id
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3.26B
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int64 1
7.7k
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stringlengths 0
228k
⌀ | state
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stringlengths 46
51
| created_at
timestamp[s]date 2020-04-14 10:18:02
2025-07-23 08:04:53
| updated_at
timestamp[s]date 2020-04-27 16:04:17
2025-07-23 18:53:44
| closed_at
timestamp[s]date 2020-04-14 12:01:40
2025-07-23 16:44:42
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|---|---|---|---|---|---|---|---|---|---|---|---|---|
890,439,523
| 2,354
|
Document DatasetInfo attributes
|
**Is your feature request related to a problem? Please describe.**
As noted in PR #2255, the attributes of `DatasetInfo` are not documented in the [docs](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=datasetinfo#datasetinfo). It would be nice to do so :)
|
closed
|
https://github.com/huggingface/datasets/issues/2354
| 2021-05-12T20:01:29
| 2021-05-22T09:26:14
| 2021-05-22T09:26:14
|
{
"login": "lewtun",
"id": 26859204,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
}
] | false
|
[] |
890,296,262
| 2,353
|
Update README vallidation rules
|
This PR allows unexpected subsections under third-level headings. All except `Contributions`.
@lhoestq
|
closed
|
https://github.com/huggingface/datasets/pull/2353
| 2021-05-12T16:57:26
| 2021-05-14T08:56:06
| 2021-05-14T08:56:06
|
{
"login": "gchhablani",
"id": 29076344,
"type": "User"
}
|
[] | true
|
[] |
889,810,100
| 2,352
|
Set to_json default to JSON lines
|
With this PR, the method `Dataset.to_json`:
- is added to the docs
- defaults to JSON lines
|
closed
|
https://github.com/huggingface/datasets/pull/2352
| 2021-05-12T08:19:25
| 2021-05-21T09:01:14
| 2021-05-21T09:01:13
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
889,584,953
| 2,351
|
simpllify faiss index save
|
Fixes #2350
In some cases, Faiss GPU index objects do not have neither "device" nor "getDevice". Possibly this happens when some part of the index is computed on CPU.
In particular, this would happen with the index `OPQ16_128,IVF512,PQ32` (issue #2350). I did check it, but it is likely that `OPQ` or `PQ` transforms cause it.
I propose, instead of using the index object to get the device, to infer it form the `FaissIndex.device` field as it is done in `.add_vectors`. Here we assume that `.device` always corresponds to the index placement and it seems reasonable.
|
closed
|
https://github.com/huggingface/datasets/pull/2351
| 2021-05-12T03:54:10
| 2021-05-17T13:41:41
| 2021-05-17T13:41:41
|
{
"login": "Guitaricet",
"id": 2821124,
"type": "User"
}
|
[] | true
|
[] |
889,580,247
| 2,350
|
`FaissIndex.save` throws error on GPU
|
## Describe the bug
After training an index with a factory string `OPQ16_128,IVF512,PQ32` on GPU, `.save_faiss_index` throws this error.
```
File "index_wikipedia.py", line 119, in <module>
data["train"].save_faiss_index("text_emb", index_save_path)
File "/home/vlialin/miniconda3/envs/cat/lib/python3.8/site-packages/datasets/search.py", line 470, in save_faiss_index
index.save(file)
File "/home/vlialin/miniconda3/envs/cat/lib/python3.8/site-packages/datasets/search.py", line 334, in save
faiss.write_index(index, str(file))
File "/home/vlialin/miniconda3/envs/cat/lib/python3.8/site-packages/faiss/swigfaiss_avx2.py", line 5654, in write_index
return _swigfaiss.write_index(*args)
RuntimeError: Error in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) at /root/miniconda3/conda-bld/faiss-pkg_1613235005464/work/faiss/impl/index_write.cpp:453: don't know how to serialize this type of index
```
## Steps to reproduce the bug
Any dataset will do, I just selected a familiar one.
```python
import numpy as np
import datasets
INDEX_STR = "OPQ16_128,IVF512,PQ32"
INDEX_SAVE_PATH = "will_not_save.faiss"
data = datasets.load_dataset("Fraser/news-category-dataset", split=f"train[:10000]")
def encode(item):
return {"text_emb": np.random.randn(768).astype(np.float32)}
data = data.map(encode)
data.add_faiss_index(column="text_emb", string_factory=INDEX_STR, train_size=10_000, device=0)
data.save_faiss_index("text_emb", INDEX_SAVE_PATH)
```
## Expected results
Saving the index
## Actual results
Error in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) ... don't know how to serialize this type of index
## Environment info
- `datasets` version: 1.6.2
- Platform: Linux-4.15.0-142-generic-x86_64-with-glibc2.10
- Python version: 3.8.8
- PyTorch version (GPU?): 1.8.1+cu111 (True)
- Tensorflow version (GPU?): 2.2.0 (False)
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No
I will be proposing a fix in a couple of minutes
|
closed
|
https://github.com/huggingface/datasets/issues/2350
| 2021-05-12T03:41:56
| 2021-05-17T13:41:41
| 2021-05-17T13:41:41
|
{
"login": "Guitaricet",
"id": 2821124,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
888,586,018
| 2,349
|
Update task_ids for Ascent KB
|
This "other-other-knowledge-base" task is better suited for the dataset.
|
closed
|
https://github.com/huggingface/datasets/pull/2349
| 2021-05-11T20:44:33
| 2021-05-17T10:53:14
| 2021-05-17T10:48:34
|
{
"login": "phongnt570",
"id": 6749421,
"type": "User"
}
|
[] | true
|
[] |
887,927,737
| 2,348
|
Add tests for dataset cards
|
Adding tests for dataset cards
This PR will potentially remove the scripts being used for dataset tags and readme validation.
Additionally, this will allow testing dataset readmes by providing the name as follows:
```bash
pytest tests/test_dataset_cards.py::test_dataset_tags[fashion_mnist]
```
and
```bash
pytest tests/test_dataset_cards.py::test_readme_content[fashion_mnist]
```
or a combined test as:
```bash
pytest tests/test_dataset_cards.py::test_dataset_card[fashion_mnist]
```
@lhoestq
|
closed
|
https://github.com/huggingface/datasets/pull/2348
| 2021-05-11T17:14:27
| 2021-05-21T12:10:47
| 2021-05-21T12:10:47
|
{
"login": "gchhablani",
"id": 29076344,
"type": "User"
}
|
[] | true
|
[] |
887,404,868
| 2,347
|
Add an API to access the language and pretty name of a dataset
|
It would be super nice to have an API to get some metadata of the dataset from the name and args passed to `load_dataset`. This way we could programmatically infer the language and the name of a dataset when creating model cards automatically in the Transformers examples scripts.
|
closed
|
https://github.com/huggingface/datasets/issues/2347
| 2021-05-11T14:10:08
| 2022-10-05T17:16:54
| 2022-10-05T17:16:53
|
{
"login": "sgugger",
"id": 35901082,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
}
] | false
|
[] |
886,632,114
| 2,346
|
Add Qasper Dataset
|
[Question Answering on Scientific Research Papers](https://allenai.org/project/qasper/home)
Doing NLP on NLP papers to do NLP ♻️ I had to add it~
- [x] Add README (just gotta fill out some more )
- [x] Dataloader code
- [x] Make dummy dataset
- [x] generate dataset infos
- [x] Tests
|
closed
|
https://github.com/huggingface/datasets/pull/2346
| 2021-05-11T09:25:44
| 2021-05-18T12:28:28
| 2021-05-18T12:28:28
|
{
"login": "cceyda",
"id": 15624271,
"type": "User"
}
|
[] | true
|
[] |
886,586,872
| 2,345
|
[Question] How to move and reuse preprocessed dataset?
|
Hi, I am training a gpt-2 from scratch using run_clm.py.
I want to move and reuse the preprocessed dataset (It take 2 hour to preprocess),
I tried to :
copy path_to_cache_dir/datasets to new_cache_dir/datasets
set export HF_DATASETS_CACHE="new_cache_dir/"
but the program still re-preprocess the whole dataset without loading cache.
I also tried to torch.save(lm_datasets, fw), but the saved file is only 14M.
What is the proper way to do this?
|
closed
|
https://github.com/huggingface/datasets/issues/2345
| 2021-05-11T09:09:17
| 2021-06-11T04:39:11
| 2021-06-11T04:39:11
|
{
"login": "AtmaHou",
"id": 15045402,
"type": "User"
}
|
[] | false
|
[] |
885,331,505
| 2,344
|
Is there a way to join multiple datasets in one?
|
**Is your feature request related to a problem? Please describe.**
I need to join 2 datasets, one that is in the hub and another I've created from my files. Is there an easy way to join these 2?
**Describe the solution you'd like**
Id like to join them with a merge or join method, just like pandas dataframes.
**Additional context**
If you want to extend an existing dataset with more data, for example for training a language model, you need that functionality. I've not found it in the documentation.
|
open
|
https://github.com/huggingface/datasets/issues/2344
| 2021-05-10T23:16:10
| 2022-10-05T17:27:05
| null |
{
"login": "avacaondata",
"id": 35173563,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
}
] | false
|
[] |
883,208,539
| 2,343
|
Columns are removed before or after map function applied?
|
## Describe the bug
According to the documentation when applying map function the [remove_columns ](https://huggingface.co/docs/datasets/processing.html#removing-columns) will be removed after they are passed to the function, but in the [source code](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map) it's documented that they are removed before applying function. I thinks the source code doc is more accurate, right?
|
open
|
https://github.com/huggingface/datasets/issues/2343
| 2021-05-10T02:36:20
| 2022-10-24T11:31:55
| null |
{
"login": "taghizad3h",
"id": 8199406,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
882,981,420
| 2,342
|
Docs - CER above 1
|
CER can actually be greater than 1.
|
closed
|
https://github.com/huggingface/datasets/pull/2342
| 2021-05-09T23:41:00
| 2021-05-10T13:34:00
| 2021-05-10T13:34:00
|
{
"login": "borisdayma",
"id": 715491,
"type": "User"
}
|
[] | true
|
[] |
882,370,933
| 2,341
|
Added the Ascent KB
|
Added the Ascent Commonsense KB of 8.9M assertions.
- Paper: [Advanced Semantics for Commonsense Knowledge Extraction (WWW'21)](https://arxiv.org/abs/2011.00905)
- Website: https://ascent.mpi-inf.mpg.de/
(I am the author of the dataset)
|
closed
|
https://github.com/huggingface/datasets/pull/2341
| 2021-05-09T14:17:39
| 2021-05-11T09:16:59
| 2021-05-11T09:16:59
|
{
"login": "phongnt570",
"id": 6749421,
"type": "User"
}
|
[] | true
|
[] |
882,370,824
| 2,340
|
More consistent copy logic
|
Use `info.copy()` instead of `copy.deepcopy(info)`.
`Features.copy` now creates a deep copy.
|
closed
|
https://github.com/huggingface/datasets/pull/2340
| 2021-05-09T14:17:33
| 2021-05-11T08:58:33
| 2021-05-11T08:58:33
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
882,046,077
| 2,338
|
fixed download link for web_science
|
Fixes #2337. Should work with:
`dataset = load_dataset("web_of_science", "WOS11967", ignore_verifications=True)`
|
closed
|
https://github.com/huggingface/datasets/pull/2338
| 2021-05-09T09:12:20
| 2021-05-10T13:35:53
| 2021-05-10T13:35:53
|
{
"login": "bhavitvyamalik",
"id": 19718818,
"type": "User"
}
|
[] | true
|
[] |
881,610,567
| 2,337
|
NonMatchingChecksumError for web_of_science dataset
|
NonMatchingChecksumError when trying to download the web_of_science dataset.
>NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://data.mendeley.com/datasets/9rw3vkcfy4/6/files/c9ea673d-5542-44c0-ab7b-f1311f7d61df/WebOfScience.zip?dl=1']
Setting `ignore_verfications=True` results in OSError.
>OSError: Cannot find data file.
Original error:
[Errno 20] Not a directory: '/root/.cache/huggingface/datasets/downloads/37ab2c42f50d553c1d0ea432baca3e9e11fedea4aeec63a81e6b7e25dd10d4e7/WOS5736/X.txt'
```python
dataset = load_dataset('web_of_science', 'WOS5736')
```
There are 3 data instances and they all don't work. 'WOS5736', 'WOS11967', 'WOS46985'
datasets 1.6.2
python 3.7.10
Ubuntu 18.04.5 LTS
|
closed
|
https://github.com/huggingface/datasets/issues/2337
| 2021-05-09T02:02:02
| 2021-05-10T13:35:53
| 2021-05-10T13:35:53
|
{
"login": "nbroad1881",
"id": 24982805,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
881,298,783
| 2,336
|
Fix overflow issue in interpolation search
|
Fixes #2335
More info about this error can be found [here](https://stackoverflow.com/questions/53239890/why-do-i-keep-getting-this-error-runtimewarning-overflow-encountered-in-int-sc/53240100).
|
closed
|
https://github.com/huggingface/datasets/pull/2336
| 2021-05-08T20:51:36
| 2021-05-10T13:29:07
| 2021-05-10T13:26:12
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
881,291,887
| 2,335
|
Index error in Dataset.map
|
The following code, if executed on master, raises an IndexError (due to overflow):
```python
>>> from datasets import *
>>> d = load_dataset("bookcorpus", split="train")
Reusing dataset bookcorpus (C:\Users\Mario\.cache\huggingface\datasets\bookcorpus\plain_text\1.0.0\44662c4a114441c35200992bea923b170e6f13f2f0beb7c14e43759cec498700)
2021-05-08 21:23:46.859818: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library cudart64_101.dll
>>> d.map(lambda ex: ex)
0%|▎ | 289430/74004228 [00:13<58:41, 20935.33ex/s]c:\users\mario\desktop\projects\datasets-1\src\datasets\table.py:84: RuntimeWarning: overflow encountered in int_scalars
k = i + ((j - i) * (x - arr[i]) // (arr[j] - arr[i]))
0%|▎ | 290162/74004228 [00:13<59:11, 20757.23ex/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "c:\users\mario\desktop\projects\datasets-1\src\datasets\arrow_dataset.py", line 1498, in map
new_fingerprint=new_fingerprint,
File "c:\users\mario\desktop\projects\datasets-1\src\datasets\arrow_dataset.py", line 174, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "c:\users\mario\desktop\projects\datasets-1\src\datasets\fingerprint.py", line 340, in wrapper
out = func(self, *args, **kwargs)
File "c:\users\mario\desktop\projects\datasets-1\src\datasets\arrow_dataset.py", line 1799, in _map_single
for i, example in enumerate(pbar):
File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\site-packages\tqdm\std.py", line 1133, in __iter__
for obj in iterable:
File "c:\users\mario\desktop\projects\datasets-1\src\datasets\arrow_dataset.py", line 1145, in __iter__
format_kwargs=format_kwargs,
File "c:\users\mario\desktop\projects\datasets-1\src\datasets\arrow_dataset.py", line 1337, in _getitem
pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
File "c:\users\mario\desktop\projects\datasets-1\src\datasets\formatting\formatting.py", line 368, in query_table
pa_subtable = _query_table(table, key)
File "c:\users\mario\desktop\projects\datasets-1\src\datasets\formatting\formatting.py", line 79, in _query_table
return table.fast_slice(key % table.num_rows, 1)
File "c:\users\mario\desktop\projects\datasets-1\src\datasets\table.py", line 128, in fast_slice
i = _interpolation_search(self._offsets, offset)
File "c:\users\mario\desktop\projects\datasets-1\src\datasets\table.py", line 91, in _interpolation_search
raise IndexError(f"Invalid query '{x}' for size {arr[-1] if len(arr) else 'none'}.")
