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https://api.github.com/repos/huggingface/datasets/issues/1926 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1926/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1926/comments | https://api.github.com/repos/huggingface/datasets/issues/1926/events | https://github.com/huggingface/datasets/pull/1926 | 813,607,994 | MDExOlB1bGxSZXF1ZXN0NTc3NzI4Mjgy | 1,926 | Fix: Wiki_dpr - add missing scalar quantizer | {
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The scalar quantizer SQ8 doesn't reduce the performance of the index as shown in retrieval experiments on RAG.
The quantizer reduces the size of the index a lot but increases index building time. | {
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https://api.github.com/repos/huggingface/datasets/issues/1925 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1925/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1925/comments | https://api.github.com/repos/huggingface/datasets/issues/1925/events | https://github.com/huggingface/datasets/pull/1925 | 813,600,902 | MDExOlB1bGxSZXF1ZXN0NTc3NzIyMzc3 | 1,925 | Fix: Wiki_dpr - fix when with_embeddings is False or index_name is "no_index" | {
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"Hi @lhoestq ,\r\n\r\nI am running into an issue now when trying to run RAG. Running exactly as described [here](https://huggingface.co/facebook/rag-token-nq?fbclid=IwAR3bTfhls5U_t9DqsX2Vzb7NhtRHxJxfQ-uwFT7VuCPMZUM2AdAlKF_qkI8#usage) I get the error below. Wondering if it's related to this.\r\n\r\nRunning Transfor... | 1,614,007,426,000 | 1,614,216,828,000 | 1,614,008,168,000 | MEMBER | null | Fix the bugs noticed in #1915
There was a bug when `with_embeddings=False` where the configuration name was the same as if `with_embeddings=True`, which led the dataset builder to do bad verifications (for example it used to expect to download the embeddings for `with_embeddings=False`).
Another issue was that setting `index_name="no_index"` didn't set `with_index` to False.
I fixed both of them and added dummy data for those configurations for testing. | {
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https://api.github.com/repos/huggingface/datasets/issues/1923 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1923/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1923/comments | https://api.github.com/repos/huggingface/datasets/issues/1923/events | https://github.com/huggingface/datasets/pull/1923 | 813,363,472 | MDExOlB1bGxSZXF1ZXN0NTc3NTI0MTU0 | 1,923 | Fix save_to_disk with relative path | {
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} | [] | closed | false | null | [] | null | [] | 1,613,989,639,000 | 1,613,992,964,000 | 1,613,992,963,000 | MEMBER | null | As noticed in #1919 and #1920 the target directory was not created using `makedirs` so saving to it raises `FileNotFoundError`. For absolute paths it works but not for the good reason. This is because the target path was the same as the temporary path where in-memory data are written as an intermediary step.
I added the `makedirs` call using `fs.makedirs` in order to support remote filesystems.
I also fixed the issue with the target path being the temporary path.
I added a test case for relative paths as well for save_to_disk.
Thanks to @M-Salti for reporting and investigating | {
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https://api.github.com/repos/huggingface/datasets/issues/1921 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1921/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1921/comments | https://api.github.com/repos/huggingface/datasets/issues/1921/events | https://github.com/huggingface/datasets/pull/1921 | 812,716,042 | MDExOlB1bGxSZXF1ZXN0NTc3MDEzMDM4 | 1,921 | Standardizing datasets dtypes | {
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"@lhoestq - apologies for the multiple PRs, my previous one (#1905) got mangled due to some merge conflicts that I had trouble resolving so I just cherry-picked my changes onto a fresh branch here."
] | 1,613,858,641,000 | 1,613,987,050,000 | 1,613,987,050,000 | CONTRIBUTOR | null | This PR follows up on discussion in #1900 to have an explicit set of basic dtypes for datasets.
This moves away from str(pyarrow.DataType) as the method of choice for creating dtypes, favoring an explicit mapping to a list of supported Value dtypes.
I believe in practice this should be backward compatible, since anyone previously using Value() would only have been able to use dtypes that had an identically named pyarrow factory function, which are all explicitly supported here, with `float32` and `float64` acting as the official datasets dtypes, which resolves the tension between `double` being the pyarrow dtype and `float64` being the pyarrow type factory function. | {
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https://api.github.com/repos/huggingface/datasets/issues/1920 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1920/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1920/comments | https://api.github.com/repos/huggingface/datasets/issues/1920/events | https://github.com/huggingface/datasets/pull/1920 | 812,628,220 | MDExOlB1bGxSZXF1ZXN0NTc2OTQ5NzI2 | 1,920 | Fix save_to_disk issue | {
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"So I was curious why the issue reported at #1919 wasn't caught in [this test](https://github.com/huggingface/datasets/blob/248104c4bdb2e01c036b7578867199191fbff181/tests/test_arrow_dataset.py#L209), so I did some digging.\r\nI tried to save to a temporary directory (just like in the test), like this:\r\n```python\... | 1,613,830,959,000 | 1,613,989,811,000 | 1,613,989,811,000 | CONTRIBUTOR | null | Fixes #1919
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"Hi thanks for reporting and for proposing a fix :)\r\n\r\nI just merged a fix, feel free to try it from the master branch !",
"Closing since this has been fixed by #1923"
] | 1,613,830,690,000 | 1,614,793,227,000 | 1,614,793,227,000 | CONTRIBUTOR | null | When I try to save a dataset locally using the `save_to_disk` method I get the error:
```bash
FileNotFoundError: [Errno 2] No such file or directory: '/content/squad/train/squad-train.arrow'
```
To replicate:
1. Install `datasets` from master
2. Run this code:
```python
from datasets import load_dataset
squad = load_dataset("squad") # or any other dataset
squad.save_to_disk("squad") # error here
```
The problem is that the method is not creating a directory with the name `dataset_path` for saving the dataset in (i.e. it's not creating the *train* and *validation* directories in this case). After creating the directory the problem resolves.
I'll open a PR soon doing that and linking this issue.
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"upgraded to php 3.9.2 and it works!"
] | 1,613,772,785,000 | 1,613,774,471,000 | 1,613,774,428,000 | NONE | null | Windows 10
Php 3.6.8
when running
```
import datasets
oscar_am = datasets.load_dataset("oscar", "unshuffled_deduplicated_am")
print(oscar_am["train"][0])
```
I get the following error
```
file "C:\PYTHON\3.6.8\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 58: character maps to <undefined>
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/1916 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1916/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1916/comments | https://api.github.com/repos/huggingface/datasets/issues/1916/events | https://github.com/huggingface/datasets/pull/1916 | 812,291,984 | MDExOlB1bGxSZXF1ZXN0NTc2NjgwNjY5 | 1,916 | Remove unused py_utils objects | {
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"Hmmm this one broke master. I'm fixing it.\r\n\r\nMaybe because your branch was outdated ?",
"Sorry @lhoestq, I forgot to update the imports... :/",
"It's fine, the CI should have caught this tbh. Not sure why it did't fail"
] | 1,613,764,285,000 | 1,614,005,816,000 | 1,614,000,769,000 | MEMBER | null | Remove unused/unnecessary py_utils functions/classes. | {
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https://api.github.com/repos/huggingface/datasets/issues/1915 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1915/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1915/comments | https://api.github.com/repos/huggingface/datasets/issues/1915/events | https://github.com/huggingface/datasets/issues/1915 | 812,229,654 | MDU6SXNzdWU4MTIyMjk2NTQ= | 1,915 | Unable to download `wiki_dpr` | {
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"Thanks for reporting ! This is a bug. For now feel free to set `ignore_verifications=False` in `load_dataset`.\r\nI'm working on a fix",
"I just merged a fix :)\r\n\r\nWe'll do a patch release soon. In the meantime feel free to try it from the master branch\r\nThanks again for reporting !",
"Closing since this... | 1,613,758,292,000 | 1,614,793,248,000 | 1,614,793,248,000 | NONE | null | I am trying to download the `wiki_dpr` dataset. Specifically, I want to download `psgs_w100.multiset.no_index` with no embeddings/no index. In order to do so, I ran:
`curr_dataset = load_dataset("wiki_dpr", embeddings_name="multiset", index_name="no_index")`
However, I got the following error:
`datasets.utils.info_utils.UnexpectedDownloadedFile: {'embeddings_index'}`
I tried adding in flags `with_embeddings=False` and `with_index=False`:
`curr_dataset = load_dataset("wiki_dpr", with_embeddings=False, with_index=False, embeddings_name="multiset", index_name="no_index")`
But I got the following error:
`raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
datasets.utils.info_utils.ExpectedMoreDownloadedFiles: {‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_5’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_15’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_30’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_36’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_18’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_41’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_13’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_48’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_10’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_23’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_14’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_34’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_43’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_40’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_47’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_3’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_24’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_7’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_33’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_46’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_42’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_27’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_29’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_26’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_22’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_4’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_20’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_39’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_6’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_16’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_8’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_35’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_49’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_17’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_25’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_0’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_38’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_12’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_44’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_1’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_32’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_19’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_31’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_37’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_9’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_11’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_21’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_28’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_45’, ‘https://dl.fbaipublicfiles.com/rag/rag_multiset_embeddings/wiki_passages_2’}`
Is there anything else I need to set to download the dataset?
**UPDATE**: just running `curr_dataset = load_dataset("wiki_dpr", with_embeddings=False, with_index=False)` gives me the same error.
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https://api.github.com/repos/huggingface/datasets/issues/1913 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1913/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1913/comments | https://api.github.com/repos/huggingface/datasets/issues/1913/events | https://github.com/huggingface/datasets/pull/1913 | 812,127,307 | MDExOlB1bGxSZXF1ZXN0NTc2NTQ0NjQw | 1,913 | Add keep_linebreaks parameter to text loader | {
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"Just so I understand how it can be used in practice, do you have an example showing how to load a text dataset with this option?",
"Sure ! Here is an example:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nload_dataset(\"text\", keep_linebreaks=True, data_files=...)\r\n```\r\n\r\nI'll update the docume... | 1,613,749,425,000 | 1,613,759,772,000 | 1,613,759,771,000 | MEMBER | null | As asked in #870 and https://github.com/huggingface/transformers/issues/10269 there should be a parameter to keep the linebreaks when loading a text dataset.
cc @sgugger @jncasey | {
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https://api.github.com/repos/huggingface/datasets/issues/1912 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1912/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1912/comments | https://api.github.com/repos/huggingface/datasets/issues/1912/events | https://github.com/huggingface/datasets/pull/1912 | 812,034,140 | MDExOlB1bGxSZXF1ZXN0NTc2NDY2ODQx | 1,912 | Update: WMT - use mirror links | {
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"So much better - thank you for doing that, @lhoestq!",
"Also fixed the `uncorpus` urls for wmt19 ru-en and zh-en for https://github.com/huggingface/datasets/issues/1893",
"Thanks!\r\nCan this be merged sooner? \r\nI manually update it and it works well."
] | 1,613,742,154,000 | 1,614,174,293,000 | 1,614,174,293,000 | MEMBER | null | As asked in #1892 I created mirrors of the data hosted on statmt.org and updated the wmt scripts.
Now downloading the wmt datasets is blazing fast :)
cc @stas00 @patrickvonplaten | {
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https://api.github.com/repos/huggingface/datasets/issues/1910 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1910/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1910/comments | https://api.github.com/repos/huggingface/datasets/issues/1910/events | https://github.com/huggingface/datasets/pull/1910 | 811,697,108 | MDExOlB1bGxSZXF1ZXN0NTc2MTg0MDQ3 | 1,910 | Adding CoNLLpp dataset. | {
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"It looks like this PR now includes changes to many other files than the ones for CoNLLpp.\r\n\r\nTo fix that feel free to create another branch and another PR.\r\n\r\nThis was probably caused by a git rebase. You can avoid this issue by using git merge if you've already pushed your branch."
] | 1,613,711,550,000 | 1,614,895,367,000 | 1,614,895,367,000 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/datasets/issues/1907 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1907/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1907/comments | https://api.github.com/repos/huggingface/datasets/issues/1907/events | https://github.com/huggingface/datasets/issues/1907 | 811,520,569 | MDU6SXNzdWU4MTE1MjA1Njk= | 1,907 | DBPedia14 Dataset Checksum bug? | {
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"Hi ! :)\r\n\r\nThis looks like the same issue as https://github.com/huggingface/datasets/issues/1856 \r\nBasically google drive has quota issues that makes it inconvenient for downloading files.\r\n\r\nIf the quota of a file is exceeded, you have to wait 24h for the quota to reset (which is painful).\r\n\r\nThe er... | 1,613,687,148,000 | 1,614,036,125,000 | 1,614,036,124,000 | CONTRIBUTOR | null | Hi there!!!
