y_pred, average, labels,
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1294, in _check_set_wise_labels
raise ValueError("Target is %s but average='binary'. Please "
ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.9.2
- PyArrow version: 4.0.1 | 29 | Metric kwargs are not passed to underlying external metric f1_score
## Describe the bug
When I want to use F1 score with average="min", this keyword argument does not seem to be passed through to the underlying sklearn metric. This is evident because [sklearn](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) throws an error telling me so.
## Steps to reproduce the bug
```python
import datasets
f1 = datasets.load_metric("f1", keep_in_memory=True, average="min")
f1.add_batch(predictions=[0,2,3], references=[1, 2, 3])
f1.compute()
```
## Expected results
No error, because `average="min"` should be passed correctly to f1_score in sklearn.
## Actual results
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\datasets\metric.py", line 402, in compute
output = self._compute(predictions=predictions, references=references, **kwargs)
File "C:\Users\bramv\.cache\huggingface\modules\datasets_modules\metrics\f1\82177930a325d4c28342bba0f116d73f6d92fb0c44cd67be32a07c1262b61cfe\f1.py", line 97, in _compute
"f1": f1_score(
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1071, in f1_score
return fbeta_score(y_true, y_pred, beta=1, labels=labels,
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1195, in fbeta_score
_, _, f, _ = precision_recall_fscore_support(y_true, y_pred,
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1464, in precision_recall_fscore_support
labels = _check_set_wise_labels(y_true, y_pred, average, labels,
File "C:\Users\bramv\.virtualenvs\pipeline-TpEsXVex\lib\site-packages\sklearn\metrics\_classification.py", line 1294, in _check_set_wise_labels
raise ValueError("Target is %s but average='binary'. Please "
ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.9.2
- PyArrow version: 4.0.1
Thanks, that was it. A bit strange though, since `load_metric` had an argument `metric_init_kwargs`. I assume that that's for specific initialisation arguments whereas `average` is for the function itself. | [
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https://github.com/huggingface/datasets/issues/2663 | [`to_json`] add multi-proc sharding support | Hi @stas00,
I want to work on this issue and I was thinking why don't we use `imap` [in this loop](https://github.com/huggingface/datasets/blob/440b14d0dd428ae1b25881aa72ba7bbb8ad9ff84/src/datasets/io/json.py#L99)? This way, using offset (which is being used to slice the pyarrow table) we can convert pyarrow table to `json` using multiprocessing. I've a small code snippet for some clarity:
```
result = list(
pool.imap(self._apply_df, [(offset, batch_size) for offset in range(0, len(self.dataset), batch_size)])
)
```
`_apply_df` is a function which will return `batch.to_pandas().to_json(path_or_buf=None, orient="records", lines=True)` which is basically json version of the batched pyarrow table. Later on we can concatenate it to form json file?
I think the only downside here is to write file from `imap` output (output would be a list and we'll need to iterate over it and write in a file) which might add a little overhead cost. What do you think about this? | As discussed on slack it appears that `to_json` is quite slow on huge datasets like OSCAR.
I implemented sharded saving, which is much much faster - but the tqdm bars all overwrite each other, so it's hard to make sense of the progress, so if possible ideally this multi-proc support could be implemented internally in `to_json` via `num_proc` argument. I guess `num_proc` will be the number of shards?
I think the user will need to use this feature wisely, since too many processes writing to say normal style HD is likely to be slower than one process.
I'm not sure whether the user should be responsible to concatenate the shards at the end or `datasets`, either way works for my needs.
The code I was using:
```
from multiprocessing import cpu_count, Process, Queue
[...]
filtered_dataset = concat_dataset.map(filter_short_documents, batched=True, batch_size=256, num_proc=cpu_count())
DATASET_NAME = "oscar"
SHARDS = 10
def process_shard(idx):
print(f"Sharding {idx}")
ds_shard = filtered_dataset.shard(SHARDS, idx, contiguous=True)
# ds_shard = ds_shard.shuffle() # remove contiguous=True above if shuffling
print(f"Saving {DATASET_NAME}-{idx}.jsonl")
ds_shard.to_json(f"{DATASET_NAME}-{idx}.jsonl", orient="records", lines=True, force_ascii=False)
queue = Queue()
processes = [Process(target=process_shard, args=(idx,)) for idx in range(SHARDS)]
for p in processes:
p.start()
for p in processes:
p.join()
```
Thank you!
@lhoestq | 139 | [`to_json`] add multi-proc sharding support
As discussed on slack it appears that `to_json` is quite slow on huge datasets like OSCAR.
I implemented sharded saving, which is much much faster - but the tqdm bars all overwrite each other, so it's hard to make sense of the progress, so if possible ideally this multi-proc support could be implemented internally in `to_json` via `num_proc` argument. I guess `num_proc` will be the number of shards?
I think the user will need to use this feature wisely, since too many processes writing to say normal style HD is likely to be slower than one process.
I'm not sure whether the user should be responsible to concatenate the shards at the end or `datasets`, either way works for my needs.
The code I was using:
```
from multiprocessing import cpu_count, Process, Queue
[...]
filtered_dataset = concat_dataset.map(filter_short_documents, batched=True, batch_size=256, num_proc=cpu_count())
DATASET_NAME = "oscar"
SHARDS = 10
def process_shard(idx):
print(f"Sharding {idx}")
ds_shard = filtered_dataset.shard(SHARDS, idx, contiguous=True)
# ds_shard = ds_shard.shuffle() # remove contiguous=True above if shuffling
print(f"Saving {DATASET_NAME}-{idx}.jsonl")
ds_shard.to_json(f"{DATASET_NAME}-{idx}.jsonl", orient="records", lines=True, force_ascii=False)
queue = Queue()
processes = [Process(target=process_shard, args=(idx,)) for idx in range(SHARDS)]
for p in processes:
p.start()
for p in processes:
p.join()
```
Thank you!