IndexError: Invalid query '290162' for size 74004228.
```
Tested on Windows, can run on Linux if needed.
EDIT:
It seems like for this to happen, the default NumPy dtype has to be np.int32.
|
closed
|
https://github.com/huggingface/datasets/issues/2335
| 2021-05-08T20:44:57
| 2021-05-10T13:26:12
| 2021-05-10T13:26:12
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
879,810,107
| 2,334
|
Updating the DART file checksums in GEM
|
The DART files were just updated on the source GitHub
https://github.com/Yale-LILY/dart/commit/34b3c872da4811523e334f1631e54ca8105dffab
|
closed
|
https://github.com/huggingface/datasets/pull/2334
| 2021-05-07T21:53:44
| 2021-05-07T22:18:10
| 2021-05-07T22:18:10
|
{
"login": "yjernite",
"id": 10469459,
"type": "User"
}
|
[] | true
|
[] |
879,214,067
| 2,333
|
Fix duplicate keys
|
As noticed in https://github.com/huggingface/datasets/pull/2245, many datasets yield duplicate keys.
Most of the time it was because the counter used for ids were reset at each new data file.
|
closed
|
https://github.com/huggingface/datasets/pull/2333
| 2021-05-07T15:28:08
| 2021-05-08T21:47:31
| 2021-05-07T15:57:08
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
879,041,608
| 2,332
|
Add note about indices mapping in save_to_disk docstring
|
closed
|
https://github.com/huggingface/datasets/pull/2332
| 2021-05-07T13:49:42
| 2021-05-07T17:20:48
| 2021-05-07T17:20:48
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
|
879,031,427
| 2,331
|
Add Topical-Chat
|
## Adding a Dataset
- **Name:** Topical-Chat
- **Description:** a knowledge-grounded human-human conversation dataset where the underlying knowledge spans 8 broad topics and conversation partners don’t have explicitly defined roles
- **Paper:** https://www.isca-speech.org/archive/Interspeech_2019/pdfs/3079.pdf
- **Data:** https://github.com/alexa/Topical-Chat
- **Motivation:** Good quality, knowledge-grounded dataset that spans a broad range of topics
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
|
open
|
https://github.com/huggingface/datasets/issues/2331
| 2021-05-07T13:43:59
| 2021-05-07T13:43:59
| null |
{
"login": "ktangri",
"id": 22266659,
"type": "User"
}
|
[
{
"name": "dataset request",
"color": "e99695"
}
] | false
|
[] |
878,490,927
| 2,330
|
Allow passing `desc` to `tqdm` in `Dataset.map()`
|
It's normal to have many `map()` calls, and some of them can take a few minutes,
it would be nice to have a description on the progress bar.
Alternative solution:
Print the description before/after the `map()` call.
|
closed
|
https://github.com/huggingface/datasets/issues/2330
| 2021-05-07T05:52:54
| 2021-05-26T14:59:21
| 2021-05-26T14:59:21
|
{
"login": "changjonathanc",
"id": 31893406,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
},
{
"name": "good first issue",
"color": "7057ff"
}
] | false
|
[] |
877,924,198
| 2,329
|
Add cache dir for in-memory datasets
|
Adds the cache dir attribute to DatasetInfo as suggested by @lhoestq.
Should fix #2322
|
closed
|
https://github.com/huggingface/datasets/pull/2329
| 2021-05-06T19:35:32
| 2021-06-08T19:46:48
| 2021-06-08T19:06:46
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
877,673,896
| 2,328
|
Add Matthews/Pearson/Spearman correlation metrics
|
Added three metrics:
- The Matthews correlation coefficient (from sklearn)
- The Pearson correlation coefficient (from scipy)
- The Spearman correlation coefficient (from scipy)
cc @sgugger
|
closed
|
https://github.com/huggingface/datasets/pull/2328
| 2021-05-06T16:09:27
| 2021-05-06T16:58:10
| 2021-05-06T16:58:10
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
877,565,831
| 2,327
|
A syntax error in example
|
Sorry to report with an image, I can't find the template source code of this snippet.
|
closed
|
https://github.com/huggingface/datasets/issues/2327
| 2021-05-06T14:34:44
| 2021-05-20T03:04:19
| 2021-05-20T03:04:19
|
{
"login": "mymusise",
"id": 6883957,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
876,829,254
| 2,326
|
Enable auto-download for PAN-X / Wikiann domain in XTREME
|
This PR replaces the manual download of the `PAN-X.lang` domains with an auto-download from a Dropbox link provided by the Wikiann author. We also add the relevant dummy data for these domains.
While re-generating `dataset_infos.json` I ran into a `KeyError` in the `udpos.Arabic` domain so have included a fix for this as well.
|
closed
|
https://github.com/huggingface/datasets/pull/2326
| 2021-05-05T20:58:38
| 2021-05-07T08:41:10
| 2021-05-07T08:41:10
|
{
"login": "lewtun",
"id": 26859204,
"type": "User"
}
|
[] | true
|
[] |
876,653,121
| 2,325
|
Added the HLGD dataset
|
Added the Headline Grouping Dataset (HLGD), from the NAACL2021 paper: News Headline Grouping as a Challenging NLU Task
Dataset Link: https://github.com/tingofurro/headline_grouping
Paper link: https://people.eecs.berkeley.edu/~phillab/pdfs/NAACL2021_HLG.pdf
|
closed
|
https://github.com/huggingface/datasets/pull/2325
| 2021-05-05T16:53:29
| 2021-05-12T14:55:13
| 2021-05-12T14:16:38
|
{
"login": "tingofurro",
"id": 2609265,
"type": "User"
}
|
[] | true
|
[] |
876,602,064
| 2,324
|
Create Audio feature
|
Create `Audio` feature to handle raw audio files.
Some decisions to be further discussed:
- I have chosen `soundfile` as the audio library; another interesting library is `librosa`, but this requires `soundfile` (see [here](https://github.com/librosa/librosa/blob/main/setup.cfg#L53)). If we require some more advanced functionalities, we could eventually switch the library.
- I have implemented the audio feature as an extra: `pip install datasets[audio]`. For the moment, the typical datasets user uses only text datasets, and there is no need for them for additional package requirements for audio/image if they do not need them.
- For tests, I require audio dependencies (so that all audio functionalities are checked with our CI test suite); I exclude Linux platforms, which require an additional library to be installed with the distribution package manager
- I also require `pytest-datadir`, which allow to have (audio) data files for tests
- The audio data contain: array and sample_rate.
- The array is reshaped as 1D array (expected input for `Wav2Vec2`).
Note that to install `soundfile` on Linux, you need to install `libsndfile` using your distribution’s package manager, for example `sudo apt-get install libsndfile1`.
## Requirements Specification
- Access example with audio loading and resampling:
```python
ds[0]["audio"]
```
- Map with audio loading & resampling:
```python
def preprocess(batch):
batch["input_values"] = processor(batch["audio"]).input_values
return batch
ds = ds.map(preprocess)
```
- Map without audio loading and resampling:
```python
def preprocess(batch):
batch["labels"] = processor(batch["target_text"]).input_values
return batch
ds = ds.map(preprocess)
```
- Additional requirement specification (see https://github.com/huggingface/datasets/pull/2324#pullrequestreview-768864998): Cast audio column to change sampling sate:
```python
ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
```
|
closed
|
https://github.com/huggingface/datasets/pull/2324
| 2021-05-05T15:55:22
| 2021-10-13T10:26:33
| 2021-10-13T10:26:33
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
876,438,507
| 2,323
|
load_dataset("timit_asr") gives back duplicates of just one sample text
|
## Describe the bug
When you look up on key ["train"] and then ['text'], you get back a list with just one sentence duplicated 4620 times. Namely, the sentence "Would such an act of refusal be useful?". Similarly when you look up ['test'] and then ['text'], the list is one sentence repeated "The bungalow was pleasantly situated near the shore." 1680 times.
I tried to work around the issue by downgrading to datasets version 1.3.0, inspired by [this post](https://www.gitmemory.com/issue/huggingface/datasets/2052/798904836) and removing the entire huggingface directory from ~/.cache, but I still get the same issue.
## Steps to reproduce the bug
```python
from datasets import load_dataset
timit = load_dataset("timit_asr")
print(timit['train']['text'])
print(timit['test']['text'])
```
## Expected Result
Rows of diverse text, like how it is shown in the [wav2vec2.0 tutorial](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_tuning_Wav2Vec2_for_English_ASR.ipynb)
<img width="485" alt="Screen Shot 2021-05-05 at 9 09 57 AM" src="https://user-images.githubusercontent.com/33647474/117146094-d9b77f00-ad81-11eb-8306-f281850c127a.png">
## Actual results
Rows of repeated text.
<img width="319" alt="Screen Shot 2021-05-05 at 9 11 53 AM" src="https://user-images.githubusercontent.com/33647474/117146231-f8b61100-ad81-11eb-834a-fc10410b0c9c.png">
## Versions
- Datasets: 1.3.0
- Python: 3.9.1
- Platform: macOS-11.2.1-x86_64-i386-64bit}
|
closed
|
https://github.com/huggingface/datasets/issues/2323
| 2021-05-05T13:14:48
| 2021-05-07T10:32:30
| 2021-05-07T10:32:30
|
{
"login": "ekeleshian",
"id": 33647474,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
876,383,853
| 2,322
|
Calls to map are not cached.
|
## Describe the bug
Somehow caching does not work for me anymore. Am I doing something wrong, or is there anything that I missed?
## Steps to reproduce the bug
```python
import datasets
datasets.set_caching_enabled(True)
sst = datasets.load_dataset("sst")
def foo(samples, i):
print("executed", i[:10])
return samples
# first call
x = sst.map(foo, batched=True, with_indices=True, num_proc=2)
print('\n'*3, "#" * 30, '\n'*3)
# second call
y = sst.map(foo, batched=True, with_indices=True, num_proc=2)
# print version
import sys
import platform
print(f"""
- Datasets: {datasets.__version__}
- Python: {sys.version}
- Platform: {platform.platform()}
""")
```
## Actual results
This code prints the following output for me:
```bash
No config specified, defaulting to: sst/default
Reusing dataset sst (/home/johannes/.cache/huggingface/datasets/sst/default/1.0.0/b8a7889ef01c5d3ae8c379b84cc4080f8aad3ac2bc538701cbe0ac6416fb76ff)
#0: 0%| | 0/5 [00:00<?, ?ba/s]
#1: 0%| | 0/5 [00:00<?, ?ba/s]
executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
executed [4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281]
executed [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]
executed [5272, 5273, 5274, 5275, 5276, 5277, 5278, 5279, 5280, 5281]
executed [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]
executed [6272, 6273, 6274, 6275, 6276, 6277, 6278, 6279, 6280, 6281]
executed [3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009]
executed [7272, 7273, 7274, 7275, 7276, 7277, 7278, 7279, 7280, 7281]
executed [4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009]
#0: 100%|██████████| 5/5 [00:00<00:00, 59.85ba/s]
executed [8272, 8273, 8274, 8275, 8276, 8277, 8278, 8279, 8280, 8281]
#1: 100%|██████████| 5/5 [00:00<00:00, 60.85ba/s]
#0: 0%| | 0/1 [00:00<?, ?ba/s]
#1: 0%| | 0/1 [00:00<?, ?ba/s]executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
#0: 100%|██████████| 1/1 [00:00<00:00, 69.32ba/s]
executed [551, 552, 553, 554, 555, 556, 557, 558, 559, 560]
#1: 100%|██████████| 1/1 [00:00<00:00, 70.93ba/s]
#0: 0%| | 0/2 [00:00<?, ?ba/s]
#1: 0%| | 0/2 [00:00<?, ?ba/s]executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
executed [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]
#0: 100%|██████████| 2/2 [00:00<00:00, 63.25ba/s]
executed [1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114]
executed [2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114]
#1: 100%|██████████| 2/2 [00:00<00:00, 57.69ba/s]
##############################
#0: 0%| | 0/5 [00:00<?, ?ba/s]
#1: 0%| | 0/5 [00:00<?, ?ba/s]
executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
executed [4272, 4273, 4274, 4275, 4276, 4277, 4278, 4279, 4280, 4281]
executed [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]
executed [5272, 5273, 5274, 5275, 5276, 5277, 5278, 5279, 5280, 5281]
executed [2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009]
executed [6272, 6273, 6274, 6275, 6276, 6277, 6278, 6279, 6280, 6281]
executed [3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009]
executed [4000, 4001, 4002, 4003, 4004, 4005, 4006, 4007, 4008, 4009]
#0: 100%|██████████| 5/5 [00:00<00:00, 58.10ba/s]
executed [7272, 7273, 7274, 7275, 7276, 7277, 7278, 7279, 7280, 7281]
executed [8272, 8273, 8274, 8275, 8276, 8277, 8278, 8279, 8280, 8281]
#1: 100%|██████████| 5/5 [00:00<00:00, 57.19ba/s]
#0: 0%| | 0/1 [00:00<?, ?ba/s]
#1: 0%| | 0/1 [00:00<?, ?ba/s]
executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
#0: 100%|██████████| 1/1 [00:00<00:00, 60.10ba/s]
executed [551, 552, 553, 554, 555, 556, 557, 558, 559, 560]
#1: 100%|██████████| 1/1 [00:00<00:00, 53.82ba/s]
#0: 0%| | 0/2 [00:00<?, ?ba/s]
#1: 0%| | 0/2 [00:00<?, ?ba/s]
executed [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
executed [1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009]
executed [1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114]
#0: 100%|██████████| 2/2 [00:00<00:00, 72.76ba/s]
executed [2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114]
#1: 100%|██████████| 2/2 [00:00<00:00, 71.55ba/s]
- Datasets: 1.6.1
- Python: 3.8.3 (default, May 19 2020, 18:47:26)
[GCC 7.3.0]
- Platform: Linux-5.4.0-72-generic-x86_64-with-glibc2.10
```
## Expected results
Caching should work.
|
closed
|
https://github.com/huggingface/datasets/issues/2322
| 2021-05-05T12:11:27
| 2021-06-08T19:10:02
| 2021-06-08T19:08:21
|
{
"login": "villmow",
"id": 2743060,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
876,304,364
| 2,321
|
Set encoding in OSCAR dataset
|
Set explicit `utf-8` encoding in OSCAR dataset, to avoid using the system default `cp1252` on Windows platforms.