I've been using successfully the DBPedia dataset (https://huggingface.co/datasets/dbpedia_14) with my codebase in the last couple of weeks, but in the last couple of days now I get this error:
```
Traceback (most recent call last):
File "./conditional_classification/basic_pipeline.py", line 178, in <module>
main()
File "./conditional_classification/basic_pipeline.py", line 128, in main
corpus.load_data(limit_train_examples_per_class=args.data_args.train_examples_per_class,
File "/home/fp/dev/conditional_classification/conditional_classification/datasets_base.py", line 83, in load_data
datasets = load_dataset(self.name, split=dataset_split)
File "/home/fp/anaconda3/envs/conditional/lib/python3.8/site-packages/datasets/load.py", line 609, in load_dataset
builder_instance.download_and_prepare(
File "/home/fp/anaconda3/envs/conditional/lib/python3.8/site-packages/datasets/builder.py", line 526, in download_and_prepare
self._download_and_prepare(
File "/home/fp/anaconda3/envs/conditional/lib/python3.8/site-packages/datasets/builder.py", line 586, in _download_and_prepare
verify_checksums(
File "/home/fp/anaconda3/envs/conditional/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 39, in verify_checksums
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbQ2Vic1kxMmZZQ1k']
```
I've seen this has happened before in other datasets as reported in #537.
I've tried clearing my cache and call again `load_dataset` but still is not working. My same codebase is successfully downloading and using other datasets (e.g. AGNews) without any problem, so I guess something has happened specifically to the DBPedia dataset in the last few days.
Can you please check if there's a problem with the checksums?
Or this is related to any other stuff? I've seen that the path in the cache for the dataset is `/home/fp/.cache/huggingface/datasets/d_bpedia14/dbpedia_14/2.0.0/a70413e39e7a716afd0e90c9e53cb053691f56f9ef5fe317bd07f2c368e8e897...` and includes `d_bpedia14` instead maybe of `dbpedia_14`. Was this maybe a bug introduced recently?
Thanks! | {
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https://api.github.com/repos/huggingface/datasets/issues/1905 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1905/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1905/comments | https://api.github.com/repos/huggingface/datasets/issues/1905/events | https://github.com/huggingface/datasets/pull/1905 | 811,384,174 | MDExOlB1bGxSZXF1ZXN0NTc1OTIxMDk1 | 1,905 | Standardizing datasets.dtypes | {
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"Also - I took a stab at updating the docs, but I'm not sure how to actually check the outputs to see if it's formatted properly."
] | 1,613,675,731,000 | 1,613,858,490,000 | 1,613,858,490,000 | CONTRIBUTOR | null | This PR was further branched off of jdy-str-to-pyarrow-parsing, so it depends on https://github.com/huggingface/datasets/pull/1900 going first for the diff to be up-to-date (I'm not sure if there's a way for me to use jdy-str-to-pyarrow-parsing as a base branch while having it appear in the pull requests here).
This moves away from `str(pyarrow.DataType)` as the method of choice for creating dtypes, favoring an explicit mapping to a list of supported Value dtypes.
I believe in practice this should be backward compatible, since anyone previously using Value() would only have been able to use dtypes that had an identically named pyarrow factory function, which are all explicitly supported here. | {
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https://api.github.com/repos/huggingface/datasets/issues/1904 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1904/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1904/comments | https://api.github.com/repos/huggingface/datasets/issues/1904/events | https://github.com/huggingface/datasets/pull/1904 | 811,260,904 | MDExOlB1bGxSZXF1ZXN0NTc1ODE4MjA0 | 1,904 | Fix to_pandas for boolean ArrayXD | {
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"Thanks!"
] | 1,613,665,846,000 | 1,613,668,203,000 | 1,613,668,201,000 | MEMBER | null | As noticed in #1887 the conversion of a dataset with a boolean ArrayXD feature types fails because of the underlying ListArray conversion to numpy requires `zero_copy_only=False`.
zero copy is available for all primitive types except booleans
see https://arrow.apache.org/docs/python/generated/pyarrow.Array.html#pyarrow.Array.to_numpy
and https://issues.apache.org/jira/browse/ARROW-2871?jql=text%20~%20%22boolean%20to_numpy%22
cc @SBrandeis | {
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https://api.github.com/repos/huggingface/datasets/issues/1903 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1903/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1903/comments | https://api.github.com/repos/huggingface/datasets/issues/1903/events | https://github.com/huggingface/datasets/pull/1903 | 811,145,531 | MDExOlB1bGxSZXF1ZXN0NTc1NzIwOTk2 | 1,903 | Initial commit for the addition of TIMIT dataset | {
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"@patrickvonplaten could you please review and help me close this PR?",
"@lhoestq Thank you so much for your comments and for patiently reviewing the code. Have _hopefully_ included all the suggested changes. Let me know if any more changes are required.\r\n\r\nSorry the code had lots of silly errors from my sid... | 1,613,658,192,000 | 1,614,591,552,000 | 1,614,591,552,000 | CONTRIBUTOR | null | Below points needs to be addressed:
- Creation of dummy dataset is failing
- Need to check on the data representation
- License is not creative commons. Copyright: Portions © 1993 Trustees of the University of Pennsylvania
Also the links (_except the download_) point to the ami corpus! ;-)
@patrickvonplaten Requesting your comments, will be happy to address them! | {
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https://api.github.com/repos/huggingface/datasets/issues/1902 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1902/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1902/comments | https://api.github.com/repos/huggingface/datasets/issues/1902/events | https://github.com/huggingface/datasets/pull/1902 | 810,931,171 | MDExOlB1bGxSZXF1ZXN0NTc1NTQwMDM1 | 1,902 | Fix setimes_2 wmt urls | {
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Some other urls were missing https | {
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https://github.com/huggingface/datasets/issues/854
https://discuss.huggingface.co/t/cannot-download-wmt16/2081
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"OK! Thank you for the review - I will follow up with a separate PR for the comments here (https://github.com/huggingface/datasets/pull/1900#discussion_r578319725)!"
] | 1,613,593,564,000 | 1,613,759,231,000 | 1,613,759,231,000 | CONTRIBUTOR | null | Should resolve https://github.com/huggingface/datasets/issues/1895
The main part of this PR adds additional parsing in `string_to_arrow` to convert the timestamp dtypes that result from `str(pa_type)` back into the pa.DataType TimestampType.
While adding unit-testing, I noticed that support for the double/float types also don't invert correctly, so I added them, which I believe would hypothetically make this section of `Value` redundant:
```
def __post_init__(self):
if self.dtype == "double": # fix inferred type
self.dtype = "float64"
if self.dtype == "float": # fix inferred type
self.dtype = "float32"
```
However, since I think Value.dtype is part of the public interface, removing that would result in a backward-incompatible change, so I didn't muck with that.
The rest of the PR consists of docstrings that I added while developing locally so I could keep track of which functions were supposed to be inverses of each other, and thought I'd include them initially in case you want to keep them around, but I'm happy to delete or remove any of them at your request! | {
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https://api.github.com/repos/huggingface/datasets/issues/1899 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1899/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1899/comments | https://api.github.com/repos/huggingface/datasets/issues/1899/events | https://github.com/huggingface/datasets/pull/1899 | 810,308,332 | MDExOlB1bGxSZXF1ZXN0NTc1MDIxMjc4 | 1,899 | Fix: ALT - fix duplicated examples in alt-parallel | {
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} | [] | closed | false | null | [] | null | [] | 1,613,577,236,000 | 1,613,582,449,000 | 1,613,582,449,000 | MEMBER | null | As noticed in #1898 by @10-zin the examples of the `alt-paralel` configurations have all the same values for the `translation` field.
This was due to a bad copy of a python dict.
This PR fixes that. | {
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https://api.github.com/repos/huggingface/datasets/issues/1898 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1898/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1898/comments | https://api.github.com/repos/huggingface/datasets/issues/1898/events | https://github.com/huggingface/datasets/issues/1898 | 810,157,251 | MDU6SXNzdWU4MTAxNTcyNTE= | 1,898 | ALT dataset has repeating instances in all splits | {
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"Thanks for reporting. This looks like a very bad issue. I'm looking into it",
"I just merged a fix, we'll do a patch release soon. Thanks again for reporting, and sorry for the inconvenience.\r\nIn the meantime you can load `ALT` using `datasets` from the master branch",
"Thanks!!! works perfectly in the blead... | 1,613,566,302,000 | 1,613,715,526,000 | 1,613,715,526,000 | NONE | null | The [ALT](https://huggingface.co/datasets/alt) dataset has all the same instances within each split :/
Seemed like a great dataset for some experiments I wanted to carry out, especially since its medium-sized, and has all splits.
Would be great if this could be fixed :)
Added a snapshot of the contents from `explore-datset` feature, for quick reference.
| {
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https://api.github.com/repos/huggingface/datasets/issues/1897 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1897/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1897/comments | https://api.github.com/repos/huggingface/datasets/issues/1897/events | https://github.com/huggingface/datasets/pull/1897 | 810,113,263 | MDExOlB1bGxSZXF1ZXN0NTc0ODU3MTIy | 1,897 | Fix PandasArrayExtensionArray conversion to native type | {
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} | [] | closed | false | null | [] | null | [] | 1,613,562,504,000 | 1,613,567,716,000 | 1,613,567,715,000 | MEMBER | null | To make the conversion to csv work in #1887 , we need PandasArrayExtensionArray used for multidimensional numpy arrays to be converted to pandas native types.
However previously pandas.core.internals.ExtensionBlock.to_native_types would fail with an PandasExtensionArray because
1. the PandasExtensionArray.isna method was wrong
2. the conversion of a PandasExtensionArray to a numpy array with dtype=object was returning a multidimensional array while pandas excepts a 1D array in this case (more info [here](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.api.extensions.ExtensionArray.html#pandas.api.extensions.ExtensionArray))
I fixed these two issues and now the conversion to native types works, and so is the export to csv.
cc @SBrandeis | {
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https://api.github.com/repos/huggingface/datasets/issues/1895 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1895/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1895/comments | https://api.github.com/repos/huggingface/datasets/issues/1895/events | https://github.com/huggingface/datasets/issues/1895 | 809,630,271 | MDU6SXNzdWU4MDk2MzAyNzE= | 1,895 | Bug Report: timestamp[ns] not recognized | {
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"Thanks for reporting !\r\n\r\nYou're right, `string_to_arrow` should be able to take `\"timestamp[ns]\"` as input and return the right pyarrow timestamp type.\r\nFeel free to suggest a fix for `string_to_arrow` and open a PR if you want to contribute ! This would be very appreciated :)\r\n\r\nTo give you more cont... | 1,613,507,884,000 | 1,613,759,231,000 | 1,613,759,231,000 | CONTRIBUTOR | null | Repro:
```
from datasets import Dataset
import pandas as pd
import pyarrow
df = pd.DataFrame(pd.date_range("2018-01-01", periods=3, freq="H"))
pyarrow.Table.from_pandas(df)
Dataset.from_pandas(df)
# Throws ValueError: Neither timestamp[ns] nor timestamp[ns]_ seems to be a pyarrow data type.
```
The factory function seems to be just "timestamp": https://arrow.apache.org/docs/python/generated/pyarrow.timestamp.html#pyarrow.timestamp
It seems like https://github.com/huggingface/datasets/blob/master/src/datasets/features.py#L36-L43 could have a little bit of additional structure for handling these cases? I'd be happy to take a shot at opening a PR if I could receive some guidance on whether parsing something like `timestamp[ns]` and resolving it to timestamp('ns') is the goal of this method.
Alternatively, if I'm using this incorrectly (e.g. is the expectation that we always provide a schema when timestamps are involved?), that would be very helpful to know as well!