@lhoestq
Hi @stas00,
I want to work on this issue and I was thinking why don't we use `imap` [in this loop](https://github.com/huggingface/datasets/blob/440b14d0dd428ae1b25881aa72ba7bbb8ad9ff84/src/datasets/io/json.py#L99)? This way, using offset (which is being used to slice the pyarrow table) we can convert pyarrow table to `json` using multiprocessing. I've a small code snippet for some clarity:
```
result = list(
pool.imap(self._apply_df, [(offset, batch_size) for offset in range(0, len(self.dataset), batch_size)])
)
```
`_apply_df` is a function which will return `batch.to_pandas().to_json(path_or_buf=None, orient="records", lines=True)` which is basically json version of the batched pyarrow table. Later on we can concatenate it to form json file?
I think the only downside here is to write file from `imap` output (output would be a list and we'll need to iterate over it and write in a file) which might add a little overhead cost. What do you think about this? | [
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https://github.com/huggingface/datasets/issues/2655 | Allow the selection of multiple columns at once | Hi! I was looking into this and hope you can clarify a point. Your my_dataset variable would be of type DatasetDict which means the alternative you've described (dict comprehension) is what makes sense.
Is there a reason why you wouldn't want to convert my_dataset to a pandas df if you'd like to use it like one? Please let me know if I'm missing something. | **Is your feature request related to a problem? Please describe.**
Similar to pandas, it would be great if we could select multiple columns at once.
**Describe the solution you'd like**
```python
my_dataset = ... # Has columns ['idx', 'sentence', 'label']
idx, label = my_dataset[['idx', 'label']]
```
**Describe alternatives you've considered**
we can do `[dataset[col] for col in ('idx', 'label')]`
**Additional context**
This is of course very minor.
| 64 | Allow the selection of multiple columns at once
**Is your feature request related to a problem? Please describe.**
Similar to pandas, it would be great if we could select multiple columns at once.
**Describe the solution you'd like**
```python
my_dataset = ... # Has columns ['idx', 'sentence', 'label']
idx, label = my_dataset[['idx', 'label']]
```
**Describe alternatives you've considered**
we can do `[dataset[col] for col in ('idx', 'label')]`
**Additional context**
This is of course very minor.
Hi! I was looking into this and hope you can clarify a point. Your my_dataset variable would be of type DatasetDict which means the alternative you've described (dict comprehension) is what makes sense.
Is there a reason why you wouldn't want to convert my_dataset to a pandas df if you'd like to use it like one? Please let me know if I'm missing something. | [
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https://github.com/huggingface/datasets/issues/2655 | Allow the selection of multiple columns at once | Hi! Sorry for the delay.
In this case, the dataset would be a `datasets.Dataset` and we want to select multiple columns, the `idx` and `label` columns for example.
My issue is that my dataset is too big for memory if I load everything into pandas. | **Is your feature request related to a problem? Please describe.**
Similar to pandas, it would be great if we could select multiple columns at once.
**Describe the solution you'd like**
```python
my_dataset = ... # Has columns ['idx', 'sentence', 'label']
idx, label = my_dataset[['idx', 'label']]
```
**Describe alternatives you've considered**
we can do `[dataset[col] for col in ('idx', 'label')]`
**Additional context**
This is of course very minor.
| 45 | Allow the selection of multiple columns at once
**Is your feature request related to a problem? Please describe.**
Similar to pandas, it would be great if we could select multiple columns at once.
**Describe the solution you'd like**
```python
my_dataset = ... # Has columns ['idx', 'sentence', 'label']
idx, label = my_dataset[['idx', 'label']]
```
**Describe alternatives you've considered**
we can do `[dataset[col] for col in ('idx', 'label')]`
**Additional context**
This is of course very minor.
Hi! Sorry for the delay.
In this case, the dataset would be a `datasets.Dataset` and we want to select multiple columns, the `idx` and `label` columns for example.
My issue is that my dataset is too big for memory if I load everything into pandas. | [
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] |
https://github.com/huggingface/datasets/issues/2654 | Give a user feedback if the dataset he loads is streamable or not | I understand it already raises a `NotImplementedError` exception, eg:
```
>>> dataset = load_dataset("journalists_questions", name="plain_text", split="train", streaming=True)
[...]
NotImplementedError: Extraction protocol for file at https://drive.google.com/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U is not implemented yet
```
| **Is your feature request related to a problem? Please describe.**
I would love to know if a `dataset` is with the current implementation streamable or not.
**Describe the solution you'd like**
We could show a warning when a dataset is loaded with `load_dataset('...',streaming=True)` when its lot streamable, e.g. if it is an archive.
**Describe alternatives you've considered**
Add a new metadata tag for "streaming"
| 30 | Give a user feedback if the dataset he loads is streamable or not
**Is your feature request related to a problem? Please describe.**
I would love to know if a `dataset` is with the current implementation streamable or not.
**Describe the solution you'd like**
We could show a warning when a dataset is loaded with `load_dataset('...',streaming=True)` when its lot streamable, e.g. if it is an archive.