Fix #2319.
|
closed
|
https://github.com/huggingface/datasets/pull/2321
| 2021-05-05T10:27:03
| 2021-05-05T10:50:55
| 2021-05-05T10:50:55
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
876,257,026
| 2,320
|
Set default name in init_dynamic_modules
|
Set default value for the name of dynamic modules.
Close #2318.
|
closed
|
https://github.com/huggingface/datasets/pull/2320
| 2021-05-05T09:30:03
| 2021-05-06T07:57:54
| 2021-05-06T07:57:54
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
876,251,376
| 2,319
|
UnicodeDecodeError for OSCAR (Afrikaans)
|
## Describe the bug
When loading the [OSCAR dataset](https://huggingface.co/datasets/oscar) (specifically `unshuffled_deduplicated_af`), I encounter a `UnicodeDecodeError`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("oscar", "unshuffled_deduplicated_af")
```
## Expected results
Anything but an error, really.
## Actual results
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset("oscar", "unshuffled_deduplicated_af")
Downloading: 14.7kB [00:00, 4.91MB/s]
Downloading: 3.07MB [00:00, 32.6MB/s]
Downloading and preparing dataset oscar/unshuffled_deduplicated_af (download: 62.93 MiB, generated: 163.38 MiB, post-processed: Unknown size, total: 226.32 MiB) to C:\Users\sgraaf\.cache\huggingface\datasets\oscar\unshuffled_deduplicated_af\1.0.0\bd4f96df5b4512007ef9fd17bbc1ecde459fa53d2fc0049cf99392ba2efcc464...
Downloading: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 81.0/81.0 [00:00<00:00, 40.5kB/s]
Downloading: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 66.0M/66.0M [00:18<00:00, 3.50MB/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\sgraaf\AppData\Local\Programs\Python\Python39\lib\site-packages\datasets\load.py", line 745, in load_dataset
builder_instance.download_and_prepare(
File "C:\Users\sgraaf\AppData\Local\Programs\Python\Python39\lib\site-packages\datasets\builder.py", line 574, in download_and_prepare
self._download_and_prepare(
File "C:\Users\sgraaf\AppData\Local\Programs\Python\Python39\lib\site-packages\datasets\builder.py", line 652, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "C:\Users\sgraaf\AppData\Local\Programs\Python\Python39\lib\site-packages\datasets\builder.py", line 979, in _prepare_split
for key, record in utils.tqdm(
File "C:\Users\sgraaf\AppData\Local\Programs\Python\Python39\lib\site-packages\tqdm\std.py", line 1133, in __iter__
for obj in iterable:
File "C:\Users\sgraaf\.cache\huggingface\modules\datasets_modules\datasets\oscar\bd4f96df5b4512007ef9fd17bbc1ecde459fa53d2fc0049cf99392ba2efcc464\oscar.py", line 359, in _generate_examples
for line in f:
File "C:\Users\sgraaf\AppData\Local\Programs\Python\Python39\lib\encodings\cp1252.py", line 23, in decode
return codecs.charmap_decode(input,self.errors,decoding_table)[0]
UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 7454: character maps to <undefined>
```
## Versions
Paste the output of the following code:
```python
import datasets
import sys
import platform
print(f"""
- Datasets: {datasets.__version__}
- Python: {sys.version}
- Platform: {platform.platform()}
""")
```
- Datasets: 1.6.2
- Python: 3.9.4 (tags/v3.9.4:1f2e308, Apr 6 2021, 13:40:21) [MSC v.1928 64 bit (AMD64)]
- Platform: Windows-10-10.0.19041-SP0
|
closed
|
https://github.com/huggingface/datasets/issues/2319
| 2021-05-05T09:22:52
| 2021-05-05T10:57:31
| 2021-05-05T10:50:55
|
{
"login": "sgraaf",
"id": 8904453,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
876,212,460
| 2,318
|
[api request] API to obtain "dataset_module" dynamic path?
|
**Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is.
This is an awesome library.
It seems like the dynamic module path in this library has broken some of hyperparameter tuning functionality: https://discuss.huggingface.co/t/using-hyperparameter-search-in-trainer/785/34
This is because Ray will spawn new processes, and each process will load modules by path. However, we need to explicitly inform Ray to load the right modules, or else it will error upon import.
I'd like an API to obtain the dynamic paths. This will allow us to support this functionality in this awesome library while being future proof.
**Describe the solution you'd like**
A clear and concise description of what you want to happen.
`datasets.get_dynamic_paths -> List[str]` will be sufficient for my use case.
By offering this API, we will be able to address the following issues (by patching the ray integration sufficiently):
https://github.com/huggingface/blog/issues/106
https://github.com/huggingface/transformers/issues/11565
https://discuss.huggingface.co/t/using-hyperparameter-search-in-trainer/785/34
https://discuss.huggingface.co/t/using-hyperparameter-search-in-trainer/785/35
|
closed
|
https://github.com/huggingface/datasets/issues/2318
| 2021-05-05T08:40:48
| 2021-05-06T08:45:45
| 2021-05-06T07:57:54
|
{
"login": "richardliaw",
"id": 4529381,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
}
] | false
|
[] |
875,767,318
| 2,317
|
Fix incorrect version specification for the pyarrow package
|
This PR addresses the bug in the pyarrow version specification, which is detailed in #2316 .
Simply, I put a comma between the version bounds.
Fix #2316.
|
closed
|
https://github.com/huggingface/datasets/pull/2317
| 2021-05-04T19:30:20
| 2021-05-05T10:09:16
| 2021-05-05T09:21:58
|
{
"login": "cemilcengiz",
"id": 32267027,
"type": "User"
}
|
[] | true
|
[] |
875,756,353
| 2,316
|
Incorrect version specification for pyarrow
|
## Describe the bug
The pyarrow dependency is incorrectly specified in setup.py file, in [this line](https://github.com/huggingface/datasets/blob/3a3e5a4da20bfcd75f8b6a6869b240af8feccc12/setup.py#L77).
Also as a snippet:
```python
"pyarrow>=1.0.0<4.0.0",
```
## Steps to reproduce the bug
```bash
pip install "pyarrow>=1.0.0<4.0.0"
```
## Expected results
It is expected to get a pyarrow version between 1.0.0 (inclusive) and 4.0.0 (exclusive).
## Actual results
pip ignores the specified versions since there is a missing comma between the lower and upper limits. Therefore, pip installs the latest pyarrow version from PYPI, which is 4.0.0.
This is especially problematic since "conda env export" fails due to incorrect version specification. Here is the conda error as well:
```bash
conda env export
InvalidVersionSpec: Invalid version '1.0.0<4.0.0': invalid character(s)
```
## Fix suggestion
Put a comma between the version limits which means replacing the line in setup.py file with the following:
```python
"pyarrow>=1.0.0,<4.0.0",
```
## Versions
Paste the output of the following code:
```python
- Datasets: 1.6.2
- Python: 3.7.10 (default, Feb 26 2021, 18:47:35)
[GCC 7.3.0]
- Platform: Linux-5.4.0-42-generic-x86_64-with-debian-buster-sid
```
|
closed
|
https://github.com/huggingface/datasets/issues/2316
| 2021-05-04T19:15:11
| 2021-05-05T10:10:03
| 2021-05-05T10:10:03
|
{
"login": "cemilcengiz",
"id": 32267027,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
875,742,200
| 2,315
|
Datasets cli improvements
|
This PR:
* replaces the code from the `bug_report.md` that was used to get relevant system info with a dedicated command (a more elegant approach than copy-pasting the code IMO)
* removes the `download` command (copied from the transformers repo?)
* adds missing help messages to the cli commands
|
closed
|
https://github.com/huggingface/datasets/pull/2315
| 2021-05-04T18:55:11
| 2021-05-10T16:36:51
| 2021-05-10T16:36:50
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
875,729,271
| 2,314
|
Minor refactor prepare_module
|
Start to refactor `prepare_module` to try to decouple functionality.
This PR does:
- extract function `_initialize_dynamic_modules_namespace_package`
- extract function `_find_module_in_github_or_s3`
- some renaming of variables
- use of f-strings
|
closed
|
https://github.com/huggingface/datasets/pull/2314
| 2021-05-04T18:37:26
| 2021-10-13T09:07:34
| 2021-10-13T09:07:34
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
875,475,367
| 2,313
|
Remove unused head_hf_s3 function
|
Currently, the function `head_hf_s3` is not used:
- neither its returned result is used
- nor it raises any exception, as exceptions are catched and returned (not raised)
This PR removes it.
|
closed
|
https://github.com/huggingface/datasets/pull/2313
| 2021-05-04T13:42:06
| 2021-05-07T09:31:42
| 2021-05-07T09:31:42
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
875,435,726
| 2,312
|
Add rename_columnS method
|
Cherry-picked from #2255
|
closed
|
https://github.com/huggingface/datasets/pull/2312
| 2021-05-04T12:57:53
| 2021-05-04T13:43:13
| 2021-05-04T13:43:12
|
{
"login": "SBrandeis",
"id": 33657802,
"type": "User"
}
|
[] | true
|
[] |
875,262,208
| 2,311
|
Add SLR52, SLR53 and SLR54 to OpenSLR
|
Add large speech datasets for Sinhala, Bengali and Nepali.
|
closed
|
https://github.com/huggingface/datasets/pull/2311
| 2021-05-04T09:08:03
| 2021-05-07T09:50:55
| 2021-05-07T09:50:55
|
{
"login": "cahya-wirawan",
"id": 7669893,
"type": "User"
}
|
[] | true
|
[] |
875,096,051
| 2,310
|
Update README.md
|
Provides description of data instances and dataset features
|
closed
|
https://github.com/huggingface/datasets/pull/2310
| 2021-05-04T04:38:01
| 2022-07-06T15:19:58
| 2022-07-06T15:19:58
|
{
"login": "cryoff",
"id": 15029054,
"type": "User"
}
|
[] | true
|
[] |
874,644,990
| 2,309
|
Fix conda release
|
There were a few issues with conda releases (they've been failing for a while now).
To fix this I had to:
- add the --single-version-externally-managed tag to the build stage (suggestion from [here](https://stackoverflow.com/a/64825075))
- set the python version of the conda build stage to 3.8 since 3.9 isn't supported
- sync the evrsion requirement of `huggingface_hub`
With these changes I'm working on uploading all missing versions until 1.6.2 to conda
EDIT: I managed to build and upload all missing versions until 1.6.2 to conda :)
|
closed
|
https://github.com/huggingface/datasets/pull/2309
| 2021-05-03T14:52:59
| 2021-05-03T16:01:17
| 2021-05-03T16:01:17
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
873,961,435
| 2,302
|
Add SubjQA dataset
|
Hello datasetters 🙂!
Here's an interesting dataset about extractive question-answering on _subjective_ product / restaurant reviews. It's quite challenging for models fine-tuned on SQuAD and provides a nice example of domain adaptation (i.e. fine-tuning a SQuAD model on this domain gives better performance).
I found a bug in the start/end indices that I've proposed a fix for here: https://github.com/megagonlabs/SubjQA/pull/2
Unfortunately, the dataset creators are unresponsive, so for now I am using my fork as the source. Will update the URL if/when the creators respond.
|
closed
|
https://github.com/huggingface/datasets/pull/2302
| 2021-05-02T14:51:20
| 2021-05-10T09:21:19
| 2021-05-10T09:21:19
|
{
"login": "lewtun",
"id": 26859204,
"type": "User"
}
|
[] | true
|
[] |
873,941,266
| 2,301
|
Unable to setup dev env on Windows
|
Hi
I tried installing the `".[dev]"` version on Windows 10 after cloning.