```
$ pip list # only the relevant libraries/versions
datasets 1.2.1
pandas 1.0.3
pyarrow 3.0.0
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/1893 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1893/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1893/comments | https://api.github.com/repos/huggingface/datasets/issues/1893/events | https://github.com/huggingface/datasets/issues/1893 | 809,556,503 | MDU6SXNzdWU4MDk1NTY1MDM= | 1,893 | wmt19 is broken | {
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"This was also mentioned in https://github.com/huggingface/datasets/issues/488 \r\n\r\nThe bucket where is data was stored seems to be unavailable now. Maybe we can change the URL to the ones in https://conferences.unite.un.org/uncorpus/en/downloadoverview ?",
"Closing since this has been fixed by #1912"
] | 1,613,500,798,000 | 1,614,793,322,000 | 1,614,793,322,000 | CONTRIBUTOR | null | 1. Check which lang pairs we have: `--dataset_name wmt19`:
Please pick one among the available configs: ['cs-en', 'de-en', 'fi-en', 'gu-en', 'kk-en', 'lt-en', 'ru-en', 'zh-en', 'fr-de']
2. OK, let's pick `ru-en`:
`--dataset_name wmt19 --dataset_config "ru-en"`
no cookies:
```
Traceback (most recent call last):
File "./run_seq2seq.py", line 661, in <module>
main()
File "./run_seq2seq.py", line 317, in main
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/load.py", line 740, in load_dataset
builder_instance.download_and_prepare(
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/builder.py", line 572, in download_and_prepare
self._download_and_prepare(
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/builder.py", line 628, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/stas/.cache/huggingface/modules/datasets_modules/datasets/wmt19/436092de5f3faaf0fc28bc84875475b384e90a5470fa6afaee11039ceddc5052/wmt_utils.py", line 755, in _split_generators
downloaded_files = dl_manager.download_and_extract(urls_to_download)
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/download_manager.py", line 276, in download_and_extract
return self.extract(self.download(url_or_urls))
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/download_manager.py", line 191, in download
downloaded_path_or_paths = map_nested(
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/py_utils.py", line 233, in map_nested
mapped = [
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/py_utils.py", line 234, in <listcomp>
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/py_utils.py", line 190, in _single_map_nested
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/py_utils.py", line 190, in <listcomp>
mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar]
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/py_utils.py", line 172, in _single_map_nested
return function(data_struct)
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/download_manager.py", line 211, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/file_utils.py", line 274, in cached_path
output_path = get_from_cache(
File "/mnt/nvme1/code/huggingface/datasets-master/src/datasets/utils/file_utils.py", line 584, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://storage.googleapis.com/tfdataset-data/downloadataset/uncorpus/UNv1.0.en-ru.tar.gz
``` | {
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"Yes that would be awesome. Not only the download speeds are awful, but also some files are missing.\r\nWe list all the URLs in the datasets/wmt19/wmt_utils.py so we can make a script to download them all and host on S3.\r\nAlso I think most of the materials are under the CC BY-NC-SA 3.0 license (must double check)... | 1,613,500,571,000 | 1,635,231,342,000 | 1,616,673,203,000 | CONTRIBUTOR | null | Would it be possible to mirror the wmt data files under hf? Some of them take hours to download and not because of the local speed. They are all quite small datasets, just extremely slow to download.
Thank you! | {
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"Next step is going to add these two in the documentation ^^"
] | 1,613,479,099,000 | 1,613,673,757,000 | 1,613,673,754,000 | CONTRIBUTOR | null | With options to return a generator or the full dataset | {
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https://api.github.com/repos/huggingface/datasets/issues/1888 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1888/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1888/comments | https://api.github.com/repos/huggingface/datasets/issues/1888/events | https://github.com/huggingface/datasets/pull/1888 | 809,241,123 | MDExOlB1bGxSZXF1ZXN0NTc0MTM2MDU4 | 1,888 | Docs for adding new column on formatted dataset | {
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"Close #1872"
] | 1,613,475,900,000 | 1,617,112,863,000 | 1,613,476,737,000 | MEMBER | null | As mentioned in #1872 we should add in the documentation how the format gets updated when new columns are added
Close #1872 | {
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"@lhoestq I stumbled upon an interesting failure when adding tests for CSV serialization of `ArrayXD` features (see the failing unit tests in the CI)\r\n\r\nIt's due to the fact that booleans cannot be converted from arrow format to numpy without copy: https://arrow.apache.org/docs/python/generated/pyarrow.Array.ht... | 1,613,474,849,000 | 1,613,727,719,000 | 1,613,727,719,000 | CONTRIBUTOR | null | cc @thomwolf
`to_csv` supports passing either a file path or a *binary* file object
The writing is batched to avoid loading the whole table in memory | {
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"Does it make sense to make the domains as the different languages?\r\nA problem is that you need to download the datasets from the browser.\r\nOne idea would be to either contact Mozilla regarding API access to the dataset or make use of a headless browser for downloading the datasets (might be hard since we have ... | 1,613,474,170,000 | 1,615,315,891,000 | 1,615,315,891,000 | CONTRIBUTOR | null | Started filling out information about the dataset and a dataset card.
To do
Create tagging file
Update the common_voice.py file with more information | {
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@lhoestq | {
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https://api.github.com/repos/huggingface/datasets/issues/1883 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1883/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1883/comments | https://api.github.com/repos/huggingface/datasets/issues/1883/events | https://github.com/huggingface/datasets/pull/1883 | 808,750,623 | MDExOlB1bGxSZXF1ZXN0NTczNzM2NTIz | 1,883 | Add not-in-place implementations for several dataset transforms | {
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"@lhoestq I am not sure how to test `dictionary_encode_column` (in-place version was not tested before)",
"I can take a look at dictionary_encode_column tomorrow.\r\nAlthough it's likely that it doesn't work then. It was added at the beginning of the lib and never tested nor used afaik.",
"Now let's update the ... | 1,613,414,666,000 | 1,614,178,489,000 | 1,614,178,406,000 | CONTRIBUTOR | null | Should we deprecate in-place versions of such methods? | {
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https://api.github.com/repos/huggingface/datasets/issues/1881 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1881/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1881/comments | https://api.github.com/repos/huggingface/datasets/issues/1881/events | https://github.com/huggingface/datasets/pull/1881 | 808,578,200 | MDExOlB1bGxSZXF1ZXN0NTczNTk1Nzkw | 1,881 | `list_datasets()` returns a list of strings, not objects | {
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```python
>>> datasets_list = list_datasets()
>>> print(', '.join(dataset.id for dataset in datasets_list))
```
However, my understanding is that `list_datasets()` returns a list of strings rather than a list of objects. | {
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https://api.github.com/repos/huggingface/datasets/issues/1880 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1880/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1880/comments | https://api.github.com/repos/huggingface/datasets/issues/1880/events | https://github.com/huggingface/datasets/pull/1880 | 808,563,439 | MDExOlB1bGxSZXF1ZXN0NTczNTgzNjg0 | 1,880 | Update multi_woz_v22 checksums | {
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I updated them in this PR | {
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https://api.github.com/repos/huggingface/datasets/issues/1879 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1879/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1879/comments | https://api.github.com/repos/huggingface/datasets/issues/1879/events | https://github.com/huggingface/datasets/pull/1879 | 808,541,442 | MDExOlB1bGxSZXF1ZXN0NTczNTY1NDAx | 1,879 | Replace flatten_nested | {
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"Hi @lhoestq. If you agree to merge this, I will start separating the logic for NestedDataStructure.map ;)"
] | 1,613,395,780,000 | 1,613,759,714,000 | 1,613,759,714,000 | MEMBER | null | Replace `flatten_nested` with `NestedDataStructure.flatten`.
This is a first step towards having all NestedDataStructure logic as a separated concern, independent of the caller/user of the data structure.
Eventually, all checks (whether the underlying data is list, dict, etc.) will be only inside this class.
I have also generalized the flattening, and now it handles multiple levels of nesting. | {
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https://api.github.com/repos/huggingface/datasets/issues/1878 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1878/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1878/comments | https://api.github.com/repos/huggingface/datasets/issues/1878/events | https://github.com/huggingface/datasets/pull/1878 | 808,526,883 | MDExOlB1bGxSZXF1ZXN0NTczNTUyODk3 | 1,878 | Add LJ Speech dataset | {
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"Hey @anton-l,\r\n\r\nThanks a lot for the very clean integration!\r\n\r\n1) I think we should now start having \"automatic-speech-recognition\" as a label in the dataset tagger (@yjernite is it easy to add?). But we can surely add this dataset with the tag you've added and then later change the label to `asr` \r\n... | 1,613,394,642,000 | 1,613,417,981,000 | 1,613,398,689,000 | CONTRIBUTOR | null | This PR adds the LJ Speech dataset (https://keithito.com/LJ-Speech-Dataset/)
As requested by #1841
The ASR format is based on #1767
There are a couple of quirks that should be addressed:
- I tagged this dataset as `other-other-automatic-speech-recognition` and `other-other-text-to-speech` (as classified by paperswithcode). Since the number of speech datasets is about to grow, maybe these categories should be added to the main list?
- Similarly to #1767 this dataset uses only a single dummy sample to reduce the zip size (`wav`s are quite heavy). Is there a plan to allow LFS or S3 usage for dummy data in the repo?
- The dataset is distributed under the Public Domain license, which is not used anywhere else in the repo, AFAIK. Do you think Public Domain is worth adding to the tagger app as well?
Pinging @patrickvonplaten to review | {
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https://api.github.com/repos/huggingface/datasets/issues/1877 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1877/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1877/comments | https://api.github.com/repos/huggingface/datasets/issues/1877/events | https://github.com/huggingface/datasets/issues/1877 | 808,462,272 | MDU6SXNzdWU4MDg0NjIyNzI= | 1,877 | Allow concatenation of both in-memory and on-disk datasets | {
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"I started working on this. My idea is to first add the pyarrow Table wrappers InMemoryTable and MemoryMappedTable that both implement what's necessary regarding copy/pickle. Then have another wrapper that takes the concatenation of InMemoryTable/MemoryMappedTable objects.\r\n\r\nWhat's important here is that conca... | 1,613,389,186,000 | 1,616,777,518,000 | 1,616,777,518,000 | MEMBER | null | This is a prerequisite for the addition of the `add_item` feature (see #1870).
Currently there is one assumption that we would need to change: a dataset is either fully in memory (dataset._data_files is empty), or the dataset can be reloaded from disk (using the dataset._data_files).
This assumption is used for pickling for example:
- in-memory dataset can just be pickled/unpickled in-memory
- on-disk dataset can be unloaded to only keep the filepaths when pickling, and then reloaded from the disk when unpickling
Maybe let's have a design that allows a Dataset to have a Table that can be rebuilt from heterogenous sources like in-memory tables or on-disk tables ? This could also be further extended in the future
One idea would be to define a list of sources and each source implements a way to reload its corresponding pyarrow Table.
Then the dataset would be the concatenation of all these tables.
Depending on the source type, the serialization using pickle would be different. In-memory data would be copied while on-disk data would simply be replaced by the path to these data.
If you have some ideas you would like to share about the design/API feel free to do so :)
cc @albertvillanova | {
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"Thanks for reporting !\r\nThis is due to the changes made in the data files in the multiwoz repo: https://github.com/budzianowski/multiwoz/pull/59\r\nI'm opening a PR to update the checksums of the data files.",
"I just merged the fix. It will be available in the new release of `datasets` later today.\r\nYou'll ... | 1,613,330,088,000 | 1,628,100,480,000 | 1,628,100,480,000 | NONE | null | Hi, it seems that loading the multi_woz_v22 dataset gives a NonMatchingChecksumError.
To reproduce:
`dataset = load_dataset('multi_woz_v22','v2.2_active_only',split='train')`
This will give the following error:
```
raise NonMatchingChecksumError(error_msg + str(bad_urls))
datasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/dialog_acts.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_001.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_003.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_004.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_005.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_006.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_007.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_008.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_009.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_010.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_012.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_013.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_014.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_015.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_016.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/train/dialogues_017.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/dev/dialogues_001.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/dev/dialogues_002.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/test/dialogues_001.json', 'https://github.com/budzianowski/multiwoz/raw/master/data/MultiWOZ_2.2/test/dialogues_002.json']
```
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"is there a way to check errors without subscribing to CircleCI? Because they want access to private repositories when logging.",
"I think you need to be logged in to check the errors unfortunately. Feel free to create an account with bitbucket maybe if you don't want it to access your private github repos",
"I... | 1,613,235,724,000 | 1,614,854,302,000 | 1,614,854,302,000 | CONTRIBUTOR | null | Implementation of Europarl bilingual dataset from described [here](https://opus.nlpl.eu/Europarl.php).
This dataset allows to use every language pair detailed in the original dataset. The loading script manages also the small errors contained in the original dataset (in very rare cases (1 over 10M) there are some keys that references to inexistent sentences).
I chose to follow the the style of a similar dataset available in this repository: `multi_para_crawl`.
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} | [] | closed | false | null | [] | null | [] | 1,613,223,267,000 | 1,613,485,318,000 | 1,613,485,318,000 | CONTRIBUTOR | null | `iapp_wiki_qa_squad` is an extractive question answering dataset from Thai Wikipedia articles.
It is adapted from [the original iapp-wiki-qa-dataset](https://github.com/iapp-technology/iapp-wiki-qa-dataset)
to [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format, resulting in
5761/742/739 questions from 1529/191/192 articles. | {
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"Hi ! Indeed if you add a column to a formatted dataset, then the new dataset gets a new formatting in which:\r\n```\r\nnew formatted columns = (all columns - previously unformatted columns)\r\n```\r\nTherefore the new column is going to be formatted using the `torch` formatting.\r\n\r\nIf you want your new column ... | 1,613,207,675,000 | 1,617,112,905,000 | 1,617,112,905,000 | NONE | null | Hi,
thanks for the nice library. I'm in the process of creating a custom dataset, which has a mix of tensors and lists of strings. I stumbled upon an error and want to know if its a problem on my side.
I load some lists of strings and integers, then call `data.set_format("torch", columns=["some_integer_column1", "some_integer_column2"], output_all_columns=True)`. This converts the integer columns into tensors, but keeps the lists of strings as they are. I then call `map` to add a new column to my dataset, which is a **list of strings**. Once I iterate through my dataset, I get an error that the new column can't be converted into a tensor (which is probably caused by `set_format`).