**Describe alternatives you've considered**
Add a new metadata tag for "streaming"
I understand it already raises a `NotImplementedError` exception, eg:
```
>>> dataset = load_dataset("journalists_questions", name="plain_text", split="train", streaming=True)
[...]
NotImplementedError: Extraction protocol for file at https://drive.google.com/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U is not implemented yet
```
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https://github.com/huggingface/datasets/issues/2653 | Add SD task for SUPERB | Note that this subset requires us to:
* generate the LibriMix corpus from LibriSpeech
* prepare the corpus for diarization
As suggested by @lhoestq we should perform these steps locally and add the prepared data to this public repo on the Hub: https://huggingface.co/datasets/superb/superb-data
Then we can use the URLs for the files to load the data in `superb`'s dataset loading script.
For consistency, I suggest we name the folders in `superb-data` in the same way as the configs in the dataset loading script - e.g. use `sd` for speech diarization in both places :) | Include the SD (Speaker Diarization) task as described in the [SUPERB paper](https://arxiv.org/abs/2105.01051) and `s3prl` [instructions](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream#sd-speaker-diarization).
Steps:
- [x] Generate the LibriMix corpus
- [x] Prepare the corpus for diarization
- [x] Upload these files to the superb-data repo
- [x] Transcribe the corresponding s3prl processing of these files into our superb loading script
- [ ] README: tags + description sections
Related to #2619.
cc: @lewtun
| 94 | Add SD task for SUPERB
Include the SD (Speaker Diarization) task as described in the [SUPERB paper](https://arxiv.org/abs/2105.01051) and `s3prl` [instructions](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream#sd-speaker-diarization).
Steps:
- [x] Generate the LibriMix corpus
- [x] Prepare the corpus for diarization
- [x] Upload these files to the superb-data repo
- [x] Transcribe the corresponding s3prl processing of these files into our superb loading script
- [ ] README: tags + description sections
Related to #2619.
cc: @lewtun
Note that this subset requires us to:
* generate the LibriMix corpus from LibriSpeech
* prepare the corpus for diarization
As suggested by @lhoestq we should perform these steps locally and add the prepared data to this public repo on the Hub: https://huggingface.co/datasets/superb/superb-data
Then we can use the URLs for the files to load the data in `superb`'s dataset loading script.
For consistency, I suggest we name the folders in `superb-data` in the same way as the configs in the dataset loading script - e.g. use `sd` for speech diarization in both places :) | [
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https://github.com/huggingface/datasets/issues/2653 | Add SD task for SUPERB | @lewtun @lhoestq:
I have already generated the LibriMix corpus and prepared the corpus for diarization. The output is 3 dirs (train, dev, test), each one containing 6 files: reco2dur rttm segments spk2utt utt2spk wav.scp
Next steps:
- Upload these files to the superb-data repo
- Transcribe the corresponding s3prl processing of these files into our superb loading script
Note that processing of these files is a bit more intricate than usual datasets: https://github.com/s3prl/s3prl/blob/master/s3prl/downstream/diarization/dataset.py#L233
| Include the SD (Speaker Diarization) task as described in the [SUPERB paper](https://arxiv.org/abs/2105.01051) and `s3prl` [instructions](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream#sd-speaker-diarization).
Steps:
- [x] Generate the LibriMix corpus
- [x] Prepare the corpus for diarization
- [x] Upload these files to the superb-data repo
- [x] Transcribe the corresponding s3prl processing of these files into our superb loading script
- [ ] README: tags + description sections
Related to #2619.
cc: @lewtun
| 73 | Add SD task for SUPERB
Include the SD (Speaker Diarization) task as described in the [SUPERB paper](https://arxiv.org/abs/2105.01051) and `s3prl` [instructions](https://github.com/s3prl/s3prl/tree/master/s3prl/downstream#sd-speaker-diarization).
Steps:
- [x] Generate the LibriMix corpus
- [x] Prepare the corpus for diarization
- [x] Upload these files to the superb-data repo
- [x] Transcribe the corresponding s3prl processing of these files into our superb loading script
- [ ] README: tags + description sections
Related to #2619.
cc: @lewtun
@lewtun @lhoestq:
I have already generated the LibriMix corpus and prepared the corpus for diarization. The output is 3 dirs (train, dev, test), each one containing 6 files: reco2dur rttm segments spk2utt utt2spk wav.scp
Next steps:
- Upload these files to the superb-data repo
- Transcribe the corresponding s3prl processing of these files into our superb loading script
Note that processing of these files is a bit more intricate than usual datasets: https://github.com/s3prl/s3prl/blob/master/s3prl/downstream/diarization/dataset.py#L233
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https://github.com/huggingface/datasets/issues/2651 | Setting log level higher than warning does not suppress progress bar | Hi,
you can suppress progress bars by patching logging as follows:
```python
import datasets
import logging
datasets.logging.get_verbosity = lambda: logging.NOTSET
# map call ...
``` | ## Describe the bug
I would like to disable progress bars for `.map` method (and other methods like `.filter` and `load_dataset` as well).
According to #1627 one can suppress it by setting log level higher than `warning`, however doing so doesn't suppress it with version 1.9.0.
I also tried to set `DATASETS_VERBOSITY` environment variable to `error` or `critical` but it also didn't work.