Here is the error I'm facing:
```bat
(env) C:\testing\datasets>pip install -e ".[dev]"
Obtaining file:///C:/testing/datasets
Requirement already satisfied: numpy>=1.17 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.19.5)
Collecting pyarrow>=0.17.1
Using cached pyarrow-4.0.0-cp37-cp37m-win_amd64.whl (13.3 MB)
Requirement already satisfied: dill in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (0.3.1.1)
Collecting pandas
Using cached pandas-1.2.4-cp37-cp37m-win_amd64.whl (9.1 MB)
Requirement already satisfied: requests>=2.19.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (2.25.1)
Requirement already satisfied: tqdm<4.50.0,>=4.27 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (4.49.0)
Requirement already satisfied: xxhash in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (2.0.2)
Collecting multiprocess
Using cached multiprocess-0.70.11.1-py37-none-any.whl (108 kB)
Requirement already satisfied: fsspec in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (2021.4.0)
Collecting huggingface_hub<0.1.0
Using cached huggingface_hub-0.0.8-py3-none-any.whl (34 kB)
Requirement already satisfied: importlib_metadata in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (4.0.1)
Requirement already satisfied: absl-py in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (0.12.0)
Requirement already satisfied: pytest in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (6.2.3)
Collecting pytest-xdist
Using cached pytest_xdist-2.2.1-py3-none-any.whl (37 kB)
Collecting apache-beam>=2.24.0
Using cached apache_beam-2.29.0-cp37-cp37m-win_amd64.whl (3.7 MB)
Collecting elasticsearch
Using cached elasticsearch-7.12.1-py2.py3-none-any.whl (339 kB)
Requirement already satisfied: boto3==1.16.43 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.16.43)
Requirement already satisfied: botocore==1.19.43 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.19.43)
Collecting moto[s3]==1.3.16
Using cached moto-1.3.16-py2.py3-none-any.whl (879 kB)
Collecting rarfile>=4.0
Using cached rarfile-4.0-py3-none-any.whl (28 kB)
Collecting tensorflow>=2.3
Using cached tensorflow-2.4.1-cp37-cp37m-win_amd64.whl (370.7 MB)
Requirement already satisfied: torch in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.8.1)
Requirement already satisfied: transformers in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (4.5.1)
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Using cached bs4-0.0.1-py3-none-any.whl
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Using cached conllu-4.4-py2.py3-none-any.whl (15 kB)
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Using cached langdetect-1.0.8-py3-none-any.whl
Collecting lxml
Using cached lxml-4.6.3-cp37-cp37m-win_amd64.whl (3.5 MB)
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Using cached mwparserfromhell-0.6-cp37-cp37m-win_amd64.whl (101 kB)
Collecting nltk
Using cached nltk-3.6.2-py3-none-any.whl (1.5 MB)
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Using cached openpyxl-3.0.7-py2.py3-none-any.whl (243 kB)
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Using cached py7zr-0.15.2-py3-none-any.whl (66 kB)
Collecting tldextract
Using cached tldextract-3.1.0-py2.py3-none-any.whl (87 kB)
Collecting zstandard
Using cached zstandard-0.15.2-cp37-cp37m-win_amd64.whl (582 kB)
Collecting bert_score>=0.3.6
Using cached bert_score-0.3.9-py3-none-any.whl (59 kB)
Collecting rouge_score
Using cached rouge_score-0.0.4-py2.py3-none-any.whl (22 kB)
Collecting sacrebleu
Using cached sacrebleu-1.5.1-py3-none-any.whl (54 kB)
Requirement already satisfied: scipy in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.6.3)
Collecting seqeval
Using cached seqeval-1.2.2-py3-none-any.whl
Collecting sklearn
Using cached sklearn-0.0-py2.py3-none-any.whl
Collecting jiwer
Using cached jiwer-2.2.0-py3-none-any.whl (13 kB)
Requirement already satisfied: toml>=0.10.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (0.10.2)
Requirement already satisfied: requests_file>=1.5.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.5.1)
Requirement already satisfied: texttable>=1.6.3 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.6.3)
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Requirement already satisfied: Werkzeug>=1.0.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datasets==1.5.0.dev0) (1.0.1)
Collecting black
Using cached black-21.4b2-py3-none-any.whl (130 kB)
Collecting isort
Using cached isort-5.8.0-py3-none-any.whl (103 kB)
Collecting flake8==3.7.9
Using cached flake8-3.7.9-py2.py3-none-any.whl (69 kB)
Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from boto3==1.16.43->datasets==1.5.0.dev0) (0.10.0)
Requirement already satisfied: s3transfer<0.4.0,>=0.3.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from boto3==1.16.43->datasets==1.5.0.dev0) (0.3.7)
Requirement already satisfied: urllib3<1.27,>=1.25.4 in c:\programdata\anaconda3\envs\env\lib\site-packages (from botocore==1.19.43->datasets==1.5.0.dev0) (1.26.4)
Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from botocore==1.19.43->datasets==1.5.0.dev0) (2.8.1)
Collecting entrypoints<0.4.0,>=0.3.0
Using cached entrypoints-0.3-py2.py3-none-any.whl (11 kB)
Collecting pyflakes<2.2.0,>=2.1.0
Using cached pyflakes-2.1.1-py2.py3-none-any.whl (59 kB)
Collecting pycodestyle<2.6.0,>=2.5.0
Using cached pycodestyle-2.5.0-py2.py3-none-any.whl (51 kB)
Collecting mccabe<0.7.0,>=0.6.0
Using cached mccabe-0.6.1-py2.py3-none-any.whl (8.6 kB)
Requirement already satisfied: jsondiff>=1.1.2 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (1.3.0)
Requirement already satisfied: pytz in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (2021.1)
Requirement already satisfied: mock in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (4.0.3)
Requirement already satisfied: MarkupSafe<2.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (1.1.1)
Requirement already satisfied: python-jose[cryptography]<4.0.0,>=3.1.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (3.2.0)
Requirement already satisfied: aws-xray-sdk!=0.96,>=0.93 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (2.8.0)
Requirement already satisfied: cryptography>=2.3.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (3.4.7)
Requirement already satisfied: more-itertools in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (8.7.0)
Requirement already satisfied: PyYAML>=5.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (5.4.1)
Requirement already satisfied: boto>=2.36.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (2.49.0)
Requirement already satisfied: idna<3,>=2.5 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (2.10)
Requirement already satisfied: sshpubkeys>=3.1.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (3.3.1)
Requirement already satisfied: responses>=0.9.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (0.13.3)
Requirement already satisfied: xmltodict in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (0.12.0)
Requirement already satisfied: setuptools in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (52.0.0.post20210125)
Requirement already satisfied: Jinja2>=2.10.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (2.11.3)
Requirement already satisfied: zipp in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (3.4.1)
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Requirement already satisfied: ecdsa<0.15 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (0.14.1)
Requirement already satisfied: docker>=2.5.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (5.0.0)
Requirement already satisfied: cfn-lint>=0.4.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from moto[s3]==1.3.16->datasets==1.5.0.dev0) (0.49.0)
Requirement already satisfied: grpcio<2,>=1.29.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from apache-beam>=2.24.0->datasets==1.5.0.dev0) (1.32.0)
Collecting hdfs<3.0.0,>=2.1.0
Using cached hdfs-2.6.0-py3-none-any.whl (33 kB)
Collecting pyarrow>=0.17.1
Using cached pyarrow-3.0.0-cp37-cp37m-win_amd64.whl (12.6 MB)
Collecting fastavro<2,>=0.21.4
Using cached fastavro-1.4.0-cp37-cp37m-win_amd64.whl (394 kB)
Requirement already satisfied: httplib2<0.18.0,>=0.8 in c:\programdata\anaconda3\envs\env\lib\site-packages (from apache-beam>=2.24.0->datasets==1.5.0.dev0) (0.17.4)
Collecting pymongo<4.0.0,>=3.8.0
Using cached pymongo-3.11.3-cp37-cp37m-win_amd64.whl (382 kB)
Collecting crcmod<2.0,>=1.7
Using cached crcmod-1.7-py3-none-any.whl
Collecting avro-python3!=1.9.2,<1.10.0,>=1.8.1
Using cached avro_python3-1.9.2.1-py3-none-any.whl
Requirement already satisfied: typing-extensions<3.8.0,>=3.7.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from apache-beam>=2.24.0->datasets==1.5.0.dev0) (3.7.4.3)
Requirement already satisfied: future<1.0.0,>=0.18.2 in c:\programdata\anaconda3\envs\env\lib\site-packages (from apache-beam>=2.24.0->datasets==1.5.0.dev0) (0.18.2)
Collecting oauth2client<5,>=2.0.1
Using cached oauth2client-4.1.3-py2.py3-none-any.whl (98 kB)
Collecting pydot<2,>=1.2.0
Using cached pydot-1.4.2-py2.py3-none-any.whl (21 kB)
Requirement already satisfied: protobuf<4,>=3.12.2 in c:\programdata\anaconda3\envs\env\lib\site-packages (from apache-beam>=2.24.0->datasets==1.5.0.dev0) (3.15.8)
Requirement already satisfied: wrapt in c:\programdata\anaconda3\envs\env\lib\site-packages (from aws-xray-sdk!=0.96,>=0.93->moto[s3]==1.3.16->datasets==1.5.0.dev0) (1.12.1)
Collecting matplotlib
Using cached matplotlib-3.4.1-cp37-cp37m-win_amd64.whl (7.1 MB)
Requirement already satisfied: junit-xml~=1.9 in c:\programdata\anaconda3\envs\env\lib\site-packages (from cfn-lint>=0.4.0->moto[s3]==1.3.16->datasets==1.5.0.dev0) (1.9)
Requirement already satisfied: jsonpatch in c:\programdata\anaconda3\envs\env\lib\site-packages (from cfn-lint>=0.4.0->moto[s3]==1.3.16->datasets==1.5.0.dev0) (1.32)
Requirement already satisfied: jsonschema~=3.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from cfn-lint>=0.4.0->moto[s3]==1.3.16->datasets==1.5.0.dev0) (3.2.0)
Requirement already satisfied: networkx~=2.4 in c:\programdata\anaconda3\envs\env\lib\site-packages (from cfn-lint>=0.4.0->moto[s3]==1.3.16->datasets==1.5.0.dev0) (2.5.1)
Requirement already satisfied: aws-sam-translator>=1.35.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from cfn-lint>=0.4.0->moto[s3]==1.3.16->datasets==1.5.0.dev0) (1.35.0)
Requirement already satisfied: cffi>=1.12 in c:\programdata\anaconda3\envs\env\lib\site-packages (from cryptography>=2.3.0->moto[s3]==1.3.16->datasets==1.5.0.dev0) (1.14.5)
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Requirement already satisfied: pywin32==227 in c:\programdata\anaconda3\envs\env\lib\site-packages (from docker>=2.5.1->moto[s3]==1.3.16->datasets==1.5.0.dev0) (227)
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Requirement already satisfied: attrs>=17.4.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from jsonschema~=3.0->cfn-lint>=0.4.0->moto[s3]==1.3.16->datasets==1.5.0.dev0) (20.3.0)
Requirement already satisfied: decorator<5,>=4.3 in c:\programdata\anaconda3\envs\env\lib\site-packages (from networkx~=2.4->cfn-lint>=0.4.0->moto[s3]==1.3.16->datasets==1.5.0.dev0) (4.4.2)
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Collecting keras-preprocessing~=1.1.2
Using cached Keras_Preprocessing-1.1.2-py2.py3-none-any.whl (42 kB)
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Collecting gast==0.3.3
Using cached gast-0.3.3-py2.py3-none-any.whl (9.7 kB)
Collecting google-pasta~=0.2
Using cached google_pasta-0.2.0-py3-none-any.whl (57 kB)
Requirement already satisfied: tensorflow-estimator<2.5.0,>=2.4.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from tensorflow>=2.3->datasets==1.5.0.dev0) (2.4.0)
Collecting astunparse~=1.6.3
Using cached astunparse-1.6.3-py2.py3-none-any.whl (12 kB)
Collecting flatbuffers~=1.12.0
Using cached flatbuffers-1.12-py2.py3-none-any.whl (15 kB)
Collecting h5py~=2.10.0
Using cached h5py-2.10.0-cp37-cp37m-win_amd64.whl (2.5 MB)
Requirement already satisfied: markdown>=2.6.8 in c:\programdata\anaconda3\envs\env\lib\site-packages (from tensorboard~=2.4->tensorflow>=2.3->datasets==1.5.0.dev0) (3.3.4)
Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from tensorboard~=2.4->tensorflow>=2.3->datasets==1.5.0.dev0) (1.8.0)
Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from tensorboard~=2.4->tensorflow>=2.3->datasets==1.5.0.dev0) (0.4.4)
Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from tensorboard~=2.4->tensorflow>=2.3->datasets==1.5.0.dev0) (0.6.0)
Requirement already satisfied: google-auth<2,>=1.6.3 in c:\programdata\anaconda3\envs\env\lib\site-packages (from tensorboard~=2.4->tensorflow>=2.3->datasets==1.5.0.dev0) (1.30.0)
Requirement already satisfied: cachetools<5.0,>=2.0.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from google-auth<2,>=1.6.3->tensorboard~=2.4->tensorflow>=2.3->datasets==1.5.0.dev0) (4.2.2)
Requirement already satisfied: requests-oauthlib>=0.7.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.4->tensorflow>=2.3->datasets==1.5.0.dev0) (1.3.0)
Requirement already satisfied: oauthlib>=3.0.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.4->tensorflow>=2.3->datasets==1.5.0.dev0) (3.1.0)
Requirement already satisfied: regex!=2019.12.17 in c:\programdata\anaconda3\envs\env\lib\site-packages (from transformers->datasets==1.5.0.dev0) (2021.4.4)