Below some pseudo code:
```python
def augment_func(sample: Dict) -> Dict:
# do something
return {
"some_integer_column1" : augmented_data["some_integer_column1"], # <-- tensor
"some_integer_column2" : augmented_data["some_integer_column2"], # <-- tensor
"NEW_COLUMN": targets, # <-- list of strings
}
data = datasets.load_dataset(__file__, data_dir="...", split="train")
data.set_format("torch", columns=["some_integer_column1", "some_integer_column2"], output_all_columns=True)
augmented_dataset = data.map(augment_func, batched=False)
for sample in augmented_dataset:
print(sample) # fails
```
and the exception:
```python
Traceback (most recent call last):
File "dataset.py", line 487, in <module>
main()
File "dataset.py", line 471, in main
for sample in augmented_dataset:
File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 697, in __iter__
yield self._getitem(
File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1069, in _getitem
outputs = self._convert_outputs(
File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 890, in _convert_outputs
v = map_nested(command, v, **map_nested_kwargs)
File "lib/python3.8/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
return function(data_struct)
File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 850, in command
return [map_nested(command, i, **map_nested_kwargs) for i in x]
File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 850, in <listcomp>
return [map_nested(command, i, **map_nested_kwargs) for i in x]
File "lib/python3.8/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
return function(data_struct)
File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 850, in command
return [map_nested(command, i, **map_nested_kwargs) for i in x]
File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 850, in <listcomp>
return [map_nested(command, i, **map_nested_kwargs) for i in x]
File "lib/python3.8/site-packages/datasets/utils/py_utils.py", line 225, in map_nested
return function(data_struct)
File "lib/python3.8/site-packages/datasets/arrow_dataset.py", line 851, in command
return torch.tensor(x, **format_kwargs)
TypeError: new(): invalid data type 'str'
```
Thanks!
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"Thanks for the changes :)\r\nmerging"
] | 1,613,201,483,000 | 1,615,198,365,000 | 1,615,198,365,000 | CONTRIBUTOR | null | {
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"Thanks @lhoestq for your remarks. Yes, I agree there are still many issues to be tackled... This PR is just a starting point, so that we can discuss how Dataset should be generalized.",
"Sure ! I opened an issue #1877 so we can discuss this specific aspect :)",
"I am going to implement this consolidation step ... | 1,613,142,226,000 | 1,619,172,091,000 | 1,619,172,091,000 | MEMBER | null | Implement `Dataset.add_item`.
Close #1854. | {
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https://api.github.com/repos/huggingface/datasets/issues/1869 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1869/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1869/comments | https://api.github.com/repos/huggingface/datasets/issues/1869/events | https://github.com/huggingface/datasets/pull/1869 | 807,159,835 | MDExOlB1bGxSZXF1ZXN0NTcyNDU0NTMy | 1,869 | Remove outdated commands in favor of huggingface-cli | {
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cc @julien-c | {
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https://api.github.com/repos/huggingface/datasets/issues/1868 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1868/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1868/comments | https://api.github.com/repos/huggingface/datasets/issues/1868/events | https://github.com/huggingface/datasets/pull/1868 | 807,138,159 | MDExOlB1bGxSZXF1ZXN0NTcyNDM2MjA0 | 1,868 | Update oscar sizes | {
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} | [] | closed | false | null | [] | null | [] | 1,613,127,335,000 | 1,613,127,787,000 | 1,613,127,786,000 | MEMBER | null | This commit https://github.com/huggingface/datasets/commit/837a152e4724adc5308e2c4481908c00a8d93383 removed empty lines from the oscar deduplicated datasets. This PR updates the size of each deduplicated dataset to fix possible `NonMatchingSplitsSizesError` errors. cc @cahya-wirawan | {
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https://api.github.com/repos/huggingface/datasets/issues/1867 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1867/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1867/comments | https://api.github.com/repos/huggingface/datasets/issues/1867/events | https://github.com/huggingface/datasets/issues/1867 | 807,127,181 | MDU6SXNzdWU4MDcxMjcxODE= | 1,867 | ERROR WHEN USING SET_TRANSFORM() | {
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"Hi @alejandrocros it looks like an incompatibility with the current Trainer @sgugger \r\nIndeed currently the Trainer of `transformers` doesn't support a dataset with a transform\r\n\r\nIt looks like it comes from this line: https://github.com/huggingface/transformers/blob/f51188cbe74195c14c5b3e2e8f10c2f435f9751a/... | 1,613,126,311,000 | 1,614,607,464,000 | 1,614,168,043,000 | NONE | null | Hi, I'm trying to use dataset.set_transform(encode) as @lhoestq told me in this issue: https://github.com/huggingface/datasets/issues/1825#issuecomment-774202797
However, when I try to use Trainer from transformers with such dataset, it throws an error:
```
TypeError: __init__() missing 1 required positional argument: 'transform'
[INFO|trainer.py:357] 2021-02-12 10:18:09,893 >> The following columns in the training set don't have a corresponding argument in `AlbertForMaskedLM.forward` and have been ignored: text.
Exception in device=TPU:0: __init__() missing 1 required positional argument: 'transform'
Traceback (most recent call last):
File "/anaconda3/envs/torch-xla-1.7/lib/python3.6/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 330, in _mp_start_fn
_start_fn(index, pf_cfg, fn, args)
File "/anaconda3/envs/torch-xla-1.7/lib/python3.6/site-packages/torch_xla/distributed/xla_multiprocessing.py", line 324, in _start_fn
fn(gindex, *args)
File "/home/alejandro_vaca/transformers/examples/language-modeling/run_mlm_wwm.py", line 368, in _mp_fn
main()
File "/home/alejandro_vaca/transformers/examples/language-modeling/run_mlm_wwm.py", line 332, in main
data_collator=data_collator,
File "/anaconda3/envs/torch-xla-1.7/lib/python3.6/site-packages/transformers/trainer.py", line 286, in __init__
self._remove_unused_columns(self.train_dataset, description="training")
File "/anaconda3/envs/torch-xla-1.7/lib/python3.6/site-packages/transformers/trainer.py", line 359, in _remove_unused_columns
dataset.set_format(type=dataset.format["type"], columns=columns)
File "/home/alejandro_vaca/datasets/src/datasets/fingerprint.py", line 312, in wrapper
out = func(self, *args, **kwargs)
File "/home/alejandro_vaca/datasets/src/datasets/arrow_dataset.py", line 818, in set_format
_ = get_formatter(type, **format_kwargs)
File "/home/alejandro_vaca/datasets/src/datasets/formatting/__init__.py", line 112, in get_formatter
return _FORMAT_TYPES[format_type](**format_kwargs)
TypeError: __init__() missing 1 required positional argument: 'transform'
```
The code I'm using:
```{python}
def tokenize_function(examples):
# Remove empty lines
examples["text"] = [line for line in examples["text"] if len(line) > 0 and not line.isspace()]
return tokenizer(examples["text"], padding=padding, truncation=True, max_length=data_args.max_seq_length)
datasets.set_transform(tokenize_function)
data_collator = DataCollatorForWholeWordMask(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=datasets["train"] if training_args.do_train else None,
eval_dataset=datasets["val"] if training_args.do_eval else None,
tokenizer=tokenizer,
data_collator=data_collator,
)
```
I've installed from source, master branch.
| {
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https://api.github.com/repos/huggingface/datasets/issues/1866 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1866/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1866/comments | https://api.github.com/repos/huggingface/datasets/issues/1866/events | https://github.com/huggingface/datasets/pull/1866 | 807,017,816 | MDExOlB1bGxSZXF1ZXN0NTcyMzM3NDQ1 | 1,866 | Add dataset for Financial PhraseBank | {
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"Thanks for the feedback. All accepted and metadata regenerated."
] | 1,613,115,056,000 | 1,613,571,756,000 | 1,613,571,756,000 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/datasets/issues/1865 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1865/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1865/comments | https://api.github.com/repos/huggingface/datasets/issues/1865/events | https://github.com/huggingface/datasets/pull/1865 | 806,388,290 | MDExOlB1bGxSZXF1ZXN0NTcxODE2ODI2 | 1,865 | Updated OPUS Open Subtitles Dataset with metadata information | {
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"Hi !\r\nAbout the problems you mentioned:\r\n- Saving the infos is only done for the configurations inside the BUILDER_CONFIGS. Otherwise you would need to run the scripts on ALL language pairs, which is not what we want.\r\n- Moreover when you're on your branch, please specify the path to your local version of th... | 1,613,049,986,000 | 1,613,738,289,000 | 1,613,149,184,000 | CONTRIBUTOR | null | Close #1844
Problems:
- I ran `python datasets-cli test datasets/open_subtitles --save_infos --all_configs`, hence the change in `dataset_infos.json`, but it appears that the metadata features have not been added for all pairs. Any idea why that might be?
- Possibly related to the above, I tried doing `pip uninstall datasets && pip install -e ".[dev]"` after the changes, and loading the dataset via `load_dataset("open_subtitles", lang1='hi', lang2='it')` to check if the update worked, but the loaded dataset did not contain the metadata fields (neither in the features nor doing `next(iter(dataset['train']))`). What step(s) did I miss?
Questions:
- Is it ok to have a `classmethod` in there? I have not seen any in the few other datasets I have checked. I could make it a local method of the `_generate_examples` method, but I'd rather not duplicate the logic... | {
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https://api.github.com/repos/huggingface/datasets/issues/1864 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1864/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1864/comments | https://api.github.com/repos/huggingface/datasets/issues/1864/events | https://github.com/huggingface/datasets/issues/1864 | 806,172,843 | MDU6SXNzdWU4MDYxNzI4NDM= | 1,864 | Add Winogender Schemas | {
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"Nevermind, this one is already available on the hub under the name `'wino_bias'`: https://huggingface.co/datasets/wino_bias"
] | 1,613,031,518,000 | 1,613,031,591,000 | 1,613,031,591,000 | NONE | null | ## Adding a Dataset
- **Name:** Winogender Schemas
- **Description:** Winogender Schemas (inspired by Winograd Schemas) are minimal pairs of sentences that differ only by the gender of one pronoun in the sentence, designed to test for the presence of gender bias in automated coreference resolution systems.
- **Paper:** https://arxiv.org/abs/1804.09301
- **Data:** https://github.com/rudinger/winogender-schemas (see data directory)
- **Motivation:** Testing gender bias in automated coreference resolution systems, improve coreference resolution in general.
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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https://api.github.com/repos/huggingface/datasets/issues/1862 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1862/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1862/comments | https://api.github.com/repos/huggingface/datasets/issues/1862/events | https://github.com/huggingface/datasets/pull/1862 | 805,722,293 | MDExOlB1bGxSZXF1ZXN0NTcxMjc2ODAx | 1,862 | Fix writing GPU Faiss index | {
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I fixed that by checking the index `getDevice()` method before calling `index_gpu_to_cpu`
Close #1859 | {
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https://api.github.com/repos/huggingface/datasets/issues/1861 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1861/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1861/comments | https://api.github.com/repos/huggingface/datasets/issues/1861/events | https://github.com/huggingface/datasets/pull/1861 | 805,631,215 | MDExOlB1bGxSZXF1ZXN0NTcxMjAwNjA1 | 1,861 | Fix Limit url | {
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} | [] | closed | false | null | [] | null | [] | 1,612,971,896,000 | 1,612,973,700,000 | 1,612,973,699,000 | MEMBER | null | The test.json file of the Literal-Motion-in-Text (LiMiT) dataset was removed recently on the master branch of the repo at https://github.com/ilmgut/limit_dataset
This PR uses the previous commit sha to download the file instead, as suggested by @Paethon
Close #1836 | {
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https://api.github.com/repos/huggingface/datasets/issues/1860 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1860/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1860/comments | https://api.github.com/repos/huggingface/datasets/issues/1860/events | https://github.com/huggingface/datasets/pull/1860 | 805,510,037 | MDExOlB1bGxSZXF1ZXN0NTcxMDk4OTIz | 1,860 | Add loading from the Datasets Hub + add relative paths in download manager | {
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"I just added the steps to share a dataset on the datasets hub. It's highly inspired by the steps to share a model in the `transformers` doc.\r\n\r\nMoreover once the new huggingface_hub is released we can update the version in the setup.py. We also need to update the command to create a dataset repo in the documen... | 1,612,963,451,000 | 1,613,157,210,000 | 1,613,157,209,000 | MEMBER | null | With the new Datasets Hub on huggingface.co it's now possible to have a dataset repo with your own script and data.
For example: https://huggingface.co/datasets/lhoestq/custom_squad/tree/main contains one script and two json files.