## Steps to reproduce the bug
```python
import datasets
from datasets.utils.logging import set_verbosity_error
set_verbosity_error()
def dummy_map(batch):
return batch
common_voice_train = datasets.load_dataset("common_voice", "de", split="train")
common_voice_test = datasets.load_dataset("common_voice", "de", split="test")
common_voice_train.map(dummy_map)
```
## Expected results
- The progress bar for `.map` call won't be shown
## Actual results
- The progress bar for `.map` is still shown
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Linux-5.4.0-1045-aws-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.5
- PyArrow version: 4.0.1
| 25 | Setting log level higher than warning does not suppress progress bar
## Describe the bug
I would like to disable progress bars for `.map` method (and other methods like `.filter` and `load_dataset` as well).
According to #1627 one can suppress it by setting log level higher than `warning`, however doing so doesn't suppress it with version 1.9.0.
I also tried to set `DATASETS_VERBOSITY` environment variable to `error` or `critical` but it also didn't work.
## Steps to reproduce the bug
```python
import datasets
from datasets.utils.logging import set_verbosity_error
set_verbosity_error()
def dummy_map(batch):
return batch
common_voice_train = datasets.load_dataset("common_voice", "de", split="train")
common_voice_test = datasets.load_dataset("common_voice", "de", split="test")
common_voice_train.map(dummy_map)
```
## Expected results
- The progress bar for `.map` call won't be shown
## Actual results
- The progress bar for `.map` is still shown
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Linux-5.4.0-1045-aws-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.5
- PyArrow version: 4.0.1
Hi,
you can suppress progress bars by patching logging as follows:
```python
import datasets
import logging
datasets.logging.get_verbosity = lambda: logging.NOTSET
# map call ...
``` | [
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https://github.com/huggingface/datasets/issues/2651 | Setting log level higher than warning does not suppress progress bar | Note also that you can disable the progress bar with
```python
from datasets.utils import disable_progress_bar
disable_progress_bar()
```
See https://github.com/huggingface/datasets/blob/8814b393984c1c2e1800ba370de2a9f7c8644908/src/datasets/utils/tqdm_utils.py#L84 | ## Describe the bug
I would like to disable progress bars for `.map` method (and other methods like `.filter` and `load_dataset` as well).
According to #1627 one can suppress it by setting log level higher than `warning`, however doing so doesn't suppress it with version 1.9.0.
I also tried to set `DATASETS_VERBOSITY` environment variable to `error` or `critical` but it also didn't work.
## Steps to reproduce the bug
```python
import datasets
from datasets.utils.logging import set_verbosity_error
set_verbosity_error()
def dummy_map(batch):
return batch
common_voice_train = datasets.load_dataset("common_voice", "de", split="train")
common_voice_test = datasets.load_dataset("common_voice", "de", split="test")
common_voice_train.map(dummy_map)
```
## Expected results
- The progress bar for `.map` call won't be shown
## Actual results
- The progress bar for `.map` is still shown
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Linux-5.4.0-1045-aws-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.5
- PyArrow version: 4.0.1
| 19 | Setting log level higher than warning does not suppress progress bar
## Describe the bug
I would like to disable progress bars for `.map` method (and other methods like `.filter` and `load_dataset` as well).
According to #1627 one can suppress it by setting log level higher than `warning`, however doing so doesn't suppress it with version 1.9.0.
I also tried to set `DATASETS_VERBOSITY` environment variable to `error` or `critical` but it also didn't work.
## Steps to reproduce the bug
```python
import datasets
from datasets.utils.logging import set_verbosity_error
set_verbosity_error()
def dummy_map(batch):
return batch
common_voice_train = datasets.load_dataset("common_voice", "de", split="train")
common_voice_test = datasets.load_dataset("common_voice", "de", split="test")
common_voice_train.map(dummy_map)
```
## Expected results
- The progress bar for `.map` call won't be shown
## Actual results
- The progress bar for `.map` is still shown
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.0
- Platform: Linux-5.4.0-1045-aws-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.5
- PyArrow version: 4.0.1
Note also that you can disable the progress bar with
```python
from datasets.utils import disable_progress_bar
disable_progress_bar()
```
See https://github.com/huggingface/datasets/blob/8814b393984c1c2e1800ba370de2a9f7c8644908/src/datasets/utils/tqdm_utils.py#L84 | [
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https://github.com/huggingface/datasets/issues/2646 | downloading of yahoo_answers_topics dataset failed | Hi ! I just tested and it worked fine today for me.
I think this is because the dataset is stored on Google Drive which has a quota limit for the number of downloads per day, see this similar issue https://github.com/huggingface/datasets/issues/996
Feel free to try again today, now that the quota was reset | ## Describe the bug
I get an error datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files when I try to download the yahoo_answers_topics dataset
## Steps to reproduce the bug
self.dataset = load_dataset(
'yahoo_answers_topics', cache_dir=self.config['yahoo_cache_dir'], split='train[:90%]')
# Sample code to reproduce the bug
self.dataset = load_dataset(
'yahoo_answers_topics', cache_dir=self.config['yahoo_cache_dir'], split='train[:90%]')
## Expected results
A clear and concise description of the expected results.
## Actual results
Specify the actual results or traceback.
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files
| 53 | downloading of yahoo_answers_topics dataset failed
## Describe the bug
I get an error datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files when I try to download the yahoo_answers_topics dataset
## Steps to reproduce the bug
self.dataset = load_dataset(
'yahoo_answers_topics', cache_dir=self.config['yahoo_cache_dir'], split='train[:90%]')
# Sample code to reproduce the bug
self.dataset = load_dataset(
'yahoo_answers_topics', cache_dir=self.config['yahoo_cache_dir'], split='train[:90%]')
## Expected results
A clear and concise description of the expected results.