Requirement already satisfied: tokenizers<0.11,>=0.10.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from transformers->datasets==1.5.0.dev0) (0.10.2)
Requirement already satisfied: sacremoses in c:\programdata\anaconda3\envs\env\lib\site-packages (from transformers->datasets==1.5.0.dev0) (0.0.45)
Requirement already satisfied: packaging in c:\programdata\anaconda3\envs\env\lib\site-packages (from transformers->datasets==1.5.0.dev0) (20.9)
Collecting pathspec<1,>=0.8.1
Using cached pathspec-0.8.1-py2.py3-none-any.whl (28 kB)
Requirement already satisfied: click>=7.1.2 in c:\programdata\anaconda3\envs\env\lib\site-packages (from black->datasets==1.5.0.dev0) (7.1.2)
Collecting appdirs
Using cached appdirs-1.4.4-py2.py3-none-any.whl (9.6 kB)
Collecting mypy-extensions>=0.4.3
Using cached mypy_extensions-0.4.3-py2.py3-none-any.whl (4.5 kB)
Requirement already satisfied: typed-ast>=1.4.2 in c:\programdata\anaconda3\envs\env\lib\site-packages (from black->datasets==1.5.0.dev0) (1.4.3)
Collecting beautifulsoup4
Using cached beautifulsoup4-4.9.3-py3-none-any.whl (115 kB)
Requirement already satisfied: soupsieve>1.2 in c:\programdata\anaconda3\envs\env\lib\site-packages (from beautifulsoup4->bs4->datasets==1.5.0.dev0) (2.2.1)
Collecting python-Levenshtein
Using cached python-Levenshtein-0.12.2.tar.gz (50 kB)
Requirement already satisfied: jsonpointer>=1.9 in c:\programdata\anaconda3\envs\env\lib\site-packages (from jsonpatch->cfn-lint>=0.4.0->moto[s3]==1.3.16->datasets==1.5.0.dev0) (2.1)
Requirement already satisfied: pillow>=6.2.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from matplotlib->bert_score>=0.3.6->datasets==1.5.0.dev0) (8.2.0)
Requirement already satisfied: cycler>=0.10 in c:\programdata\anaconda3\envs\env\lib\site-packages (from matplotlib->bert_score>=0.3.6->datasets==1.5.0.dev0) (0.10.0)
Requirement already satisfied: kiwisolver>=1.0.1 in c:\programdata\anaconda3\envs\env\lib\site-packages (from matplotlib->bert_score>=0.3.6->datasets==1.5.0.dev0) (1.3.1)
Collecting multiprocess
Using cached multiprocess-0.70.11-py3-none-any.whl (98 kB)
Using cached multiprocess-0.70.10.zip (2.4 MB)
Using cached multiprocess-0.70.9-py3-none-any.whl
Requirement already satisfied: joblib in c:\programdata\anaconda3\envs\env\lib\site-packages (from nltk->datasets==1.5.0.dev0) (1.0.1)
Collecting et-xmlfile
Using cached et_xmlfile-1.1.0-py3-none-any.whl (4.7 kB)
Requirement already satisfied: pyzstd<0.15.0,>=0.14.4 in c:\programdata\anaconda3\envs\env\lib\site-packages (from py7zr->datasets==1.5.0.dev0) (0.14.4)
Collecting pyppmd<0.13.0,>=0.12.1
Using cached pyppmd-0.12.1-cp37-cp37m-win_amd64.whl (32 kB)
Collecting pycryptodome>=3.6.6
Using cached pycryptodome-3.10.1-cp35-abi3-win_amd64.whl (1.6 MB)
Collecting bcj-cffi<0.6.0,>=0.5.1
Using cached bcj_cffi-0.5.1-cp37-cp37m-win_amd64.whl (21 kB)
Collecting multivolumefile<0.3.0,>=0.2.0
Using cached multivolumefile-0.2.3-py3-none-any.whl (17 kB)
Requirement already satisfied: iniconfig in c:\programdata\anaconda3\envs\env\lib\site-packages (from pytest->datasets==1.5.0.dev0) (1.1.1)
Requirement already satisfied: py>=1.8.2 in c:\programdata\anaconda3\envs\env\lib\site-packages (from pytest->datasets==1.5.0.dev0) (1.10.0)
Requirement already satisfied: pluggy<1.0.0a1,>=0.12 in c:\programdata\anaconda3\envs\env\lib\site-packages (from pytest->datasets==1.5.0.dev0) (0.13.1)
Requirement already satisfied: atomicwrites>=1.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from pytest->datasets==1.5.0.dev0) (1.4.0)
Requirement already satisfied: colorama in c:\programdata\anaconda3\envs\env\lib\site-packages (from pytest->datasets==1.5.0.dev0) (0.4.4)
Collecting pytest-forked
Using cached pytest_forked-1.3.0-py2.py3-none-any.whl (4.7 kB)
Collecting execnet>=1.1
Using cached execnet-1.8.0-py2.py3-none-any.whl (39 kB)
Requirement already satisfied: apipkg>=1.4 in c:\programdata\anaconda3\envs\env\lib\site-packages (from execnet>=1.1->pytest-xdist->datasets==1.5.0.dev0) (1.5)
Collecting portalocker==2.0.0
Using cached portalocker-2.0.0-py2.py3-none-any.whl (11 kB)
Requirement already satisfied: scikit-learn>=0.21.3 in c:\programdata\anaconda3\envs\env\lib\site-packages (from seqeval->datasets==1.5.0.dev0) (0.24.2)
Requirement already satisfied: threadpoolctl>=2.0.0 in c:\programdata\anaconda3\envs\env\lib\site-packages (from scikit-learn>=0.21.3->seqeval->datasets==1.5.0.dev0) (2.1.0)
Building wheels for collected packages: python-Levenshtein
Building wheel for python-Levenshtein (setup.py) ... error
ERROR: Command errored out with exit status 1:
command: 'C:\ProgramData\Anaconda3\envs\env\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\setup.py'"'"'; __file__='"'"'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d 'C:\Users\VKC~1\AppData\Local\Temp\pip-wheel-8jh7fm18'
cwd: C:\Users\VKC~1\AppData\Local\Temp\pip-install-ynt_dbm4\python-levenshtein_c02e7e6f9def4629a475349654670ae9\
Complete output (27 lines):
running bdist_wheel
running build
running build_py
creating build
creating build\lib.win-amd64-3.7
creating build\lib.win-amd64-3.7\Levenshtein
copying Levenshtein\StringMatcher.py -> build\lib.win-amd64-3.7\Levenshtein
copying Levenshtein\__init__.py -> build\lib.win-amd64-3.7\Levenshtein
running egg_info
writing python_Levenshtein.egg-info\PKG-INFO
writing dependency_links to python_Levenshtein.egg-info\dependency_links.txt
writing entry points to python_Levenshtein.egg-info\entry_points.txt
writing namespace_packages to python_Levenshtein.egg-info\namespace_packages.txt
writing requirements to python_Levenshtein.egg-info\requires.txt
writing top-level names to python_Levenshtein.egg-info\top_level.txt
reading manifest file 'python_Levenshtein.egg-info\SOURCES.txt'
reading manifest template 'MANIFEST.in'
warning: no previously-included files matching '*pyc' found anywhere in distribution
warning: no previously-included files matching '*so' found anywhere in distribution
warning: no previously-included files matching '.project' found anywhere in distribution
warning: no previously-included files matching '.pydevproject' found anywhere in distribution
writing manifest file 'python_Levenshtein.egg-info\SOURCES.txt'
copying Levenshtein\_levenshtein.c -> build\lib.win-amd64-3.7\Levenshtein
copying Levenshtein\_levenshtein.h -> build\lib.win-amd64-3.7\Levenshtein
running build_ext
building 'Levenshtein._levenshtein' extension
error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/
----------------------------------------
ERROR: Failed building wheel for python-Levenshtein
Running setup.py clean for python-Levenshtein
Failed to build python-Levenshtein
Installing collected packages: python-Levenshtein, pytest-forked, pyppmd, pymongo, pyflakes, pydot, pycryptodome, pycodestyle, pyarrow, portalocker, pathspec, pandas, opt-einsum, oauth2client, nltk, mypy-extensions, multivolumefile, multiprocess, moto, mccabe, matplotlib, keras-preprocessing, huggingface-hub, hdfs, h5py, google-pasta, gast, flatbuffers, fastavro, execnet, et-xmlfile, entrypoints, crcmod, beautifulsoup4, bcj-cffi, avro-python3, astunparse, appdirs, zstandard, tldextract, tensorflow, sklearn, seqeval, sacrebleu, rouge-score, rarfile, pytest-xdist, py7zr, openpyxl, mwparserfromhell, lxml, langdetect, jiwer, isort, flake8, elasticsearch, datasets, conllu, bs4, black, bert-score, apache-beam
Running setup.py install for python-Levenshtein ... error
ERROR: Command errored out with exit status 1:
command: 'C:\ProgramData\Anaconda3\envs\env\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\setup.py'"'"'; __file__='"'"'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\VKC~1\AppData\Local\Temp\pip-record-v7l7zitb\install-record.txt' --single-version-externally-managed --compile --install-headers 'C:\ProgramData\Anaconda3\envs\env\Include\python-Levenshtein'
cwd: C:\Users\VKC~1\AppData\Local\Temp\pip-install-ynt_dbm4\python-levenshtein_c02e7e6f9def4629a475349654670ae9\
Complete output (27 lines):
running install
running build
running build_py
creating build
creating build\lib.win-amd64-3.7
creating build\lib.win-amd64-3.7\Levenshtein
copying Levenshtein\StringMatcher.py -> build\lib.win-amd64-3.7\Levenshtein
copying Levenshtein\__init__.py -> build\lib.win-amd64-3.7\Levenshtein
running egg_info
writing python_Levenshtein.egg-info\PKG-INFO
writing dependency_links to python_Levenshtein.egg-info\dependency_links.txt
writing entry points to python_Levenshtein.egg-info\entry_points.txt
writing namespace_packages to python_Levenshtein.egg-info\namespace_packages.txt
writing requirements to python_Levenshtein.egg-info\requires.txt
writing top-level names to python_Levenshtein.egg-info\top_level.txt
reading manifest file 'python_Levenshtein.egg-info\SOURCES.txt'
reading manifest template 'MANIFEST.in'
warning: no previously-included files matching '*pyc' found anywhere in distribution
warning: no previously-included files matching '*so' found anywhere in distribution
warning: no previously-included files matching '.project' found anywhere in distribution
warning: no previously-included files matching '.pydevproject' found anywhere in distribution
writing manifest file 'python_Levenshtein.egg-info\SOURCES.txt'
copying Levenshtein\_levenshtein.c -> build\lib.win-amd64-3.7\Levenshtein
copying Levenshtein\_levenshtein.h -> build\lib.win-amd64-3.7\Levenshtein
running build_ext
building 'Levenshtein._levenshtein' extension
error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/
----------------------------------------
ERROR: Command errored out with exit status 1: 'C:\ProgramData\Anaconda3\envs\env\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\setup.py'"'"'; __file__='"'"'C:\\Users\\VKC~1\\AppData\\Local\\Temp\\pip-install-ynt_dbm4\\python-levenshtein_c02e7e6f9def4629a475349654670ae9\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\VKC~1\AppData\Local\Temp\pip-record-v7l7zitb\install-record.txt' --single-version-externally-managed --compile --install-headers 'C:\ProgramData\Anaconda3\envs\env\Include\python-Levenshtein' Check the logs for full command output.
```
Here are conda and python versions:
```bat
(env) C:\testing\datasets>conda --version
conda 4.9.2
(env) C:\testing\datasets>python --version
Python 3.7.10
```
Please help me out. Thanks.
|
closed
|
https://github.com/huggingface/datasets/issues/2301
| 2021-05-02T13:20:42
| 2021-05-03T15:18:01
| 2021-05-03T15:17:34
|
{
"login": "gchhablani",
"id": 29076344,
"type": "User"
}
|
[] | false
|
[] |
873,928,169
| 2,300
|
Add VoxPopuli
|
## Adding a Dataset
- **Name:** Voxpopuli
- **Description:** VoxPopuli is raw data is collected from 2009-2020 European Parliament event recordings
- **Paper:** https://arxiv.org/abs/2101.00390
- **Data:** https://github.com/facebookresearch/voxpopuli
- **Motivation:** biggest unlabeled speech dataset
**Note**: Since the dataset is so huge, we should only add the config `10k` in the beginning.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
|
closed
|
https://github.com/huggingface/datasets/issues/2300
| 2021-05-02T12:17:40
| 2023-02-28T17:43:52
| 2023-02-28T17:43:51
|
{
"login": "patrickvonplaten",
"id": 23423619,
"type": "User"
}
|
[
{
"name": "dataset request",
"color": "e99695"
},
{
"name": "speech",
"color": "d93f0b"
}
] | false
|
[] |
873,914,717
| 2,299
|
My iPhone
|
## Adding a Dataset
- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
|
closed
|
https://github.com/huggingface/datasets/issues/2299
| 2021-05-02T11:11:11
| 2021-07-23T09:24:16
| 2021-05-03T08:17:38
|
{
"login": "Jasonbuchanan1983",
"id": 82856229,
"type": "User"
}
|
[] | false
|
[] |
873,771,942
| 2,298
|
Mapping in the distributed setting
|
The barrier trick for distributed mapping as discussed on Thursday with @lhoestq
|
closed
|
https://github.com/huggingface/datasets/pull/2298
| 2021-05-01T21:23:05
| 2021-05-03T13:54:53
| 2021-05-03T13:54:53
|
{
"login": "TevenLeScao",
"id": 26709476,
"type": "User"
}
|
[] | true
|
[] |
872,974,907
| 2,296
|
1
|
## Adding a Dataset
- **Name:** *name of the dataset*
- **Description:** *short description of the dataset (or link to social media or blog post)*
- **Paper:** *link to the dataset paper if available*
- **Data:** *link to the Github repository or current dataset location*
- **Motivation:** *what are some good reasons to have this dataset*
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
|
closed
|
https://github.com/huggingface/datasets/issues/2296
| 2021-04-30T17:53:49
| 2021-05-03T08:17:31
| 2021-05-03T08:17:31
|
{
"login": "zinnyi",
"id": 82880142,
"type": "User"
}
|
[
{
"name": "dataset request",
"color": "e99695"
}
] | false
|
[] |
872,902,867
| 2,295
|
Create ExtractManager
|
Perform refactoring to decouple extract functionality.
|
closed
|
https://github.com/huggingface/datasets/pull/2295
| 2021-04-30T17:13:34
| 2021-07-12T14:12:03
| 2021-07-08T08:11:49
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[
{
"name": "refactoring",
"color": "B67A40"
}
] | true
|
[] |
872,136,075
| 2,294
|
Slow #0 when using map to tokenize.
|
Hi, _datasets_ is really amazing! I am following [run_mlm_no_trainer.py](url) to pre-train BERT, and it uses `tokenized_datasets = raw_datasets.map(
tokenize_function,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
)` to tokenize by multiprocessing. However, I have found that when `num_proc`>1，the process _#0_ is much slower than others.