You can load it using
```python
from datasets import load_dataset
d = load_dataset("lhoestq/custom_squad")
```
To be able to use the data files that live right next to the dataset script on the repo in the hub, I added relative paths support for the DownloadManager. For example in the repo mentioned above, there are two json files that can be downloaded via
```python
_URLS = {
"train": "train-v1.1.json",
"dev": "dev-v1.1.json",
}
downloaded_files = dl_manager.download_and_extract(_URLS)
```
To make it work, I set the `base_path` of the DownloadManager to be the parent path of the dataset script (which comes from either a local path or a remote url).
I also had to add the auth header of the requests to huggingface.co for private datasets repos. The token is fetched from [huggingface_hub](https://github.com/huggingface/huggingface_hub). | {
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https://api.github.com/repos/huggingface/datasets/issues/1859 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1859/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1859/comments | https://api.github.com/repos/huggingface/datasets/issues/1859/events | https://github.com/huggingface/datasets/issues/1859 | 805,479,025 | MDU6SXNzdWU4MDU0NzkwMjU= | 1,859 | Error "in void don't know how to serialize this type of index" when saving index to disk when device=0 (GPU) | {
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"Hi @corticalstack ! Thanks for reporting. Indeed in the recent versions of Faiss we must use `getDevice` to check if the index in on GPU.\r\n\r\nI'm opening a PR",
"I fixed this issue. It should work fine now.\r\nFeel free to try it out by installing `datasets` from source.\r\nOtherwise you can wait for the next... | 1,612,960,860,000 | 1,612,981,932,000 | 1,612,981,067,000 | NONE | null | Error serializing faiss index. Error as follows:
`Error in void faiss::write_index(const faiss::Index*, faiss::IOWriter*) at /home/conda/feedstock_root/build_artifacts/faiss-split_1612472484670/work/faiss/impl/index_write.cpp:453: don't know how to serialize this type of index`
Note:
`torch.cuda.is_available()` reports:
```
Cuda is available
cuda:0
```
Adding index, device=0 for GPU.
`dataset.add_faiss_index(column='embeddings', index_name='idx_embeddings', device=0)`
However, during a quick debug, self.faiss_index has no attr "device" when checked in` search.py, method save`, so fails to transform gpu index to cpu index. If I add index without device, index is saved OK.
```
def save(self, file: str):
"""Serialize the FaissIndex on disk"""
import faiss # noqa: F811
if (
hasattr(self.faiss_index, "device")
and self.faiss_index.device is not None
and self.faiss_index.device > -1
):
index = faiss.index_gpu_to_cpu(self.faiss_index)
else:
index = self.faiss_index
faiss.write_index(index, file)
```
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https://api.github.com/repos/huggingface/datasets/issues/1858 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1858/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1858/comments | https://api.github.com/repos/huggingface/datasets/issues/1858/events | https://github.com/huggingface/datasets/pull/1858 | 805,477,774 | MDExOlB1bGxSZXF1ZXN0NTcxMDcxNzIx | 1,858 | Clean config getenvs | {
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Remove double getenv calls and fix one issue with rarfile
cc @albertvillanova | {
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"Hi ! We're in the process of switching the community datasets to git repos, exactly like what we're doing for models.\r\nYou can find an example here:\r\nhttps://huggingface.co/datasets/lhoestq/custom_squad/tree/main\r\n\r\nWe'll update the CLI in the coming days and do a new release :)\r\n\r\nAlso cc @julien-c ma... | 1,612,953,541,000 | 1,627,967,173,000 | 1,627,967,173,000 | CONTRIBUTOR | null | Hi,
i'm trying to a upload a dataset as described [here](https://huggingface.co/docs/datasets/v1.2.0/share_dataset.html#sharing-a-community-provided-dataset). This is what happens:
```
$ datasets-cli login
$ datasets-cli upload_dataset my_dataset
About to upload file /path/to/my_dataset/dataset_infos.json to S3 under filename my_dataset/dataset_infos.json and namespace username
About to upload file /path/to/my_dataset/my_dataset.py to S3 under filename my_dataset/my_dataset.py and namespace username
Proceed? [Y/n] Y
Uploading... This might take a while if files are large
400 Client Error: Bad Request for url: https://huggingface.co/api/datasets/presign
huggingface.co migrated to a new model hosting system.
You need to upgrade to transformers v3.5+ to upload new models.
More info at https://discuss.hugginface.co or https://twitter.com/julien_c. Thank you!
```
I'm using the latest releases of datasets and transformers. | {
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"Hi @sshleifer.\r\n\r\nI am not sure of understanding the need of the `add_item` approach...\r\n\r\nBy just reading your \"Desired API\" section, I would say you could (nearly) get it with a 1-column Dataset:\r\n```python\r\ndata = {\"input_ids\": [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5])]}\r\... | 1,612,937,160,000 | 1,619,172,090,000 | 1,619,172,090,000 | MEMBER | null | I'm trying to integrate `huggingface/datasets` functionality into `fairseq`, which requires (afaict) being able to build a dataset through an `add_item` method, such as https://github.com/pytorch/fairseq/blob/master/fairseq/data/indexed_dataset.py#L318, as opposed to loading all the text into arrow, and then `dataset.map(binarizer)`.
Is this possible at the moment? Is there an example? I'm happy to use raw `pa.Table` but not sure whether it will support uneven length entries.
### Desired API
```python
import numpy as np
tokenized: List[np.NDArray[np.int64]] = [np.array([4,4,2]), np.array([8,6,5,5,2]), np.array([3,3,31,5])
def build_dataset_from_tokenized(tokenized: List[np.NDArray[int]]) -> Dataset:
"""FIXME"""
dataset = EmptyDataset()
for t in tokenized: dataset.append(t)
return dataset
ds = build_dataset_from_tokenized(tokenized)
assert (ds[0] == np.array([4,4,2])).all()
```
### What I tried
grep, google for "add one entry at a time", "datasets.append"
### Current Code
This code achieves the same result but doesn't fit into the `add_item` abstraction.
```python
dataset = load_dataset('text', data_files={'train': 'train.txt'})
tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_length=4096)
def tokenize_function(examples):
ids = tokenizer(examples['text'], return_attention_mask=False)['input_ids']
return {'input_ids': [x[1:] for x in ids]}
ds = dataset.map(tokenize_function, batched=True, num_proc=4, remove_columns=['text'], load_from_cache_file=not overwrite_cache)
print(ds['train'][0]) => np array
```
Thanks in advance! | {
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https://api.github.com/repos/huggingface/datasets/issues/1853 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1853/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1853/comments | https://api.github.com/repos/huggingface/datasets/issues/1853/events | https://github.com/huggingface/datasets/pull/1853 | 804,791,166 | MDExOlB1bGxSZXF1ZXN0NTcwNTAwMjc4 | 1,853 | Configure library root logger at the module level | {
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} | [] | closed | false | null | [] | null | [] | 1,612,894,272,000 | 1,612,960,354,000 | 1,612,960,354,000 | MEMBER | null | Configure library root logger at the datasets.logging module level (singleton-like).
By doing it this way:
- we are sure configuration is done only once: module level code is only runned once
- no need of global variable
- no need of threading lock | {
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https://api.github.com/repos/huggingface/datasets/issues/1852 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1852/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1852/comments | https://api.github.com/repos/huggingface/datasets/issues/1852/events | https://github.com/huggingface/datasets/pull/1852 | 804,633,033 | MDExOlB1bGxSZXF1ZXN0NTcwMzY3NTU1 | 1,852 | Add Arabic Speech Corpus | {
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https://api.github.com/repos/huggingface/datasets/issues/1851 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1851/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1851/comments | https://api.github.com/repos/huggingface/datasets/issues/1851/events | https://github.com/huggingface/datasets/pull/1851 | 804,523,174 | MDExOlB1bGxSZXF1ZXN0NTcwMjc2MTk5 | 1,851 | set bert_score version dependency | {
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https://api.github.com/repos/huggingface/datasets/issues/1850 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1850/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1850/comments | https://api.github.com/repos/huggingface/datasets/issues/1850/events | https://github.com/huggingface/datasets/pull/1850 | 804,412,249 | MDExOlB1bGxSZXF1ZXN0NTcwMTg0MDAx | 1,850 | Add cord 19 dataset | {
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"Cleaned-up version of previous PR: https://github.com/huggingface/datasets/pull/1129",
"@lhoestq FYI",
"Before merging I might tweak a little bit the dummy data to avoid having to check if the `document_parses` and `embeddings` directories exist or not. I'll do that later today",
"Looks all good now ! Thanks... | 1,612,866,128,000 | 1,612,883,786,000 | 1,612,883,786,000 | CONTRIBUTOR | null | Initial version only reading the metadata in CSV.
### Checklist:
- [x] Create the dataset script /datasets/my_dataset/my_dataset.py using the template
- [x] Fill the _DESCRIPTION and _CITATION variables
- [x] Implement _infos(), _split_generators() and _generate_examples()
- [x] Make sure that the BUILDER_CONFIGS class attribute is filled with the different configurations of the dataset and that the BUILDER_CONFIG_CLASS is specified if there is a custom config class.
- [x] Generate the metadata file dataset_infos.json for all configurations
- [x] Generate the dummy data dummy_data.zip files to have the dataset script tested and that they don't weigh too much (<50KB)
- [x] Add the dataset card README.md using the template and at least fill the tags
- [x] Both tests for the real data and the dummy data pass.
### Extras:
- [x] add more metadata
- [x] add full text
- [x] add pre-computed document embedding | {
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https://api.github.com/repos/huggingface/datasets/issues/1849 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1849/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1849/comments | https://api.github.com/repos/huggingface/datasets/issues/1849/events | https://github.com/huggingface/datasets/issues/1849 | 804,292,971 | MDU6SXNzdWU4MDQyOTI5NzE= | 1,849 | Add TIMIT | {
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"@patrickvonplaten Could you please help me with how the output text has to be represented in the data? TIMIT has Words, Phonemes and texts. Also has lot on info on the speaker and the dialect. Could you please help me? An example of how to arrange it would be super helpful!\r\n\r\n",
"Hey @vrindaprabhu - sure I'... | 1,612,855,781,000 | 1,615,787,977,000 | 1,615,787,977,000 | MEMBER | null | ## Adding a Dataset
- **Name:** *TIMIT*
- **Description:** *The TIMIT corpus of read speech has been designed to provide speech data for the acquisition of acoustic-phonetic knowledge and for the development and evaluation of automatic speech recognition systems*
- **Paper:** *Homepage*: http://groups.inf.ed.ac.uk/ami/corpus/ / *Wikipedia*: https://en.wikipedia.org/wiki/TIMIT
- **Data:** *https://deepai.org/dataset/timit*
- **Motivation:** Important speech dataset
If interested in tackling this issue, feel free to tag @patrickvonplaten
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
| {
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https://api.github.com/repos/huggingface/datasets/issues/1848 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1848/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1848/comments | https://api.github.com/repos/huggingface/datasets/issues/1848/events | https://github.com/huggingface/datasets/pull/1848 | 803,826,506 | MDExOlB1bGxSZXF1ZXN0NTY5Njg5ODU1 | 1,848 | Refactoring: Create config module | {
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This could be seen as a Pythonic singleton-like approach. Eventually a config instance class might be created. | {
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https://api.github.com/repos/huggingface/datasets/issues/1847 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1847/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1847/comments | https://api.github.com/repos/huggingface/datasets/issues/1847/events | https://github.com/huggingface/datasets/pull/1847 | 803,824,694 | MDExOlB1bGxSZXF1ZXN0NTY5Njg4NDY0 | 1,847 | [Metrics] Add word error metric metric | {
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"Feel free to merge once the CI is all green ;)"
] | 1,612,809,675,000 | 1,612,893,201,000 | 1,612,893,201,000 | MEMBER | null | This PR adds the word error rate metric to datasets.
WER: https://en.wikipedia.org/wiki/Word_error_rate
for speech recognition. WER is the main metric used in ASR.
`jiwer` seems to be a solid library (see https://github.com/asteroid-team/asteroid/pull/329#discussion_r525158939) | {
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"First I was thinking of the dict, which makes sense for .download, mapping URL to downloaded path. However does this make sense for .extract, mapping the downloaded path to the extracted path? I ask this because the user did not chose the downloaded path, so this is completely unknown for them...",
"There could ... | 1,612,808,082,000 | 1,614,262,218,000 | 1,614,262,218,000 | MEMBER | null | Make accessible the file paths downloaded/extracted by DownloadManager.
Close #1831.