## Actual results
Specify the actual results or traceback.
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files
Hi ! I just tested and it worked fine today for me.
I think this is because the dataset is stored on Google Drive which has a quota limit for the number of downloads per day, see this similar issue https://github.com/huggingface/datasets/issues/996
Feel free to try again today, now that the quota was reset | [
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https://github.com/huggingface/datasets/issues/2645 | load_dataset processing failed with OS error after downloading a dataset | Hi ! It looks like an issue with pytorch.
Could you try to run `import torch` and see if it raises an error ? | ## Describe the bug
After downloading a dataset like opus100, there is a bug that
OSError: Cannot find data file.
Original error:
dlopen: cannot load any more object with static TLS
## Steps to reproduce the bug
```python
from datasets import load_dataset
this_dataset = load_dataset('opus100', 'af-en')
```
## Expected results
there is no error when running load_dataset.
## Actual results
Specify the actual results or traceback.
Traceback (most recent call last):
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 652, in _download_and_prep
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 989, in _prepare_split
example = self.info.features.encode_example(record)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 952, in encode_example
example = cast_to_python_objects(example)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 219, in cast_to_python_ob
return _cast_to_python_objects(obj)[0]
File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 165, in _cast_to_python_o
import torch
File "/home/anaconda3/lib/python3.6/site-packages/torch/__init__.py", line 188, in <module>
_load_global_deps()
File "/home/anaconda3/lib/python3.6/site-packages/torch/__init__.py", line 141, in _load_global_deps
ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
File "/home/anaconda3/lib/python3.6/ctypes/__init__.py", line 348, in __init__
self._handle = _dlopen(self._name, mode)
OSError: dlopen: cannot load any more object with static TLS
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "download_hub_opus100.py", line 9, in <module>
this_dataset = load_dataset('opus100', language_pair)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/load.py", line 748, in load_dataset
use_auth_token=use_auth_token,
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 575, in download_and_prepa
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 658, in _download_and_prep
+ str(e)
OSError: Cannot find data file.
Original error:
dlopen: cannot load any more object with static TLS
## Environment info
- `datasets` version: 1.8.0
- Platform: Linux-3.13.0-32-generic-x86_64-with-debian-jessie-sid
- Python version: 3.6.6
- PyArrow version: 3.0.0
| 24 | load_dataset processing failed with OS error after downloading a dataset
## Describe the bug
After downloading a dataset like opus100, there is a bug that
OSError: Cannot find data file.
Original error:
dlopen: cannot load any more object with static TLS
## Steps to reproduce the bug
```python
from datasets import load_dataset
this_dataset = load_dataset('opus100', 'af-en')
```
## Expected results
there is no error when running load_dataset.
## Actual results
Specify the actual results or traceback.
Traceback (most recent call last):
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 652, in _download_and_prep
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 989, in _prepare_split
example = self.info.features.encode_example(record)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 952, in encode_example
example = cast_to_python_objects(example)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 219, in cast_to_python_ob
return _cast_to_python_objects(obj)[0]
File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 165, in _cast_to_python_o
import torch
File "/home/anaconda3/lib/python3.6/site-packages/torch/__init__.py", line 188, in <module>
_load_global_deps()
File "/home/anaconda3/lib/python3.6/site-packages/torch/__init__.py", line 141, in _load_global_deps
ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
File "/home/anaconda3/lib/python3.6/ctypes/__init__.py", line 348, in __init__
self._handle = _dlopen(self._name, mode)
OSError: dlopen: cannot load any more object with static TLS
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "download_hub_opus100.py", line 9, in <module>
this_dataset = load_dataset('opus100', language_pair)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/load.py", line 748, in load_dataset
use_auth_token=use_auth_token,
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 575, in download_and_prepa
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 658, in _download_and_prep
+ str(e)
OSError: Cannot find data file.
Original error:
dlopen: cannot load any more object with static TLS
## Environment info
- `datasets` version: 1.8.0
- Platform: Linux-3.13.0-32-generic-x86_64-with-debian-jessie-sid
- Python version: 3.6.6
- PyArrow version: 3.0.0
Hi ! It looks like an issue with pytorch.
Could you try to run `import torch` and see if it raises an error ? | [
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https://github.com/huggingface/datasets/issues/2645 | load_dataset processing failed with OS error after downloading a dataset | > Hi ! It looks like an issue with pytorch.
>
> Could you try to run `import torch` and see if it raises an error ?
It works. Thank you! | ## Describe the bug
After downloading a dataset like opus100, there is a bug that
OSError: Cannot find data file.
Original error:
dlopen: cannot load any more object with static TLS
## Steps to reproduce the bug
```python
from datasets import load_dataset
this_dataset = load_dataset('opus100', 'af-en')
```
## Expected results
there is no error when running load_dataset.
## Actual results
Specify the actual results or traceback.