It looks like this:
It takes more than 12 hours for #0, while others just about half an hour. Could anyone tell me it is normal or not, and is there any methods to speed up it?
|
open
|
https://github.com/huggingface/datasets/issues/2294
| 2021-04-30T08:00:33
| 2021-05-04T11:00:11
| null |
{
"login": "VerdureChen",
"id": 31714566,
"type": "User"
}
|
[] | false
|
[] |
872,079,385
| 2,293
|
imdb dataset from Don't Stop Pretraining Paper
|
closed
|
https://github.com/huggingface/datasets/pull/2293
| 2021-04-30T06:40:48
| 2021-04-30T06:54:25
| 2021-04-30T06:54:25
|
{
"login": "BobbyManion",
"id": 52530809,
"type": "User"
}
|
[] | true
|
[] |
|
871,230,183
| 2,292
|
Fixed typo seperate->separate
|
closed
|
https://github.com/huggingface/datasets/pull/2292
| 2021-04-29T16:40:53
| 2021-04-30T13:29:18
| 2021-04-30T13:03:12
|
{
"login": "laksh9950",
"id": 32505743,
"type": "User"
}
|
[] | true
|
[] |
|
871,216,757
| 2,291
|
Don't copy recordbatches in memory during a table deepcopy
|
Fix issue #2276 and hopefully #2134
The recordbatches of the `IndexedTableMixin` used to speed up queries to the table were copied in memory during a table deepcopy.
This resulted in `concatenate_datasets`, `load_from_disk` and other methods to always bring the data in memory.
I fixed the copy similarly to #2287 and updated the test to make sure it doesn't happen again (added a test for deepcopy + make sure that the immutable arrow objects are passed to the copied table without being copied).
The issue was not caught by our tests because the total allocated bytes value in PyArrow isn't updated when deepcopying recordbatches: the copy in memory wasn't detected. This behavior looks like a bug in PyArrow, I'll open a ticket on JIRA.
Thanks @samsontmr , @TaskManager91 and @mariosasko for the help
|
closed
|
https://github.com/huggingface/datasets/pull/2291
| 2021-04-29T16:26:05
| 2021-04-29T16:34:35
| 2021-04-29T16:34:34
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
871,145,817
| 2,290
|
Bbaw egyptian
|
This is the "hieroglyph corpus" that I could unfortunately not contribute during the marathon. I re-extracted it again now, so that it is in the state as used in my paper (seee documentation). I hope it satiesfies your requirements and wish every scientist out their loads of fun deciphering a 5.000 years old language :-)
|
closed
|
https://github.com/huggingface/datasets/pull/2290
| 2021-04-29T15:27:58
| 2021-05-06T17:25:25
| 2021-05-06T17:25:25
|
{
"login": "phiwi",
"id": 54144149,
"type": "User"
}
|
[] | true
|
[] |
871,118,573
| 2,289
|
Allow collaborators to self-assign issues
|
Allow collaborators (without write access to the repository) to self-assign issues.
In order to self-assign an issue, they have to comment it with the word: `#take` or `#self-assign`.
|
closed
|
https://github.com/huggingface/datasets/pull/2289
| 2021-04-29T15:07:06
| 2021-04-30T18:28:16
| 2021-04-30T18:28:16
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
871,111,235
| 2,288
|
Load_dataset for local CSV files
|
The method load_dataset fails to correctly load a dataset from csv.
Moreover, I am working on a token-classification task ( POS tagging) , where each row in my CSV contains two columns each of them having a list of strings.
row example:
```tokens | labels
['I' , 'am', 'John'] | ['PRON', 'AUX', 'PROPN' ]
```
The method, loads each list as a string: (i.g "['I' , 'am', 'John']").
To solve this issue, I copied the Datasets.Features, created Sequence types ( instead of Value) and tried to cast the features type
```
new_features['tokens'] = Sequence(feature=Value(dtype='string', id=None))
new_features['labels'] = Sequence(feature=ClassLabel(num_classes=len(tag2idx), names=list(unique_tags)))
dataset = dataset.cast(new_features)
```
but I got the following error
```
ArrowNotImplementedError: Unsupported cast from string to list using function cast_list
```
Moreover, I tried to set feature parameter in load_dataset method, to my new_features, but this fails as well.
How can this be solved ?
|
closed
|
https://github.com/huggingface/datasets/issues/2288
| 2021-04-29T15:01:10
| 2021-06-15T13:49:26
| 2021-06-15T13:49:26
|
{
"login": "sstojanoska",
"id": 17052700,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
871,063,374
| 2,287
|
Avoid copying table's record batches
|
Fixes #2276
|
closed
|
https://github.com/huggingface/datasets/pull/2287
| 2021-04-29T14:15:01
| 2021-04-29T16:34:23
| 2021-04-29T16:34:22
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
871,032,393
| 2,286
|
Fix metadata validation with config names
|
I noticed in https://github.com/huggingface/datasets/pull/2280 that the metadata validator doesn't parse the tags in the readme properly when then contain the tags per config.
|
closed
|
https://github.com/huggingface/datasets/pull/2286
| 2021-04-29T13:44:32
| 2021-04-29T14:07:29
| 2021-04-29T14:07:28
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
871,005,236
| 2,285
|
Help understanding how to build a dataset for language modeling as with the old TextDataset
|
Hello,
I am trying to load a custom dataset that I will then use for language modeling. The dataset consists of a text file that has a whole document in each line, meaning that each line overpasses the normal 512 tokens limit of most tokenizers.
I would like to understand what is the process to build a text dataset that tokenizes each line, having previously split the documents in the dataset into lines of a "tokenizable" size, as the old TextDataset class would do, where you only had to do the following, and a tokenized dataset without text loss would be available to pass to a DataCollator:
```
model_checkpoint = 'distilbert-base-uncased'
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
from transformers import TextDataset
dataset = TextDataset(
tokenizer=tokenizer,
file_path="path/to/text_file.txt",
block_size=512,
)
```
For now, what I have is the following, which, of course, throws an error because each line is longer than the maximum block size in the tokenizer:
```
import datasets
dataset = datasets.load_dataset('path/to/text_file.txt')
model_checkpoint = 'distilbert-base-uncased'
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
def tokenize_function(examples):
return tokenizer(examples["text"])
tokenized_datasets = dataset.map(tokenize_function, batched=True, num_proc=4, remove_columns=["text"])
tokenized_datasets
```
So what would be the "standard" way of creating a dataset in the way it was done before?
Thank you very much for the help :))
|
closed
|
https://github.com/huggingface/datasets/issues/2285
| 2021-04-29T13:16:45
| 2021-05-19T07:22:45
| 2021-05-19T07:22:39
|
{
"login": "danieldiezmallo",
"id": 46021411,
"type": "User"
}
|
[] | false
|
[] |
870,932,710
| 2,284
|
Initialize Imdb dataset as used in Don't Stop Pretraining Paper
|
closed
|
https://github.com/huggingface/datasets/pull/2284
| 2021-04-29T11:52:38
| 2021-04-29T12:54:34
| 2021-04-29T12:54:34
|
{
"login": "BobbyManion",
"id": 52530809,
"type": "User"
}
|
[] | true
|
[] |
|
870,926,475
| 2,283
|
Initialize imdb dataset from don't stop pretraining paper
|
closed
|
https://github.com/huggingface/datasets/pull/2283
| 2021-04-29T11:44:54
| 2021-04-29T11:50:24
| 2021-04-29T11:50:24
|
{
"login": "BobbyManion",
"id": 52530809,
"type": "User"
}
|
[] | true
|
[] |
|
870,900,332
| 2,282
|
Initialize imdb dataset from don't stop pretraining paper
|
closed
|
https://github.com/huggingface/datasets/pull/2282
| 2021-04-29T11:17:56
| 2021-04-29T11:43:51
| 2021-04-29T11:43:51
|
{
"login": "BobbyManion",
"id": 52530809,
"type": "User"
}
|
[] | true
|
[] |
|
870,792,784
| 2,281
|
Update multi_woz_v22 checksum
|
Fix issue https://github.com/huggingface/datasets/issues/1876
The files were changed in https://github.com/budzianowski/multiwoz/pull/72
|
closed
|
https://github.com/huggingface/datasets/pull/2281
| 2021-04-29T09:09:11
| 2021-04-29T13:41:35
| 2021-04-29T13:41:34
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
870,780,431
| 2,280
|
Fixed typo seperate->separate
|
closed
|
https://github.com/huggingface/datasets/pull/2280
| 2021-04-29T08:55:46
| 2021-04-29T16:41:22
| 2021-04-29T16:41:16
|
{
"login": "laksh9950",
"id": 32505743,
"type": "User"
}
|
[] | true
|
[] |
|
870,431,662
| 2,279
|
Compatibility with Ubuntu 18 and GLIBC 2.27?
|
## Describe the bug
For use on Ubuntu systems, it seems that datasets requires GLIBC 2.29. However, Ubuntu 18 runs with GLIBC 2.27 and it seems [non-trivial to upgrade GLIBC to 2.29 for Ubuntu 18 users](https://www.digitalocean.com/community/questions/how-install-glibc-2-29-or-higher-in-ubuntu-18-04).
I'm not sure if there is anything that can be done about this, but I'd like to confirm that using huggingface/datasets requires either an upgrade to Ubuntu 19/20 or a hand-rolled install of a higher version of GLIBC.
## Steps to reproduce the bug
1. clone the transformers repo
2. move to examples/pytorch/language-modeling
3. run example command:
```python run_clm.py --model_name_or_path gpt2 --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 --do_train --do_eval --output_dir /tmp/test-clm```
## Expected results
As described in the transformers repo.
## Actual results
```Traceback (most recent call last):
File "run_clm.py", line 34, in <module>
from transformers import (
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/__init__.py", line 2487, in __getattr__
return super().__getattr__(name)
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/file_utils.py", line 1699, in __getattr__
module = self._get_module(self._class_to_module[name])
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/__init__.py", line 2481, in _get_module
return importlib.import_module("." + module_name, self.__name__)
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/models/__init__.py", line 19, in <module>
from . import (
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/models/layoutlm/__init__.py", line 23, in <module>
from .tokenization_layoutlm import LayoutLMTokenizer
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/models/layoutlm/tokenization_layoutlm.py", line 19, in <module>
from ..bert.tokenization_bert import BertTokenizer
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/models/bert/tokenization_bert.py", line 23, in <module>
from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/tokenization_utils.py", line 26, in <module>
from .tokenization_utils_base import (
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 68, in <module>
from tokenizers import AddedToken
File "/home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/tokenizers/__init__.py", line 79, in <module>
from .tokenizers import (
ImportError: /lib/x86_64-linux-gnu/libm.so.6: version `GLIBC_2.29' not found (required by /home/tginart/anaconda3/envs/huggingface/lib/python3.7/site-packages/tokenizers/tokenizers.cpython-37m-x86_64-linux-gnu.so)
```
## Versions
Paste the output of the following code:
```
- Datasets: 1.6.1
- Python: 3.7.10 (default, Feb 26 2021, 18:47:35)
[GCC 7.3.0]
- Platform: Linux-4.15.0-128-generic-x86_64-with-debian-buster-sid
```
|
closed
|
https://github.com/huggingface/datasets/issues/2279
| 2021-04-28T22:08:07
| 2021-04-29T07:42:42
| 2021-04-29T07:42:42
|
{
"login": "tginart",
"id": 11379648,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
870,088,059
| 2,278
|
Loss result inGptNeoForCasual
|
Is there any way you give the " loss" and "logits" results in the gpt neo api?
|
closed
|
https://github.com/huggingface/datasets/issues/2278
| 2021-04-28T15:39:52
| 2021-05-06T16:14:23
| 2021-05-06T16:14:23
|
{
"login": "Yossillamm",
"id": 51174606,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
}
] | false
|
[] |
870,071,994
| 2,277
|
Create CacheManager
|
Perform refactoring to decouple cache functionality (method `as_dataset`).
|
open
|
https://github.com/huggingface/datasets/pull/2277
| 2021-04-28T15:23:42
| 2022-07-06T15:19:48
| null |
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[
{
"name": "refactoring",
"color": "B67A40"
}
] | true
|
[] |
870,010,511
| 2,276
|
concatenate_datasets loads all the data into memory
|
## Describe the bug
When I try to concatenate 2 datasets (10GB each) , the entire data is loaded into memory instead of being written directly to disk.
Interestingly, this happens when trying to save the new dataset to disk or concatenating it again.
## Steps to reproduce the bug
```python
from datasets import concatenate_datasets, load_from_disk
test_sampled_pro = load_from_disk("test_sampled_pro")
val_sampled_pro = load_from_disk("val_sampled_pro")
big_set = concatenate_datasets([test_sampled_pro, val_sampled_pro])
# Loaded to memory
big_set.save_to_disk("big_set")
# Loaded to memory
big_set = concatenate_datasets([big_set, val_sampled_pro])
```
## Expected results
The data should be loaded into memory in batches and then saved directly to disk.
## Actual results
The entire data set is loaded into the memory and then saved to the hard disk.
## Versions
Paste the output of the following code:
```python
- Datasets: 1.6.1
- Python: 3.8.8 (default, Apr 13 2021, 19:58:26)
[GCC 7.3.0]
- Platform: Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-glibc2.10
```
|
closed
|
https://github.com/huggingface/datasets/issues/2276
| 2021-04-28T14:27:21
| 2021-05-03T08:41:55
| 2021-05-03T08:41:55
|
{
"login": "chbensch",
"id": 7063207,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
869,378,311
| 2,275
|
SNLI dataset has labels of -1
|
There are a number of rows with a label of -1 in the SNLI dataset. The dataset descriptions [here](https://nlp.stanford.edu/projects/snli/) and [here](https://github.com/huggingface/datasets/tree/master/datasets/snli) don't list -1 as a label possibility, and neither does the dataset viewer. As examples, see index 107 or 124 of the test set.
It isn't clear what these labels mean. I found a [line of code](https://github.com/huggingface/datasets/blob/80e59ef178d3bb2090d091bc32315c655eb0633d/datasets/snli/snli.py#L94) that seems to put them in but it seems still unclear why they are there. The current workaround is to just drop the rows from any model being trained.