The approach:
- I set these paths as DownloadManager attributes: these are DownloadManager's concerns
- To access to these from DatasetBuilder, I set the DownloadManager instance as DatasetBuilder attribute: object composition | {
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https://api.github.com/repos/huggingface/datasets/issues/1845 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1845/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1845/comments | https://api.github.com/repos/huggingface/datasets/issues/1845/events | https://github.com/huggingface/datasets/pull/1845 | 803,714,493 | MDExOlB1bGxSZXF1ZXN0NTY5NTk2MTIz | 1,845 | Enable logging propagation and remove logging handler | {
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"Thank you @lhoestq. This logging configuration makes more sense to me.\r\n\r\nOnce propagation is allowed, the end-user can customize logging behavior and add custom handlers to the proper top logger in the hierarchy.\r\n\r\nAnd I also agree with following the best practices and removing any custom handlers:\r\n- ... | 1,612,801,333,000 | 1,612,880,558,000 | 1,612,880,557,000 | MEMBER | null | We used to have logging propagation disabled because of this issue: https://github.com/tensorflow/tensorflow/issues/26691
But since it's now fixed we should re-enable it. This is important to keep the default logging behavior for users, and propagation is also needed for pytest fixtures as asked in #1826
I also removed the handler that was added since, according to the logging [documentation](https://docs.python.org/3/howto/logging.html#configuring-logging-for-a-library):
> It is strongly advised that you do not add any handlers other than NullHandler to your library’s loggers. This is because the configuration of handlers is the prerogative of the application developer who uses your library. The application developer knows their target audience and what handlers are most appropriate for their application: if you add handlers ‘under the hood’, you might well interfere with their ability to carry out unit tests and deliver logs which suit their requirements.
It could have been useful if we wanted to have a custom formatter for the logging but I think it's more important to keep the logging as default to not interfere with the users' logging management.
Therefore I also removed the two methods `datasets.logging.enable_default_handler` and `datasets.logging.disable_default_handler`.
cc @albertvillanova this should let you use capsys/caplog in pytest
cc @LysandreJik @sgugger if you want to do the same in `transformers` | {
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https://api.github.com/repos/huggingface/datasets/issues/1844 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1844/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1844/comments | https://api.github.com/repos/huggingface/datasets/issues/1844/events | https://github.com/huggingface/datasets/issues/1844 | 803,588,125 | MDU6SXNzdWU4MDM1ODgxMjU= | 1,844 | Update Open Subtitles corpus with original sentence IDs | {
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"Hi ! You're right this can can useful.\r\nThis should be easy to add, so feel free to give it a try if you want to contribute :)\r\nI think we just need to add it to the _generate_examples method of the OpenSubtitles dataset builder [here](https://github.com/huggingface/datasets/blob/master/datasets/open_subtitles... | 1,612,792,513,000 | 1,613,151,538,000 | 1,613,151,538,000 | CONTRIBUTOR | null | Hi! It would be great if you could add the original sentence ids to [Open Subtitles](https://huggingface.co/datasets/open_subtitles).
I can think of two reasons: first, it's possible to gather sentences for an entire document (the original ids contain media id, subtitle file id and sentence id), therefore somewhat allowing for document-level machine translation (and other document-level stuff which could be cool to have); second, it's possible to have parallel sentences in multiple languages, as they share the same ids across bitexts.
I think I should tag @abhishekkrthakur as he's the one who added it in the first place.
Thanks! | {
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... | closed | false | null | [] | null | [] | 1,612,790,546,000 | 1,615,787,942,000 | 1,615,787,942,000 | MEMBER | null | ## Adding a Dataset
- **Name:** *ljspeech*
- **Description:** *This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours.
The texts were published between 1884 and 1964, and are in the public domain. The audio was recorded in 2016-17 by the LibriVox project and is also in the public domain.)*
- **Paper:** *Homepage*: https://keithito.com/LJ-Speech-Dataset/
- **Data:** *https://keithito.com/LJ-Speech-Dataset/*
- **Motivation:** Important speech dataset
- **TFDatasets Implementation**: https://www.tensorflow.org/datasets/catalog/ljspeech
If interested in tackling this issue, feel free to tag @patrickvonplaten
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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"I have started working on adding this dataset.",
"Hey @BirgerMoell - awesome that you started working on Common Voice. Common Voice is a bit special since, there is no direct download link to download the data. In these cases we usually consider two options:\r\n\r\n1) Find a hacky solution to extract the downloa... | 1,612,790,465,000 | 1,632,474,311,000 | 1,615,787,781,000 | MEMBER | null | ## Adding a Dataset
- **Name:** *common voice*
- **Description:** *Mozilla Common Voice Dataset*
- **Paper:** Homepage: https://voice.mozilla.org/en/datasets
- **Data:** https://voice.mozilla.org/en/datasets
- **Motivation:** Important speech dataset
- **TFDatasets Implementation**: https://www.tensorflow.org/datasets/catalog/common_voice
If interested in tackling this issue, feel free to tag @patrickvonplaten
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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https://api.github.com/repos/huggingface/datasets/issues/1836 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1836/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1836/comments | https://api.github.com/repos/huggingface/datasets/issues/1836/events | https://github.com/huggingface/datasets/issues/1836 | 803,531,837 | MDU6SXNzdWU4MDM1MzE4Mzc= | 1,836 | test.json has been removed from the limit dataset repo (breaks dataset) | {
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"Thanks for the heads up ! I'm opening a PR to fix that"
] | 1,612,788,353,000 | 1,612,973,698,000 | 1,612,973,698,000 | NONE | null | https://github.com/huggingface/datasets/blob/16042b233dbff2a7585110134e969204c69322c3/datasets/limit/limit.py#L51
The URL is not valid anymore since test.json has been removed in master for some reason. Directly referencing the last commit works:
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https://api.github.com/repos/huggingface/datasets/issues/1834 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1834/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1834/comments | https://api.github.com/repos/huggingface/datasets/issues/1834/events | https://github.com/huggingface/datasets/pull/1834 | 803,517,094 | MDExOlB1bGxSZXF1ZXN0NTY5NDMzNDA4 | 1,834 | Fixes base_url of limit dataset | {
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"OK, apparently it is a lot more complicated than simply changing the URL? Going to make an issue."
] | 1,612,787,195,000 | 1,612,788,170,000 | 1,612,788,170,000 | NONE | null | `test.json` is not available in the master branch of the repository anymore. Linking to a specific commit. | {
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"@lhoestq Thanks for the suggestions! I agree with all of them. Should I accept them one by one or can I accept them all at once? When I try to load the whole diff GitHub is complaining and it does no render them well (probably my browser?) 😅 ",
"I just merged the tables as suggested 😄 . However I noticed somet... | 1,612,748,389,000 | 1,613,138,965,000 | 1,613,138,904,000 | CONTRIBUTOR | null | I added more information and completed the dataset card for OSCAR which was started by @lhoestq in his previous [PR](https://github.com/huggingface/datasets/pull/1824). | {
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https://api.github.com/repos/huggingface/datasets/issues/1832 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1832/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1832/comments | https://api.github.com/repos/huggingface/datasets/issues/1832/events | https://github.com/huggingface/datasets/issues/1832 | 802,880,897 | MDU6SXNzdWU4MDI4ODA4OTc= | 1,832 | Looks like nokogumbo is up-to-date now, so this is no longer needed. | {
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__Originally posted by @dependabot in https://github.com/discourse/discourse/pull/11373#issuecomment-738993432__ | {
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https://api.github.com/repos/huggingface/datasets/issues/1831 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1831/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1831/comments | https://api.github.com/repos/huggingface/datasets/issues/1831/events | https://github.com/huggingface/datasets/issues/1831 | 802,868,854 | MDU6SXNzdWU4MDI4Njg4NTQ= | 1,831 | Some question about raw dataset download info in the project . | {
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"Hi ! The `dl_manager` is a `DownloadManager` object and is responsible for downloading the raw data files.\r\nIt is used by dataset builders in their `_split_generators` method to download the raw data files that are necessary to build the datasets splits.\r\n\r\nThe `Conll2003` class is a dataset builder, and so ... | 1,612,676,016,000 | 1,614,262,218,000 | 1,614,262,218,000 | NONE | null | Hi , i review the code in
https://github.com/huggingface/datasets/blob/master/datasets/conll2003/conll2003.py
in the _split_generators function is the truly logic of download raw datasets with dl_manager
and use Conll2003 cls by use import_main_class in load_dataset function
My question is that , with this logic it seems that i can not have the raw dataset download location
in variable in downloaded_files in _split_generators.
If someone also want use huggingface datasets as raw dataset downloader,
how can he retrieve the raw dataset download path from attributes in
datasets.dataset_dict.DatasetDict ? | {
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https://api.github.com/repos/huggingface/datasets/issues/1829 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1829/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1829/comments | https://api.github.com/repos/huggingface/datasets/issues/1829/events | https://github.com/huggingface/datasets/pull/1829 | 802,693,600 | MDExOlB1bGxSZXF1ZXN0NTY4NzgzNjA5 | 1,829 | Add Tweet Eval Dataset | {
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Notes:
1. I have excluded `mapping.txt` from the dataset at it only contained the name mappings, which are already present in the ClassLabels.
2. I have also exluded the textual names for the emojis mentioned in the [mapping](https://github.com/cardiffnlp/tweeteval/blob/main/datasets/emoji/mapping.txt).
3. I do not understand @abhishekkrthakur's example generator on #1407. Maybe he was trying to build up on code from some other dataset.
Requesting @lhoestq to review. | {
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https://api.github.com/repos/huggingface/datasets/issues/1828 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1828/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1828/comments | https://api.github.com/repos/huggingface/datasets/issues/1828/events | https://github.com/huggingface/datasets/pull/1828 | 802,449,234 | MDExOlB1bGxSZXF1ZXN0NTY4NTkwNDM2 | 1,828 | Add CelebA Dataset | {
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"Hi @gchhablani! Thanks for all the contributions! We definitely want more image datasets, but Face datasets are tricky in general, in this one includes predicting attributes such as Attractiveness, Gender, or Race, which can be pretty problematic.\r\n\r\nWould you be up for starting with only object classification... | 1,612,556,455,000 | 1,613,657,827,000 | 1,613,657,827,000 | CONTRIBUTOR | null | Trying to add CelebA Dataset.
Need help with testing. Loading examples takes a lot of time so I am unable to generate the `dataset_infos.json` and unable to test. Also, need help with creating `dummy_data.zip`.
Additionally, trying to load a few examples using `load_dataset('./datasets/celeb_a',split='train[10:20]')` still loads all the examples (doesn't stop at 10). | {
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https://api.github.com/repos/huggingface/datasets/issues/1827 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1827/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1827/comments | https://api.github.com/repos/huggingface/datasets/issues/1827/events | https://github.com/huggingface/datasets/issues/1827 | 802,353,974 | MDU6SXNzdWU4MDIzNTM5NzQ= | 1,827 | Regarding On-the-fly Data Loading | {
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"Possible duplicate\r\n\r\n#1776 https://github.com/huggingface/datasets/issues/\r\n\r\nreally looking PR for this feature",
"Hi @acul3 \r\n\r\nIssue #1776 talks about doing on-the-fly data pre-processing, which I think is solved in the next release as mentioned in the issue #1825. I also look forward to using t... | 1,612,547,028,000 | 1,613,656,516,000 | 1,613,656,516,000 | CONTRIBUTOR | null | Hi,
I was wondering if it is possible to load images/texts as a batch during the training process, without loading the entire dataset on the RAM at any given point.
Thanks,
Gunjan | {
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https://api.github.com/repos/huggingface/datasets/issues/1826 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1826/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1826/comments | https://api.github.com/repos/huggingface/datasets/issues/1826/events | https://github.com/huggingface/datasets/pull/1826 | 802,074,744 | MDExOlB1bGxSZXF1ZXN0NTY4Mjc4OTI2 | 1,826 | Print error message with filename when malformed CSV | {
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Close #1821 | {
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"Hi ! Looks related to #861 \r\n\r\nYou are right: tokenizing a dataset using map takes a lot of space since it can store `input_ids` but also `token_type_ids`, `attention_mask` and `special_tokens_mask`. Moreover if your tokenization function returns python integers then by default they'll be stored as int64 which... | 1,612,523,210,000 | 1,617,113,041,000 | 1,615,887,840,000 | NONE | null | Hi,
I'm trying to use datasets library to load a 187GB dataset of pure text, with the intention of building a Language Model. The problem is that from the 187GB it goes to some TB when processed by Datasets. First of all, I think the pre-tokenizing step (with tokenizer.map()) is not really thought for datasets this big, but for fine-tuning datasets, as this process alone takes so much time, usually in expensive machines (due to the need of tpus - gpus) which is not being used for training. It would possibly be more efficient in such cases to tokenize each batch at training time (receive batch - tokenize batch - train with batch), so that the whole time the machine is up it's being used for training.
Moreover, the pyarrow objects created from a 187 GB datasets are huge, I mean, we always receive OOM, or No Space left on device errors when only 10-12% of the dataset has been processed, and only that part occupies 2.1TB in disk, which is so many times the disk usage of the pure text (and this doesn't make sense, as tokenized texts should be lighter than pure texts).