Traceback (most recent call last):
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 652, in _download_and_prep
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 989, in _prepare_split
example = self.info.features.encode_example(record)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 952, in encode_example
example = cast_to_python_objects(example)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 219, in cast_to_python_ob
return _cast_to_python_objects(obj)[0]
File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 165, in _cast_to_python_o
import torch
File "/home/anaconda3/lib/python3.6/site-packages/torch/__init__.py", line 188, in <module>
_load_global_deps()
File "/home/anaconda3/lib/python3.6/site-packages/torch/__init__.py", line 141, in _load_global_deps
ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
File "/home/anaconda3/lib/python3.6/ctypes/__init__.py", line 348, in __init__
self._handle = _dlopen(self._name, mode)
OSError: dlopen: cannot load any more object with static TLS
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "download_hub_opus100.py", line 9, in <module>
this_dataset = load_dataset('opus100', language_pair)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/load.py", line 748, in load_dataset
use_auth_token=use_auth_token,
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 575, in download_and_prepa
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 658, in _download_and_prep
+ str(e)
OSError: Cannot find data file.
Original error:
dlopen: cannot load any more object with static TLS
## Environment info
- `datasets` version: 1.8.0
- Platform: Linux-3.13.0-32-generic-x86_64-with-debian-jessie-sid
- Python version: 3.6.6
- PyArrow version: 3.0.0
| 31 | load_dataset processing failed with OS error after downloading a dataset
## Describe the bug
After downloading a dataset like opus100, there is a bug that
OSError: Cannot find data file.
Original error:
dlopen: cannot load any more object with static TLS
## Steps to reproduce the bug
```python
from datasets import load_dataset
this_dataset = load_dataset('opus100', 'af-en')
```
## Expected results
there is no error when running load_dataset.
## Actual results
Specify the actual results or traceback.
Traceback (most recent call last):
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 652, in _download_and_prep
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 989, in _prepare_split
example = self.info.features.encode_example(record)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 952, in encode_example
example = cast_to_python_objects(example)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 219, in cast_to_python_ob
return _cast_to_python_objects(obj)[0]
File "/home/anaconda3/lib/python3.6/site-packages/datasets/features.py", line 165, in _cast_to_python_o
import torch
File "/home/anaconda3/lib/python3.6/site-packages/torch/__init__.py", line 188, in <module>
_load_global_deps()
File "/home/anaconda3/lib/python3.6/site-packages/torch/__init__.py", line 141, in _load_global_deps
ctypes.CDLL(lib_path, mode=ctypes.RTLD_GLOBAL)
File "/home/anaconda3/lib/python3.6/ctypes/__init__.py", line 348, in __init__
self._handle = _dlopen(self._name, mode)
OSError: dlopen: cannot load any more object with static TLS
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "download_hub_opus100.py", line 9, in <module>
this_dataset = load_dataset('opus100', language_pair)
File "/home/anaconda3/lib/python3.6/site-packages/datasets/load.py", line 748, in load_dataset
use_auth_token=use_auth_token,
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 575, in download_and_prepa
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/anaconda3/lib/python3.6/site-packages/datasets/builder.py", line 658, in _download_and_prep
+ str(e)
OSError: Cannot find data file.
Original error:
dlopen: cannot load any more object with static TLS
## Environment info
- `datasets` version: 1.8.0
- Platform: Linux-3.13.0-32-generic-x86_64-with-debian-jessie-sid
- Python version: 3.6.6
- PyArrow version: 3.0.0
> Hi ! It looks like an issue with pytorch.
>
> Could you try to run `import torch` and see if it raises an error ?
It works. Thank you! | [
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https://github.com/huggingface/datasets/issues/2644 | Batched `map` not allowed to return 0 items | Hi ! Thanks for reporting. Indeed it looks like type inference makes it fail. We should probably just ignore this step until a non-empty batch is passed. | ## Describe the bug
I'm trying to use `map` to filter a large dataset by selecting rows that match an expensive condition (files referenced by one of the columns need to exist in the filesystem, so we have to `stat` them). According to [the documentation](https://huggingface.co/docs/datasets/processing.html#augmenting-the-dataset), `a batch mapped function can take as input a batch of size N and return a batch of size M where M can be greater or less than N and can even be zero`.
However, when the returned batch has a size of zero (neither item in the batch fulfilled the condition), we get an `index out of bounds` error. I think that `arrow_writer.py` is [trying to infer the returned types using the first element returned](https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_writer.py#L100), but no elements were returned in this case.
For this error to happen, I'm returning a dictionary that contains empty lists for the keys I want to keep, see below. If I return an empty dictionary instead (no keys), then a different error eventually occurs.
## Steps to reproduce the bug
```python
def select_rows(examples):
# `key` is a column name that exists in the original dataset
# The following line simulates no matches found, so we return an empty batch
result = {'key': []}
return result
filtered_dataset = dataset.map(
select_rows,
remove_columns = dataset.column_names,
batched = True,
num_proc = 1,
desc = "Selecting rows with images that exist"
)
```
The code above immediately triggers the exception. If we use the following instead:
```python
def select_rows(examples):
# `key` is a column name that exists in the original dataset
result = {'key': []} # or defaultdict or whatever
# code to check for condition and append elements to result
# some_items_found will be set to True if there were any matching elements in the batch
return result if some_items_found else {}
```
Then it _seems_ to work, but it eventually fails with some sort of schema error. I believe it may happen when an empty batch is followed by a non-empty one, but haven't set up a test to verify it.
In my opinion, returning a dictionary with empty lists and valid column names should be accepted as a valid result with zero items.
## Expected results
The dataset would be filtered and only the matching fields would be returned.