Perhaps the documentation should be updated.
|
closed
|
https://github.com/huggingface/datasets/issues/2275
| 2021-04-28T00:32:25
| 2021-05-17T13:34:18
| 2021-05-17T13:34:18
|
{
"login": "puzzler10",
"id": 17426779,
"type": "User"
}
|
[] | false
|
[] |
869,186,276
| 2,274
|
Always update metadata in arrow schema
|
We store a redundant copy of the features in the metadata of the schema of the arrow table. This is used to recover the features when doing `Dataset.from_file`. These metadata are updated after each transfor, that changes the feature types.
For each function that transforms the feature types of the dataset, I added a step in the tests to make sure the metadata in the arrow schema are up to date.
I also added a line to update the metadata directly in the Dataset.__init__ method.
This way even a dataset instantiated with __init__ will have a table with the right metadata.
Fix #2271.
cc @mariosasko
|
closed
|
https://github.com/huggingface/datasets/pull/2274
| 2021-04-27T19:21:57
| 2022-06-03T08:31:19
| 2021-04-29T09:57:50
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
869,046,290
| 2,273
|
Added CUAD metrics
|
`EM`, `F1`, `AUPR`, `Precision@80%Recall`, and `Precision@90%Recall` metrics supported for CUAD
|
closed
|
https://github.com/huggingface/datasets/pull/2273
| 2021-04-27T16:49:12
| 2021-04-29T13:59:47
| 2021-04-29T13:59:47
|
{
"login": "bhavitvyamalik",
"id": 19718818,
"type": "User"
}
|
[] | true
|
[] |
869,017,977
| 2,272
|
Bug in Dataset.class_encode_column
|
## Describe the bug
All the rest of the columns except the one passed to `Dataset.class_encode_column` are discarded.
## Expected results
All the original columns should be kept.
This needs regression tests.
|
closed
|
https://github.com/huggingface/datasets/issues/2272
| 2021-04-27T16:13:18
| 2021-04-30T12:54:27
| 2021-04-30T12:54:27
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
869,002,141
| 2,271
|
Synchronize table metadata with features
|
**Is your feature request related to a problem? Please describe.**
As pointed out in this [comment](https://github.com/huggingface/datasets/pull/2145#discussion_r621326767):
> Metadata stored in the schema is just a redundant information regarding the feature types.
It is used when calling Dataset.from_file to know which feature types to use.
These metadata are stored in the schema of the pyarrow table by using `update_metadata_with_features`.
However this something that's almost never tested properly.
**Describe the solution you'd like**
We should find a way to always make sure that the metadata (in `self.data.schema.metadata`) are synced with the actual feature types (in `self.info.features`).
|
closed
|
https://github.com/huggingface/datasets/issues/2271
| 2021-04-27T15:55:13
| 2022-06-01T17:13:21
| 2022-06-01T17:13:21
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
}
] | false
|
[] |
868,913,660
| 2,270
|
Fix iterable interface expected by numpy
|
Numpy expects the old iterable interface with `__getitem__` instead of `__iter__`.
|
closed
|
https://github.com/huggingface/datasets/pull/2270
| 2021-04-27T14:35:56
| 2021-04-28T17:39:27
| 2021-04-28T17:39:27
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
868,878,468
| 2,269
|
Fix query table with iterable
|
The benchmark runs are failing on master because it tries to use an iterable to query the dataset.
However there's currently an issue caused by the use of `np.array` instead of `np.fromiter` on the iterable.
This PR fixes it
|
closed
|
https://github.com/huggingface/datasets/pull/2269
| 2021-04-27T13:59:38
| 2021-04-27T14:21:57
| 2021-04-27T14:21:56
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
868,773,380
| 2,268
|
Don't use pyarrow 4.0.0 since it segfaults when casting a sliced ListArray of integers
|
This test `tests/test_table.py::test_concatenation_table_cast` segfaults with the latest update of pyarrow 4.0.0.
Setting `pyarrow<4.0.0` for now. I'll open an issue on JIRA once I know more about the origin of the issue
|
closed
|
https://github.com/huggingface/datasets/pull/2268
| 2021-04-27T11:58:28
| 2021-06-12T12:44:49
| 2021-04-27T13:43:20
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
868,291,129
| 2,267
|
DatasetDict save load Failing test in 1.6 not in 1.5
|
## Describe the bug
We have a test that saves a DatasetDict to disk and then loads it from disk. In 1.6 there is an incompatibility in the schema.
Downgrading to `>1.6` -- fixes the problem.
## Steps to reproduce the bug
```python
### Load a dataset dict from jsonl
path = '/test/foo'
ds_dict.save_to_disk(path)
ds_from_disk = DatasetDict.load_from_disk(path). ## <-- this is where I see the error on 1.6
```
## Expected results
Upgrading to 1.6 shouldn't break that test. We should be able to serialize to and from disk.
## Actual results
```
# Infer features if None
inferred_features = Features.from_arrow_schema(arrow_table.schema)
if self.info.features is None:
self.info.features = inferred_features
# Infer fingerprint if None
if self._fingerprint is None:
self._fingerprint = generate_fingerprint(self)
# Sanity checks
assert self.features is not None, "Features can't be None in a Dataset object"
assert self._fingerprint is not None, "Fingerprint can't be None in a Dataset object"
if self.info.features.type != inferred_features.type:
> raise ValueError(
"External features info don't match the dataset:\nGot\n{}\nwith type\n{}\n\nbut expected something like\n{}\nwith type\n{}".format(
self.info.features, self.info.features.type, inferred_features, inferred_features.type
)
)
E ValueError: External features info don't match the dataset:
E Got
E {'_input_hash': Value(dtype='int64', id=None), '_task_hash': Value(dtype='int64', id=None), '_view_id': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'encoding__ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'encoding__offsets': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'encoding__overflowing': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'encoding__tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'encoding__words': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'relations': [{'child': Value(dtype='int64', id=None), 'child_span': {'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None)}, 'color': Value(dtype='string', id=None), 'head': Value(dtype='int64', id=None), 'head_span': {'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None)}, 'label': Value(dtype='string', id=None)}], 'spans': [{'end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None), 'token_end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'type': Value(dtype='string', id=None)}], 'text': Value(dtype='string', id=None), 'tokens': [{'disabled': Value(dtype='bool', id=None), 'end': Value(dtype='int64', id=None), 'id': Value(dtype='int64', id=None), 'start': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None), 'ws': Value(dtype='bool', id=None)}]}
E with type
E struct<_input_hash: int64, _task_hash: int64, _view_id: string, answer: string, encoding__ids: list<item: int64>, encoding__offsets: list<item: list<item: int64>>, encoding__overflowing: list<item: null>, encoding__tokens: list<item: string>, encoding__words: list<item: int64>, ner_ids: list<item: int64>, ner_labels: list<item: string>, relations: list<item: struct<child: int64, child_span: struct<end: int64, label: string, start: int64, token_end: int64, token_start: int64>, color: string, head: int64, head_span: struct<end: int64, label: string, start: int64, token_end: int64, token_start: int64>, label: string>>, spans: list<item: struct<end: int64, label: string, start: int64, text: string, token_end: int64, token_start: int64, type: string>>, text: string, tokens: list<item: struct<disabled: bool, end: int64, id: int64, start: int64, text: string, ws: bool>>>
E
E but expected something like
E {'_input_hash': Value(dtype='int64', id=None), '_task_hash': Value(dtype='int64', id=None), '_view_id': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'encoding__ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'encoding__offsets': Sequence(feature=Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), length=-1, id=None), 'encoding__overflowing': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None), 'encoding__tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'encoding__words': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'ner_labels': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'relations': [{'head': Value(dtype='int64', id=None), 'child': Value(dtype='int64', id=None), 'head_span': {'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None)}, 'child_span': {'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'label': Value(dtype='string', id=None)}, 'color': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}], 'spans': [{'text': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'token_start': Value(dtype='int64', id=None), 'token_end': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'type': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}], 'text': Value(dtype='string', id=None), 'tokens': [{'text': Value(dtype='string', id=None), 'start': Value(dtype='int64', id=None), 'end': Value(dtype='int64', id=None), 'id': Value(dtype='int64', id=None), 'ws': Value(dtype='bool', id=None), 'disabled': Value(dtype='bool', id=None)}]}
E with type
E struct<_input_hash: int64, _task_hash: int64, _view_id: string, answer: string, encoding__ids: list<item: int64>, encoding__offsets: list<item: list<item: int64>>, encoding__overflowing: list<item: null>, encoding__tokens: list<item: string>, encoding__words: list<item: int64>, ner_ids: list<item: int64>, ner_labels: list<item: string>, relations: list<item: struct<head: int64, child: int64, head_span: struct<start: int64, end: int64, token_start: int64, token_end: int64, label: string>, child_span: struct<start: int64, end: int64, token_start: int64, token_end: int64, label: string>, color: string, label: string>>, spans: list<item: struct<text: string, start: int64, token_start: int64, token_end: int64, end: int64, type: string, label: string>>, text: string, tokens: list<item: struct<text: string, start: int64, end: int64, id: int64, ws: bool, disabled: bool>>>
../../../../../.virtualenvs/tf_ner_rel_lib/lib/python3.8/site-packages/datasets/arrow_dataset.py:274: ValueError
```
## Versions
- Datasets: 1.6.1
- Python: 3.8.5 (default, Jan 26 2021, 10:01:04)
[Clang 12.0.0 (clang-1200.0.32.2)]
- Platform: macOS-10.15.7-x86_64-i386-64bit
```
|
open
|
https://github.com/huggingface/datasets/issues/2267
| 2021-04-27T00:03:25
| 2021-05-28T15:27:34
| null |
{
"login": "timothyjlaurent",
"id": 2000204,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
867,864,353
| 2,266
|
Make tests run faster
|
From 7min to 2min to run pytest.
Ideally we should keep the whole CI run time below 10min.
In this PR I removed the remote tests that were never used.
I also replaced nested parametrized tests with unit tests.
This makes me think that we could still add more high level tests to check for a few combinations of parameters (but not all of them since there are too many of them).
Let me know what you think
Finally in another PR we can also separate in two circleci jobs:
- the tests of the code code of the lib
- the tests of the all the dataset/metric scripts.
|
closed
|
https://github.com/huggingface/datasets/pull/2266
| 2021-04-26T15:55:40
| 2021-04-29T10:00:13
| 2021-04-29T10:00:04
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
867,490,646
| 2,265
|
Update black
|
Latest black version 21.4b0 requires to reformat most dataset scripts and also the core code of the lib.
This makes the CI currently fail on master
|
closed
|
https://github.com/huggingface/datasets/pull/2265
| 2021-04-26T09:35:09
| 2021-04-26T09:47:48
| 2021-04-26T09:47:47
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
867,476,228
| 2,264
|
Fix memory issue in multiprocessing: Don't pickle table index
|
The table index is currently being pickled when doing multiprocessing, which brings all the record batches of the dataset in memory.
I fixed that by not pickling the index attributes. Therefore each process has to rebuild the index when unpickling the table.
Fix issue #2256
We'll do a patch release asap !
|
closed
|
https://github.com/huggingface/datasets/pull/2264
| 2021-04-26T09:21:35
| 2021-04-26T10:30:28
| 2021-04-26T10:08:14
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
867,420,912
| 2,263
|
test data added, dataset_infos updated
|
Fixes #2262. Thanks for pointing out issue with dataset @jinmang2!
|
closed
|
https://github.com/huggingface/datasets/pull/2263
| 2021-04-26T08:27:18
| 2021-04-29T09:30:21
| 2021-04-29T09:30:20
|
{
"login": "bhavitvyamalik",
"id": 19718818,
"type": "User"
}
|
[] | true
|
[] |
867,325,351
| 2,262
|
NewsPH NLI dataset script fails to access test data.
|
In Newsph-NLI Dataset (#1192), it fails to access test data.
According to the script below, the download manager will download the train data when trying to download the test data.
https://github.com/huggingface/datasets/blob/2a2dd6316af2cc7fdf24e4779312e8ee0c7ed98b/datasets/newsph_nli/newsph_nli.py#L71
If you download it according to the script above, you can see that train and test receive the same data as shown below.
```python
>>> from datasets import load_dataset
>>> newsph_nli = load_dataset(path="./datasets/newsph_nli.py")
>>> newsph_nli
DatasetDict({
train: Dataset({
features: ['premise', 'hypothesis', 'label'],
num_rows: 420000
})
test: Dataset({
features: ['premise', 'hypothesis', 'label'],
num_rows: 420000
})
validation: Dataset({
features: ['premise', 'hypothesis', 'label'],
num_rows: 90000
})
})
>>> newsph_nli["train"][0]
{'hypothesis': 'Ito ang dineklara ni Atty. Romulo Macalintal, abogado ni Robredo, kaugnay ng pagsisimula ng preliminary conference ngayong hapon sa Presidential Electoral Tribunal (PET).',
'label': 1,
'premise': '"Hindi ko ugali ang mamulitika; mas gusto kong tahimik na magtrabaho. Pero sasabihin ko ito ngayon: ang tapang, lakas, at diskarte, hindi nadadaan sa mapanirang salita. Ang kailangan ng taumbayan ay tapang sa gawa," ayon kay Robredo sa inilabas nitong statement.'}
>>> newsph_nli["test"][0]
{'hypothesis': 'Ito ang dineklara ni Atty. Romulo Macalintal, abogado ni Robredo, kaugnay ng pagsisimula ng preliminary conference ngayong hapon sa Presidential Electoral Tribunal (PET).',
'label': 1,
'premise': '"Hindi ko ugali ang mamulitika; mas gusto kong tahimik na magtrabaho. Pero sasabihin ko ito ngayon: ang tapang, lakas, at diskarte, hindi nadadaan sa mapanirang salita. Ang kailangan ng taumbayan ay tapang sa gawa," ayon kay Robredo sa inilabas nitong statement.'}
```
In local, I modified the code of the source as below and got the correct result.