Any suggestions?? | {
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https://api.github.com/repos/huggingface/datasets/issues/1824 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1824/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1824/comments | https://api.github.com/repos/huggingface/datasets/issues/1824/events | https://github.com/huggingface/datasets/pull/1824 | 802,048,281 | MDExOlB1bGxSZXF1ZXN0NTY4MjU3MTU3 | 1,824 | Add OSCAR dataset card | {
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"Hi @lhoestq! When are you planning to release the version with this dataset?\r\n\r\nBTW: What a huge README file :astonished:",
"Next week !",
"Closing in favor of #1833"
] | 1,612,521,026,000 | 1,620,239,054,000 | 1,612,783,833,000 | MEMBER | null | I started adding the dataset card for OSCAR !
For now it's just basic info for all the different configurations in `Dataset Structure`.
In particular the Data Splits section tells how may samples there are for each config. The Data Instances section show an example for each config, and it also shows the size in MB. Since the Data Instances section is very long the user has to click to expand the info. I was able to generate it thanks to the tools made by @madlag and @yjernite :D
Cc @pjox could you help me with the other sections ? (Dataset Description, Dataset Creation, Considerations for Using the Data, Additional Information)
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https://api.github.com/repos/huggingface/datasets/issues/1823 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1823/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1823/comments | https://api.github.com/repos/huggingface/datasets/issues/1823/events | https://github.com/huggingface/datasets/pull/1823 | 802,042,181 | MDExOlB1bGxSZXF1ZXN0NTY4MjUyMjIx | 1,823 | Add FewRel Dataset | {
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"Hi @lhoestq,\r\n\r\nSorry for the late response. What do you mean when you say \"adding names to default config\"? Should I handle \"pid2name\" in the same config as \"default\"?",
"Yes I was thinking of having the pid2name field available in the default configuration (and therefore only have one config). What d... | 1,612,520,523,000 | 1,614,599,780,000 | 1,614,594,099,000 | CONTRIBUTOR | null | Hi,
This PR closes this [Card](https://github.com/huggingface/datasets/projects/1#card-53285184) and Issue #1757.
I wasn't sure how to add `pid2name` along with the dataset so I added it as a separate configuration. For each (head, tail, tokens) triplet, I have created one example. I have added the dictionary key as `"relation"` in the dataset. Additionally, for `pubmed_unsupervised`, I kept `"relation":""` in the dictionary.
Please recommend better alternatives, if any.
Thanks,
Gunjan | {
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https://api.github.com/repos/huggingface/datasets/issues/1822 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1822/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1822/comments | https://api.github.com/repos/huggingface/datasets/issues/1822/events | https://github.com/huggingface/datasets/pull/1822 | 802,003,835 | MDExOlB1bGxSZXF1ZXN0NTY4MjIxMzIz | 1,822 | Add Hindi Discourse Analysis Natural Language Inference Dataset | {
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"Could you also run `make style` to fix the CI check on code formatting ?",
"@lhoestq completed and resolved all comments."
] | 1,612,517,454,000 | 1,613,383,059,000 | 1,613,383,059,000 | CONTRIBUTOR | null | # Dataset Card for Hindi Discourse Analysis Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- HomePage : https://github.com/midas-research/hindi-nli-data
- Paper : https://www.aclweb.org/anthology/2020.aacl-main.71
- Point of Contact : https://github.com/midas-research/hindi-nli-data
### Dataset Summary
- Dataset for Natural Language Inference in Hindi Language. Hindi Discourse Analysis (HDA) Dataset consists of textual-entailment pairs.
- Each row of the Datasets if made up of 4 columns - Premise, Hypothesis, Label and Topic.
- Premise and Hypothesis is written in Hindi while Entailment_Label is in English.
- Entailment_label is of 2 types - entailed and not-entailed.
- Entailed means that hypotheis can be inferred from premise and not-entailed means vice versa
- Dataset can be used to train models for Natural Language Inference tasks in Hindi Language.
### Supported Tasks and Leaderboards
- Natural Language Inference for Hindi
### Languages
- Dataset is in Hindi
## Dataset Structure
- Data is structured in TSV format.
- train, test and dev files are in seperate files
### Dataset Instances
An example of 'train' looks as follows.
```
{'hypothesis': 'यह एक वर्णनात्मक कथन है।', 'label': 1, 'premise': 'जैसे उस का सारा चेहरा अपना हो और आँखें किसी दूसरे की जो चेहरे पर पपोटों के पीछे महसूर कर दी गईं।', 'topic': 1}
```
### Data Fields
- Each row contatins 4 columns - premise, hypothesis, label and topic.
### Data Splits
- Train : 31892
- Valid : 9460
- Test : 9970
## Dataset Creation
- We employ a recasting technique from Poliak et al. (2018a,b) to convert publicly available Hindi Discourse Analysis classification datasets in Hindi and pose them as TE problems
- In this recasting process, we build template hypotheses for each class in the label taxonomy
- Then, we pair the original annotated sentence with each of the template hypotheses to create TE samples.
- For more information on the recasting process, refer to paper https://www.aclweb.org/anthology/2020.aacl-main.71
### Source Data
Source Dataset for the recasting process is the BBC Hindi Headlines Dataset(https://github.com/NirantK/hindi2vec/releases/tag/bbc-hindi-v0.1)
#### Initial Data Collection and Normalization
- Initial Data was collected by members of MIDAS Lab from Hindi Websites. They crowd sourced the data annotation process and selected two random stories from our corpus and had the three annotators work on them independently and classify each sentence based on the discourse mode.
- Please refer to this paper for detailed information: https://www.aclweb.org/anthology/2020.lrec-1.149/
- The Discourse is further classified into "Argumentative" , "Descriptive" , "Dialogic" , "Informative" and "Narrative" - 5 Clases.
#### Who are the source language producers?
Please refer to this paper for detailed information: https://www.aclweb.org/anthology/2020.lrec-1.149/
### Annotations
#### Annotation process
Annotation process has been described in Dataset Creation Section.
#### Who are the annotators?
Annotation is done automatically by machine and corresponding recasting process.
### Personal and Sensitive Information
No Personal and Sensitive Information is mentioned in the Datasets.
## Considerations for Using the Data
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Discussion of Biases
No known bias exist in the dataset.
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Other Known Limitations
No other known limitations . Size of data may not be enough to train large models
## Additional Information
Pls refer to this link: https://github.com/midas-research/hindi-nli-data
### Dataset Curators
It is written in the repo : https://github.com/midas-research/hindi-nli-data that
- This corpus can be used freely for research purposes.
- The paper listed below provide details of the creation and use of the corpus. If you use the corpus, then please cite the paper.
- If interested in commercial use of the corpus, send email to midas@iiitd.ac.in.
- If you use the corpus in a product or application, then please credit the authors and Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi appropriately. Also, if you send us an email, we will be thrilled to know about how you have used the corpus.
- Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India disclaims any responsibility for the use of the corpus and does not provide technical support. However, the contact listed above will be happy to respond to queries and clarifications.
- Rather than redistributing the corpus, please direct interested parties to this page
- Please feel free to send us an email:
- with feedback regarding the corpus.
- with information on how you have used the corpus.
- if interested in having us analyze your data for natural language inference.
- if interested in a collaborative research project.
### Licensing Information
Copyright (C) 2019 Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi (MIDAS, IIIT-Delhi).
Pls contact authors for any information on the dataset.
### Citation Information
```
@inproceedings{uppal-etal-2020-two,
title = "Two-Step Classification using Recasted Data for Low Resource Settings",
author = "Uppal, Shagun and
Gupta, Vivek and
Swaminathan, Avinash and
Zhang, Haimin and
Mahata, Debanjan and
Gosangi, Rakesh and
Shah, Rajiv Ratn and
Stent, Amanda",
booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.aacl-main.71",
pages = "706--719",
abstract = "An NLP model{'}s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource languages like English. To address scarcity of data in low-resource languages such as Hindi, we use data recasting to create NLI datasets for four existing text classification datasets. Through experiments, we show that our recasted dataset is devoid of statistical irregularities and spurious patterns. We further study the consistency in predictions of the textual entailment models and propose a consistency regulariser to remove pairwise-inconsistencies in predictions. We propose a novel two-step classification method which uses textual-entailment predictions for classification task. We further improve the performance by using a joint-objective for classification and textual entailment. We therefore highlight the benefits of data recasting and improvements on classification performance using our approach with supporting experimental results.",
}
```
### Contributions
Thanks to [@avinsit123](https://github.com/avinsit123) for adding this dataset.
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"Hi!\r\n\r\nThank you for reporting this issue. I agree that the information about the exception should be more clear and explicit.\r\n\r\nI could take on this issue.\r\n\r\nOn the meantime, as you can see from the exception stack trace, HF Datasets uses pandas to read the CSV files. You can pass arguments to `pand... | 1,612,486,143,000 | 1,612,892,367,000 | 1,612,892,367,000 | NONE | null | I find when I process many files, i.e.
```
train_files = glob.glob('rain*.csv')
validation_files = glob.glob(validation*.csv')
datasets = load_dataset("csv", data_files=dict(train=train_files, validation=validation_files))
```
I sometimes encounter an error due to one of the files being misformed (i.e. no data, or a comma in a field that isn't quoted, etc).
For example, this is the tail of an exception which I suspect is due to a stray comma.
> File "pandas/_libs/parsers.pyx", line 756, in pandas._libs.parsers.TextReader.read
> File "pandas/_libs/parsers.pyx", line 783, in pandas._libs.parsers.TextReader._read_low_memory
> File "pandas/_libs/parsers.pyx", line 827, in pandas._libs.parsers.TextReader._read_rows
> File "pandas/_libs/parsers.pyx", line 814, in pandas._libs.parsers.TextReader._tokenize_rows
> File "pandas/_libs/parsers.pyx", line 1951, in pandas._libs.parsers.raise_parser_error
> pandas.errors.ParserError: Error tokenizing data. C error: Expected 2 fields in line 559, saw 3
It would be nice if the exception trace contained the name of the file being processed (I have 250 separate files!) | {
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https://api.github.com/repos/huggingface/datasets/issues/1820 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1820/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1820/comments | https://api.github.com/repos/huggingface/datasets/issues/1820/events | https://github.com/huggingface/datasets/pull/1820 | 801,529,936 | MDExOlB1bGxSZXF1ZXN0NTY3ODI4OTg1 | 1,820 | Add metrics usage examples and tests | {
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} | [] | closed | false | null | [] | null | [] | 1,612,463,030,000 | 1,612,533,601,000 | 1,612,533,600,000 | MEMBER | null | All metrics finally have usage examples and proper fast + slow tests :)
I added examples of usage for every metric, and I use doctest to make sure they all work as expected.
For "slow" metrics such as bert_score or bleurt which require to download + run a transformer model, the download + forward pass are only done in the slow test.
In the fast test on the other hand, the download + forward pass are monkey patched.
Metrics that need to be installed from github are not added to setup.py because it prevents uploading the `datasets` package to pypi.
An additional-test-requirements.txt file is used instead. This file also include `comet` in order to not have to resolve its *impossible* dependencies.
Also `comet` is not tested on windows because one of its dependencies (fairseq) can't be installed in the CI for some reason. | {
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https://api.github.com/repos/huggingface/datasets/issues/1819 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1819/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1819/comments | https://api.github.com/repos/huggingface/datasets/issues/1819/events | https://github.com/huggingface/datasets/pull/1819 | 801,448,670 | MDExOlB1bGxSZXF1ZXN0NTY3NzYyMzI2 | 1,819 | Fixed spelling `S3Fileystem` to `S3FileSystem` | {
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Wrong `S3Fileystem`
Right `S3FileSystem` | {
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https://api.github.com/repos/huggingface/datasets/issues/1816 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1816/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1816/comments | https://api.github.com/repos/huggingface/datasets/issues/1816/events | https://github.com/huggingface/datasets/pull/1816 | 800,660,995 | MDExOlB1bGxSZXF1ZXN0NTY3MTExMjEx | 1,816 | Doc2dial rc update to latest version | {
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"- update data loader and readme for latest version 1.0.1"
] | 1,612,382,934,000 | 1,613,402,124,000 | 1,613,401,473,000 | CONTRIBUTOR | null | {
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https://api.github.com/repos/huggingface/datasets/issues/1815 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1815/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1815/comments | https://api.github.com/repos/huggingface/datasets/issues/1815/events | https://github.com/huggingface/datasets/pull/1815 | 800,610,017 | MDExOlB1bGxSZXF1ZXN0NTY3MDY3NjU1 | 1,815 | Add CCAligned Multilingual Dataset | {
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"Hi !\r\n\r\nWe already have some datasets that can have many many configurations possible.\r\nTo be able to support that, we allow to subclass BuilderConfig to add as many additional parameters as you may need.\r\nThis way users can load any language they want. For example the [bible_para](https://github.com/huggi... | 1,612,378,792,000 | 1,614,601,983,000 | 1,614,594,981,000 | CONTRIBUTOR | null | Hello,
I'm trying to add [CCAligned Multilingual Dataset](http://www.statmt.org/cc-aligned/). This has the potential to close #1756.