## Actual results
An exception is encountered, as described. Using a workaround makes it fail further along the line.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.1.dev0
- Platform: Linux-5.4.0-53-generic-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
| 27 | Batched `map` not allowed to return 0 items
## Describe the bug
I'm trying to use `map` to filter a large dataset by selecting rows that match an expensive condition (files referenced by one of the columns need to exist in the filesystem, so we have to `stat` them). According to [the documentation](https://huggingface.co/docs/datasets/processing.html#augmenting-the-dataset), `a batch mapped function can take as input a batch of size N and return a batch of size M where M can be greater or less than N and can even be zero`.
However, when the returned batch has a size of zero (neither item in the batch fulfilled the condition), we get an `index out of bounds` error. I think that `arrow_writer.py` is [trying to infer the returned types using the first element returned](https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_writer.py#L100), but no elements were returned in this case.
For this error to happen, I'm returning a dictionary that contains empty lists for the keys I want to keep, see below. If I return an empty dictionary instead (no keys), then a different error eventually occurs.
## Steps to reproduce the bug
```python
def select_rows(examples):
# `key` is a column name that exists in the original dataset
# The following line simulates no matches found, so we return an empty batch
result = {'key': []}
return result
filtered_dataset = dataset.map(
select_rows,
remove_columns = dataset.column_names,
batched = True,
num_proc = 1,
desc = "Selecting rows with images that exist"
)
```
The code above immediately triggers the exception. If we use the following instead:
```python
def select_rows(examples):
# `key` is a column name that exists in the original dataset
result = {'key': []} # or defaultdict or whatever
# code to check for condition and append elements to result
# some_items_found will be set to True if there were any matching elements in the batch
return result if some_items_found else {}
```
Then it _seems_ to work, but it eventually fails with some sort of schema error. I believe it may happen when an empty batch is followed by a non-empty one, but haven't set up a test to verify it.
In my opinion, returning a dictionary with empty lists and valid column names should be accepted as a valid result with zero items.
## Expected results
The dataset would be filtered and only the matching fields would be returned.
## Actual results
An exception is encountered, as described. Using a workaround makes it fail further along the line.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.1.dev0
- Platform: Linux-5.4.0-53-generic-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
Hi ! Thanks for reporting. Indeed it looks like type inference makes it fail. We should probably just ignore this step until a non-empty batch is passed. | [
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https://github.com/huggingface/datasets/issues/2644 | Batched `map` not allowed to return 0 items | Sounds good! Do you want me to propose a PR? I'm quite busy right now, but if it's not too urgent I could take a look next week. | ## Describe the bug
I'm trying to use `map` to filter a large dataset by selecting rows that match an expensive condition (files referenced by one of the columns need to exist in the filesystem, so we have to `stat` them). According to [the documentation](https://huggingface.co/docs/datasets/processing.html#augmenting-the-dataset), `a batch mapped function can take as input a batch of size N and return a batch of size M where M can be greater or less than N and can even be zero`.
However, when the returned batch has a size of zero (neither item in the batch fulfilled the condition), we get an `index out of bounds` error. I think that `arrow_writer.py` is [trying to infer the returned types using the first element returned](https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_writer.py#L100), but no elements were returned in this case.
For this error to happen, I'm returning a dictionary that contains empty lists for the keys I want to keep, see below. If I return an empty dictionary instead (no keys), then a different error eventually occurs.
## Steps to reproduce the bug
```python
def select_rows(examples):
# `key` is a column name that exists in the original dataset
# The following line simulates no matches found, so we return an empty batch
result = {'key': []}
return result
filtered_dataset = dataset.map(
select_rows,
remove_columns = dataset.column_names,
batched = True,
num_proc = 1,
desc = "Selecting rows with images that exist"
)
```
The code above immediately triggers the exception. If we use the following instead:
```python
def select_rows(examples):
# `key` is a column name that exists in the original dataset
result = {'key': []} # or defaultdict or whatever
# code to check for condition and append elements to result
# some_items_found will be set to True if there were any matching elements in the batch
return result if some_items_found else {}
```
Then it _seems_ to work, but it eventually fails with some sort of schema error. I believe it may happen when an empty batch is followed by a non-empty one, but haven't set up a test to verify it.
In my opinion, returning a dictionary with empty lists and valid column names should be accepted as a valid result with zero items.
## Expected results
The dataset would be filtered and only the matching fields would be returned.
## Actual results
An exception is encountered, as described. Using a workaround makes it fail further along the line.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.1.dev0
- Platform: Linux-5.4.0-53-generic-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
| 28 | Batched `map` not allowed to return 0 items
## Describe the bug
I'm trying to use `map` to filter a large dataset by selecting rows that match an expensive condition (files referenced by one of the columns need to exist in the filesystem, so we have to `stat` them). According to [the documentation](https://huggingface.co/docs/datasets/processing.html#augmenting-the-dataset), `a batch mapped function can take as input a batch of size N and return a batch of size M where M can be greater or less than N and can even be zero`.
However, when the returned batch has a size of zero (neither item in the batch fulfilled the condition), we get an `index out of bounds` error. I think that `arrow_writer.py` is [trying to infer the returned types using the first element returned](https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_writer.py#L100), but no elements were returned in this case.
For this error to happen, I'm returning a dictionary that contains empty lists for the keys I want to keep, see below. If I return an empty dictionary instead (no keys), then a different error eventually occurs.