```python
71 test_path = os.path.join(download_path, "test.csv")
```
```python
>>> from datasets import load_dataset
>>> newsph_nli = load_dataset(path="./datasets/newsph_nli.py")
>>> newsph_nli
DatasetDict({
train: Dataset({
features: ['premise', 'hypothesis', 'label'],
num_rows: 420000
})
test: Dataset({
features: ['premise', 'hypothesis', 'label'],
num_rows: 9000
})
validation: Dataset({
features: ['premise', 'hypothesis', 'label'],
num_rows: 90000
})
})
>>> newsph_nli["train"][0]
{'hypothesis': 'Ito ang dineklara ni Atty. Romulo Macalintal, abogado ni Robredo, kaugnay ng pagsisimula ng preliminary conference ngayong hapon sa Presidential Electoral Tribunal (PET).',
'label': 1,
'premise': '"Hindi ko ugali ang mamulitika; mas gusto kong tahimik na magtrabaho. Pero sasabihin ko ito ngayon: ang tapang, lakas, at diskarte, hindi nadadaan sa mapanirang salita. Ang kailangan ng taumbayan ay tapang sa gawa," ayon kay Robredo sa inilabas nitong statement.'}
>>> newsph_nli["test"][0]
{'hypothesis': '-- JAI (@JaiPaller) September 13, 2019',
'label': 1,
'premise': 'Pinag-iingat ng Konsulado ng Pilipinas sa Dubai ang publiko, partikular ang mga donor, laban sa mga scam na gumagamit ng mga charitable organization.'}
```
I don't have experience with open source pull requests, so I suggest that you reflect them in the source.
Thank you for reading :)
|
closed
|
https://github.com/huggingface/datasets/issues/2262
| 2021-04-26T06:44:41
| 2021-04-29T09:32:03
| 2021-04-29T09:30:20
|
{
"login": "jinmang2",
"id": 37775784,
"type": "User"
}
|
[
{
"name": "dataset bug",
"color": "2edb81"
}
] | false
|
[] |
867,088,818
| 2,261
|
Improve ReadInstruction logic and update docs
|
Improve ReadInstruction logic and docs.
|
closed
|
https://github.com/huggingface/datasets/pull/2261
| 2021-04-25T19:07:26
| 2021-05-17T18:24:44
| 2021-05-17T16:48:57
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
866,961,697
| 2,260
|
GooAQ dataset added
|
@lhoestq here the dataset is stored with Git LFS. Should I add option for manual downloading of dataset using `git lfs pull` post repo cloning or can we accommodate this in the current `download_and_extract`?
|
closed
|
https://github.com/huggingface/datasets/pull/2260
| 2021-04-25T09:26:48
| 2021-05-07T08:36:17
| 2021-05-07T08:36:17
|
{
"login": "bhavitvyamalik",
"id": 19718818,
"type": "User"
}
|
[] | true
|
[] |
866,880,092
| 2,259
|
Add support for Split.ALL
|
The title says it all.
|
closed
|
https://github.com/huggingface/datasets/pull/2259
| 2021-04-25T01:45:42
| 2021-06-28T08:21:27
| 2021-06-28T08:21:27
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
866,870,588
| 2,258
|
Fix incorrect update_metadata_with_features calls in ArrowDataset
|
Fixes bugs in the `unpdate_metadata_with_features` calls (caused by changes in #2151)
|
closed
|
https://github.com/huggingface/datasets/pull/2258
| 2021-04-25T00:48:38
| 2021-04-26T17:16:30
| 2021-04-26T16:54:04
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
866,755,203
| 2,257
|
added metrics for CUAD
|
For now I've added F1, AUPR, Precision at 80% recall, and Precision at 90%. Last 3 metrics were reported in the [paper](https://arxiv.org/pdf/2103.06268.pdf). Please let me know if we require `exact_match` metric too here
|
closed
|
https://github.com/huggingface/datasets/pull/2257
| 2021-04-24T14:09:54
| 2021-04-29T09:53:38
| 2021-04-27T16:16:32
|
{
"login": "bhavitvyamalik",
"id": 19718818,
"type": "User"
}
|
[] | true
|
[] |
866,708,609
| 2,256
|
Running `datase.map` with `num_proc > 1` uses a lot of memory
|
## Describe the bug
Running `datase.map` with `num_proc > 1` leads to a tremendous memory usage that requires swapping on disk and it becomes very slow.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dstc8_datset = load_dataset("roskoN/dstc8-reddit-corpus", keep_in_memory=False)
def _prepare_sample(batch):
return {"input_ids": list(), "attention_mask": list()}
for split_name, dataset_split in list(dstc8_datset.items()):
print(f"Processing {split_name}")
encoded_dataset_split = dataset_split.map(
function=_prepare_sample,
batched=True,
num_proc=4,
remove_columns=dataset_split.column_names,
batch_size=10,
writer_batch_size=10,
keep_in_memory=False,
)
print(encoded_dataset_split)
path = f"./data/encoded_{split_name}"
encoded_dataset_split.save_to_disk(path)
```
## Expected results
Memory usage should stay within reasonable boundaries.
## Actual results
This is htop-output from running the provided script.
## Versions
```
- Datasets: 1.6.0
- Python: 3.8.8 (default, Apr 13 2021, 19:58:26)
[GCC 7.3.0]
- Platform: Linux-4.19.128-microsoft-standard-x86_64-with-glibc2.10
```
Running on WSL2
|
closed
|
https://github.com/huggingface/datasets/issues/2256
| 2021-04-24T09:56:20
| 2021-04-26T17:12:15
| 2021-04-26T17:12:15
|
{
"login": "roskoN",
"id": 8143425,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
866,242,892
| 2,255
|
Task casting for text classification & question answering
|
This PR implements task preparation for a given task, in the continuation of #2143
Task taxonomy follows 🤗 Transformers's pipelines taxonomy: https://github.com/huggingface/transformers/tree/master/src/transformers/pipelines
Edit by @lewtun:
This PR implements support for the following tasks:
* `text-classification`
* `question-answering`
The intended usage is as follows:
```python
# Load a dataset with default column names / features
ds = load_dataset("dataset_name")
# Cast column names / features to schema. Casting is defined in the dataset's `DatasetInfo`
ds = ds.prepare_for_task(task="text-classification")
# Casting can also be realised during load
ds = load_dataset("dataset_name", task="text-classification")
# We can also combine shared tasks across dataset concatenation
ds1 = load_dataset("dataset_name_1", task="text-classification")
ds2 = load_dataset("dataset_name_2", task="text-classification")
# If the tasks have the same schema, so will `ds_concat`
ds_concat = concatenate_datasets([ds1, ds2])
```
Note that the current implementation assumes that `DatasetInfo.task_templates` has been pre-defined by the user / contributor when overriding the `MyDataset(GeneratorBasedBuilder)._info` function.
As pointed out by @SBrandeis, for evaluation we'll need a way to detect which datasets are already have a compatible schema so we don't have to edit hundreds of dataset scripts. One possibility is to check if the schema features are a subset of the dataset ones, e.g.
```python
squad = load_dataset("./datasets/squad", split="train")
qa = QuestionAnswering()
schema = Features({**qa.input_schema, **qa.label_schema})
assert all(item in squad.features.items() for item in schema.items())
```
|
closed
|
https://github.com/huggingface/datasets/pull/2255
| 2021-04-23T16:00:41
| 2021-05-18T13:31:36
| 2021-05-18T13:31:35
|
{
"login": "SBrandeis",
"id": 33657802,
"type": "User"
}
|
[] | true
|
[] |
866,169,312
| 2,254
|
Update format, fingerprint and indices after add_item
|
Added fingerprint and format update wrappers + update the indices by adding the index of the newly added item in the table.
|
closed
|
https://github.com/huggingface/datasets/pull/2254
| 2021-04-23T14:31:49
| 2021-04-27T16:30:49
| 2021-04-27T16:30:48
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
866,034,321
| 2,253
|
Perform minor refactoring: use config
|
Perform minor refactoring related to `config`.
|
closed
|
https://github.com/huggingface/datasets/pull/2253
| 2021-04-23T11:45:47
| 2021-05-27T09:12:45
| 2021-04-27T15:02:59
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[
{
"name": "refactoring",
"color": "B67A40"
}
] | true
|
[] |
865,870,710
| 2,252
|
Slow dataloading with big datasets issue persists
|
Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action | Mean duration (s) |Num calls | Total time (s) | Percentage % |
------------------------------------------------------------------------------------------------------------------------------------
Total | - |_ | 517.96 | 100 % |
------------------------------------------------------------------------------------------------------------------------------------
model_backward | 0.26144 |100 | 26.144 | 5.0475 |
model_forward | 0.11123 |100 | 11.123 | 2.1474 |
get_train_batch | 0.097121 |100 | 9.7121 | 1.8751 |
```
3) Running with 600GB, datasets==1.6.0
```
Action | Mean duration (s) |Num calls | Total time (s) | Percentage % |
------------------------------------------------------------------------------------------------------------------------------------
Total | - |_ | 4563.2 | 100 % |
------------------------------------------------------------------------------------------------------------------------------------
get_train_batch | 5.1279 |100 | 512.79 | 11.237 |
model_backward | 4.8394 |100 | 483.94 | 10.605 |
model_forward | 0.12162 |100 | 12.162 | 0.26652 |
```
I see that `get_train_batch` lags when data is large. Could this be related to different issues?
I would be happy to provide necessary information to investigate.
|
closed
|
https://github.com/huggingface/datasets/issues/2252
| 2021-04-23T08:18:20
| 2024-01-26T15:10:28
| 2024-01-26T15:10:28
|
{
"login": "hwijeen",
"id": 29157715,
"type": "User"
}
|
[] | false
|
[] |
865,848,705
| 2,251
|
while running run_qa.py, ran into a value error
|
command:
python3 run_qa.py --model_name_or_path hyunwoongko/kobart --dataset_name squad_kor_v2 --do_train --do_eval --per_device_train_batch_size 8 --learning_rate 3e-5 --num_train_epochs 3 --max_seq_length 512 --doc_stride 128 --output_dir /tmp/debug_squad/
error:
ValueError: External features info don't match the dataset:
Got
{'id': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'context': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None), 'answer': {'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None), 'html_answer_start': Value(dtype='int32', id=None)}, 'url': Value(dtype='string', id=None), 'raw_html': Value(dtype='string', id=None)}
with type
struct<answer: struct<text: string, answer_start: int32, html_answer_start: int32>, context: string, id: string, question: string, raw_html: string, title: string, url: string>
but expected something like
{'answer': {'answer_start': Value(dtype='int32', id=None), 'html_answer_start': Value(dtype='int32', id=None), 'text': Value(dtype='string', id=None)}, 'context': Value(dtype='string', id=None), 'id': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None), 'raw_html': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'url': Value(dtype='string', id=None)}
with type
struct<answer: struct<answer_start: int32, html_answer_start: int32, text: string>, context: string, id: string, question: string, raw_html: string, title: string, url: string>
I didn't encounter this error 4 hours ago. any solutions for this kind of issue?
looks like gained dataset format refers to 'Data Fields', while expected refers to 'Data Instances'.
|
open
|
https://github.com/huggingface/datasets/issues/2251
| 2021-04-23T07:51:03
| 2021-04-23T07:51:03
| null |
{
"login": "nlee0212",
"id": 44570724,
"type": "User"
}
|
[] | false
|
[] |
865,402,449
| 2,250
|
some issue in loading local txt file as Dataset for run_mlm.py
|
first of all, I tried to load 3 .txt files as a dataset (sure that the directory and permission is OK.), I face with the below error.
> FileNotFoundError: [Errno 2] No such file or directory: 'c'
by removing one of the training .txt files It's fixed and although if I put all file as training it's ok
after this, my question is how could I use this defined Dataset for run_mlm.py for from scratch pretraining.
by using --train_file path_to_train_file just can use one .txt , .csv or, .json file. I tried to set my defined Dataset as --dataset_name but the below issue occurs.
> Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/datasets/load.py", line 336, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py", line 291, in cached_path
use_auth_token=download_config.use_auth_token,
File "/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py", line 621, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/dataset/dataset.py
> During handling of the above exception, another exception occurred:
> Traceback (most recent call last):
File "run_mlm.py", line 486, in <module>
main()
File "run_mlm.py", line 242, in main
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)
File "/usr/local/lib/python3.7/dist-packages/datasets/load.py", line 719, in load_dataset
use_auth_token=use_auth_token,
File "/usr/local/lib/python3.7/dist-packages/datasets/load.py", line 347, in prepare_module
combined_path, github_file_path
FileNotFoundError: Couldn't find file locally at dataset/dataset.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.6.0/datasets/dataset/dataset.py.
The file is also not present on the master branch on github.
|
closed
|
https://github.com/huggingface/datasets/issues/2250
| 2021-04-22T19:39:13
| 2022-03-30T08:29:47
| 2022-03-30T08:29:47
|
{
"login": "alighofrani95",
"id": 14968123,
"type": "User"
}
|
[] | false
|
[] |
865,257,826
| 2,249
|
Allow downloading/processing/caching only specific splits
|
Allow downloading/processing/caching only specific splits without downloading/processing/caching the other splits.
This PR implements two steps to handle only specific splits:
- it allows processing/caching only specific splits into Arrow files
- for some simple cases, it allows downloading only specific splits (which is more intricate as it depends on the user-defined method `_split_generators`)
This PR makes several assumptions:
- `DownloadConfig` contains the configuration settings for downloading
- the parameter `split` passed to `load_dataset` is just a parameter for loading (from cache), not for downloading
|
open
|
https://github.com/huggingface/datasets/pull/2249
| 2021-04-22T17:51:44
| 2022-07-06T15:19:48
| null |
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
}
] | true
|
[] |
864,853,447
| 2,248
|
Implement Dataset to JSON
|
Implement `Dataset.to_json`.
|
closed
|
https://github.com/huggingface/datasets/pull/2248
| 2021-04-22T11:46:51
| 2021-04-27T15:29:21
| 2021-04-27T15:29:20
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
}
] | true
|
[] |
864,817,520
| 2,247
|
Implement Dataset from Parquet
|
Implement instantiation of Dataset from Parquet file.
|
closed
|
https://github.com/huggingface/datasets/pull/2247
| 2021-04-22T11:01:38
| 2021-07-26T13:28:52
| 2021-07-26T13:28:51
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
}
] | true
|
[] |
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