## Steps to reproduce the bug
```python
def select_rows(examples):
# `key` is a column name that exists in the original dataset
# The following line simulates no matches found, so we return an empty batch
result = {'key': []}
return result
filtered_dataset = dataset.map(
select_rows,
remove_columns = dataset.column_names,
batched = True,
num_proc = 1,
desc = "Selecting rows with images that exist"
)
```
The code above immediately triggers the exception. If we use the following instead:
```python
def select_rows(examples):
# `key` is a column name that exists in the original dataset
result = {'key': []} # or defaultdict or whatever
# code to check for condition and append elements to result
# some_items_found will be set to True if there were any matching elements in the batch
return result if some_items_found else {}
```
Then it _seems_ to work, but it eventually fails with some sort of schema error. I believe it may happen when an empty batch is followed by a non-empty one, but haven't set up a test to verify it.
In my opinion, returning a dictionary with empty lists and valid column names should be accepted as a valid result with zero items.
## Expected results
The dataset would be filtered and only the matching fields would be returned.
## Actual results
An exception is encountered, as described. Using a workaround makes it fail further along the line.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.1.dev0
- Platform: Linux-5.4.0-53-generic-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
Sounds good! Do you want me to propose a PR? I'm quite busy right now, but if it's not too urgent I could take a look next week. | [
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https://github.com/huggingface/datasets/issues/2644 | Batched `map` not allowed to return 0 items | Sure if you're interested feel free to open a PR :)
You can also ping me anytime if you have questions or if I can help ! | ## Describe the bug
I'm trying to use `map` to filter a large dataset by selecting rows that match an expensive condition (files referenced by one of the columns need to exist in the filesystem, so we have to `stat` them). According to [the documentation](https://huggingface.co/docs/datasets/processing.html#augmenting-the-dataset), `a batch mapped function can take as input a batch of size N and return a batch of size M where M can be greater or less than N and can even be zero`.
However, when the returned batch has a size of zero (neither item in the batch fulfilled the condition), we get an `index out of bounds` error. I think that `arrow_writer.py` is [trying to infer the returned types using the first element returned](https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_writer.py#L100), but no elements were returned in this case.
For this error to happen, I'm returning a dictionary that contains empty lists for the keys I want to keep, see below. If I return an empty dictionary instead (no keys), then a different error eventually occurs.
## Steps to reproduce the bug
```python
def select_rows(examples):
# `key` is a column name that exists in the original dataset
# The following line simulates no matches found, so we return an empty batch
result = {'key': []}
return result
filtered_dataset = dataset.map(
select_rows,
remove_columns = dataset.column_names,
batched = True,
num_proc = 1,
desc = "Selecting rows with images that exist"
)
```
The code above immediately triggers the exception. If we use the following instead:
```python
def select_rows(examples):
# `key` is a column name that exists in the original dataset
result = {'key': []} # or defaultdict or whatever
# code to check for condition and append elements to result
# some_items_found will be set to True if there were any matching elements in the batch
return result if some_items_found else {}
```
Then it _seems_ to work, but it eventually fails with some sort of schema error. I believe it may happen when an empty batch is followed by a non-empty one, but haven't set up a test to verify it.
In my opinion, returning a dictionary with empty lists and valid column names should be accepted as a valid result with zero items.
## Expected results
The dataset would be filtered and only the matching fields would be returned.
## Actual results
An exception is encountered, as described. Using a workaround makes it fail further along the line.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.1.dev0
- Platform: Linux-5.4.0-53-generic-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
| 27 | Batched `map` not allowed to return 0 items
## Describe the bug
I'm trying to use `map` to filter a large dataset by selecting rows that match an expensive condition (files referenced by one of the columns need to exist in the filesystem, so we have to `stat` them). According to [the documentation](https://huggingface.co/docs/datasets/processing.html#augmenting-the-dataset), `a batch mapped function can take as input a batch of size N and return a batch of size M where M can be greater or less than N and can even be zero`.
However, when the returned batch has a size of zero (neither item in the batch fulfilled the condition), we get an `index out of bounds` error. I think that `arrow_writer.py` is [trying to infer the returned types using the first element returned](https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_writer.py#L100), but no elements were returned in this case.
For this error to happen, I'm returning a dictionary that contains empty lists for the keys I want to keep, see below. If I return an empty dictionary instead (no keys), then a different error eventually occurs.
## Steps to reproduce the bug
```python
def select_rows(examples):
# `key` is a column name that exists in the original dataset
# The following line simulates no matches found, so we return an empty batch
result = {'key': []}
return result
filtered_dataset = dataset.map(
select_rows,
remove_columns = dataset.column_names,
batched = True,
num_proc = 1,
desc = "Selecting rows with images that exist"
)
```
The code above immediately triggers the exception. If we use the following instead:
```python
def select_rows(examples):
# `key` is a column name that exists in the original dataset
result = {'key': []} # or defaultdict or whatever
# code to check for condition and append elements to result
# some_items_found will be set to True if there were any matching elements in the batch
return result if some_items_found else {}
```
Then it _seems_ to work, but it eventually fails with some sort of schema error. I believe it may happen when an empty batch is followed by a non-empty one, but haven't set up a test to verify it.
In my opinion, returning a dictionary with empty lists and valid column names should be accepted as a valid result with zero items.
## Expected results
The dataset would be filtered and only the matching fields would be returned.
## Actual results
An exception is encountered, as described. Using a workaround makes it fail further along the line.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.9.1.dev0
- Platform: Linux-5.4.0-53-generic-x86_64-with-glibc2.17
- Python version: 3.8.10
- PyArrow version: 4.0.1
Sure if you're interested feel free to open a PR :)
You can also ping me anytime if you have questions or if I can help ! | [
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