This dataset has two types - Document-Pairs, and Sentence-Pairs.
The datasets are huge, so I won't be able to test all of them. At the same time, a user might only want to download one particular language and not all. To provide this feature, `load_dataset`'s `**config_kwargs` should allow some random keyword args, in this case -`language_code`. This will be needed before the dataset is downloaded and extracted.
I'm expecting the usage to be something like -
`load_dataset('ccaligned_multilingual','documents',language_code='en_XX-af_ZA')`. Ofcourse, at a later stage we can provide just two character language codes. This also has an issue where one language has multiple files (`my_MM` and `my_MM_zaw` on the link), but before that the required functionality must be added to `load_dataset`.
It would be great if someone could either tell me an alternative way to do this, or point me to where changes need to be made, if any, apart from the `BuilderConfig` definition.
Additionally, I believe the tests will also have to be modified if this change is made, since it would not be possible to test for any random keyword arguments.
A decent way to go about this would be to provide all the options in a list/dictionary for `language_code` and use that to test the arguments. In essence, this is similar to the pre-trained checkpoint dictionary as `transformers`. That means writing dataset specific tests, or adding something new to dataset generation script to make it easier for everyone to add keyword arguments without having to worry about the tests.
Thanks,
Gunjan
Requesting @lhoestq / @yjernite to review. | {
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https://api.github.com/repos/huggingface/datasets/issues/1814 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1814/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1814/comments | https://api.github.com/repos/huggingface/datasets/issues/1814/events | https://github.com/huggingface/datasets/pull/1814 | 800,516,236 | MDExOlB1bGxSZXF1ZXN0NTY2OTg4NTI1 | 1,814 | Add Freebase QA Dataset | {
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"Hi @lhoestq \r\n\r\nThanks for approving. Request you to close PR #1435 as well."
] | 1,612,371,469,000 | 1,612,468,071,000 | 1,612,455,708,000 | CONTRIBUTOR | null | Closes PR #1435. Fixed issues with PR #1809.
Requesting @lhoestq to review. | {
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https://api.github.com/repos/huggingface/datasets/issues/1813 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1813/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1813/comments | https://api.github.com/repos/huggingface/datasets/issues/1813/events | https://github.com/huggingface/datasets/pull/1813 | 800,435,973 | MDExOlB1bGxSZXF1ZXN0NTY2OTIxNDcz | 1,813 | Support future datasets | {
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} | [] | closed | false | null | [] | null | [] | 1,612,366,009,000 | 1,612,521,228,000 | 1,612,521,227,000 | MEMBER | null | If a dataset is available at the version of the local installation of `datasets` (e.g. 1.2.0), then loading this dataset means loading the script at this version.
However when trying to load a dataset that is only available on master, currently users have to specify `script_version="master"` in `load_dataset` to make it work.
However we could automatically get the dataset from master instead in this case.
I added this feature in this PR.
I also added a warning if a dataset is not available at the version of the local installation of `datasets` but is loaded from master:
```python
>>> load_dataset("silicone", "dyda_da")
Couldn't find file locally at silicone/silicone.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.2.0/datasets/silicone/silicone.py.
The file was picked from the master branch on github instead at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/silicone/silicone.py.
Downloading and preparing dataset silicone/dyda_da (download: 8.46 MiB, generated: 9.39 MiB, post-processed: Unknown size, total: 17.86 MiB) to /Users/quentinlhoest/.cache/huggingface/datasets/silicone/dyda_da/1.0.0/d41d8c0b73c6df035b1369c45774418f0051163ea689b5502b8bda783adf6342...
...
```
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https://api.github.com/repos/huggingface/datasets/issues/1812 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1812/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1812/comments | https://api.github.com/repos/huggingface/datasets/issues/1812/events | https://github.com/huggingface/datasets/pull/1812 | 799,379,178 | MDExOlB1bGxSZXF1ZXN0NTY2MDMxODIy | 1,812 | Add CIFAR-100 Dataset | {
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"Hi @lhoestq,\r\nI have updated with the changes from the review.",
"Thanks for approving :)"
] | 1,612,279,379,000 | 1,612,782,618,000 | 1,612,780,746,000 | CONTRIBUTOR | null | Adding CIFAR-100 Dataset. | {
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https://api.github.com/repos/huggingface/datasets/issues/1811 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1811/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1811/comments | https://api.github.com/repos/huggingface/datasets/issues/1811/events | https://github.com/huggingface/datasets/issues/1811 | 799,211,060 | MDU6SXNzdWU3OTkyMTEwNjA= | 1,811 | Unable to add Multi-label Datasets | {
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"Thanks for adding this dataset! As far as I know `supervised_keys` is mostly a holdover from TFDS, but isn't really used, so feel free to drop it (@lhoestq or @thomwolf correct me if I'm wrong). It definitely shouldn't be blocking :) ",
"I can confirm that it comes from TFDS and is not used at the moment.",
"... | 1,612,266,656,000 | 1,613,657,791,000 | 1,613,657,791,000 | CONTRIBUTOR | null | I am trying to add [CIFAR-100](https://www.cs.toronto.edu/~kriz/cifar.html) dataset. The dataset contains two labels per image - `fine label` and `coarse label`. Using just one label in supervised keys as
`supervised_keys=("img", "fine_label")` raises no issue. But trying `supervised_keys=("img", "fine_label","coarse_label")` leads to this error :
```python
Traceback (most recent call last):
File "test_script.py", line 2, in <module>
d = load_dataset('./datasets/cifar100')
File "~/datasets/src/datasets/load.py", line 668, in load_dataset
**config_kwargs,
File "~/datasets/src/datasets/builder.py", line 896, in __init__
super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)
File "~/datasets/src/datasets/builder.py", line 247, in __init__
info.update(self._info())
File "~/.cache/huggingface/modules/datasets_modules/datasets/cifar100/61d2489b2d4a4abc34201432541b7380984ec714e290817d9a1ee318e4b74e0f/cifar100.py", line 79, in _info
citation=_CITATION,
File "<string>", line 19, in __init__
File "~/datasets/src/datasets/info.py", line 136, in __post_init__
self.supervised_keys = SupervisedKeysData(*self.supervised_keys)
TypeError: __init__() takes from 1 to 3 positional arguments but 4 were given
```
Is there a way I can fix this?
Also, what does adding `supervised_keys` do? Is it necessary? How would I specify `supervised_keys` for a multi-input, multi-label dataset?
Thanks,
Gunjan | {
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https://api.github.com/repos/huggingface/datasets/issues/1809 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1809/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1809/comments | https://api.github.com/repos/huggingface/datasets/issues/1809/events | https://github.com/huggingface/datasets/pull/1809 | 799,059,141 | MDExOlB1bGxSZXF1ZXN0NTY1NzY4ODQz | 1,809 | Add FreebaseQA dataset | {
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"Hi ! It looks like this PR contains changes about other datasets than freebase_qa such as DuoRC.\r\n\r\nCan you remove these changes please ?",
"Hi @lhoestq,\r\n\r\nI think this happened because of rebasing. I'm unable to remove the duorc commit from the branch. GEM, Arabic sarcasm datasets are also there. I ca... | 1,612,254,953,000 | 1,612,372,505,000 | 1,612,370,586,000 | CONTRIBUTOR | null | Adding FreebaseQA dataset suggested in PR #1435 with minor edits. Also closes that PR.
Requesting @lhoestq to review. | {
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https://api.github.com/repos/huggingface/datasets/issues/1807 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1807/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1807/comments | https://api.github.com/repos/huggingface/datasets/issues/1807/events | https://github.com/huggingface/datasets/pull/1807 | 798,823,591 | MDExOlB1bGxSZXF1ZXN0NTY1NTczNzU5 | 1,807 | Adding an aggregated dataset for the GEM benchmark | {
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"Nice !"
] | 1,612,226,393,000 | 1,612,306,121,000 | 1,612,289,218,000 | MEMBER | null | This dataset gathers modified versions of several other conditional text generation datasets which together make up the shared task for the Generation Evaluation and Metrics workshop (think GLUE for text generation)
The changes from the original datasets are detailed in the Dataset Cards on the GEM website, which are linked to in this dataset card.
cc @sebastianGehrmann
| {
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https://api.github.com/repos/huggingface/datasets/issues/1806 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1806/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1806/comments | https://api.github.com/repos/huggingface/datasets/issues/1806/events | https://github.com/huggingface/datasets/pull/1806 | 798,607,869 | MDExOlB1bGxSZXF1ZXN0NTY1Mzk0ODIz | 1,806 | Update details to MLSUM dataset | {
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"Thanks!"
] | 1,612,204,512,000 | 1,612,205,188,000 | 1,612,205,181,000 | CONTRIBUTOR | null | Update details to MLSUM dataset | {
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https://api.github.com/repos/huggingface/datasets/issues/1805 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1805/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1805/comments | https://api.github.com/repos/huggingface/datasets/issues/1805/events | https://github.com/huggingface/datasets/issues/1805 | 798,498,053 | MDU6SXNzdWU3OTg0OTgwNTM= | 1,805 | can't pickle SwigPyObject objects when calling dataset.get_nearest_examples from FAISS index | {
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"Hi ! Indeed we used to require mapping functions to be picklable with `pickle` or `dill` in order to cache the resulting datasets. And FAISS indexes are not picklable unfortunately.\r\n\r\nBut since #1703 this is no longer required (the caching will simply be disabled). This change will be available in the next re... | 1,612,196,057,000 | 1,615,041,166,000 | 1,615,041,166,000 | CONTRIBUTOR | null | So, I have the following instances in my dataset
```
{'question': 'An astronomer observes that a planet rotates faster after a meteorite impact. Which is the most likely effect of
this increase in rotation?',
'answer': 'C',
'example_id': 'ARCCH_Mercury_7175875',
'options':[{'option_context': 'One effect of increased amperage in the planetary world (..)', 'option_id': 'A', 'option_text': 'Planetary density will decrease.'},
(...)]}
```
The `options` value is always an list with 4 options, each one is a dict with `option_context`; `option_id` and `option_text`.
I would like to overwrite the `option_context` of each instance of my dataset for a dpr result that I am developing. Then, I trained a model already and save it in a FAISS index
```
dpr_dataset = load_dataset(
"text",
data_files=ARC_CORPUS_TEXT,
cache_dir=CACHE_DIR,
split="train[:100%]",
)
dpr_dataset.load_faiss_index("embeddings", f"{ARC_CORPUS_FAISS}")
torch.set_grad_enabled(False)
```
Then, as a processor of my dataset, I created a map function that calls the `dpr_dataset` for each _option_
```
def generate_context(example):
question_text = example['question']
for option in example['options']:
question_with_option = question_text + " " + option['option_text']
tokenize_text = question_tokenizer(question_with_option, return_tensors="pt").to(device)
question_embed = (
question_encoder(**tokenize_text)
)[0][0].cpu().numpy()
_, retrieved_examples = dpr_dataset.get_nearest_examples(
"embeddings", question_embed, k=10
)
# option["option_context"] = retrieved_examples["text"]
# option["option_context"] = " ".join(option["option_context"]).strip()
#result_dict = {
# 'example_id': example['example_id'],
# 'answer': example['answer'],
# 'question': question_text,
#options': example['options']
# }
return example
```
I intentionally commented on this portion of the code.
But when I call the `map` method, `ds_with_context = dataset.map(generate_context,load_from_cache_file=False)`
It calls the following error:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-55-75a458ce205c> in <module>
----> 1 ds_with_context = dataset.map(generate_context,load_from_cache_file=False)
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/dataset_dict.py in map(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc)
301 num_proc=num_proc,
302 )
--> 303 for k, dataset in self.items()
304 }
305 )
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/dataset_dict.py in <dictcomp>(.0)
301 num_proc=num_proc,
302 )
--> 303 for k, dataset in self.items()
304 }
305 )
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)
1257 fn_kwargs=fn_kwargs,
1258 new_fingerprint=new_fingerprint,
-> 1259 update_data=update_data,
1260 )
1261 else:
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
155 }
156 # apply actual function
--> 157 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
158 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
159 # re-apply format to the output
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in wrapper(*args, **kwargs)
156 kwargs_for_fingerprint["fingerprint_name"] = fingerprint_name
157 kwargs[fingerprint_name] = update_fingerprint(
--> 158 self._fingerprint, transform, kwargs_for_fingerprint
159 )
160
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in update_fingerprint(fingerprint, transform, transform_args)
103 for key in sorted(transform_args):
104 hasher.update(key)
--> 105 hasher.update(transform_args[key])
106 return hasher.hexdigest()
107
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in update(self, value)
55 def update(self, value):
56 self.m.update(f"=={type(value)}==".encode("utf8"))
---> 57 self.m.update(self.hash(value).encode("utf-8"))
58
59 def hexdigest(self):
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in hash(cls, value)
51 return cls.dispatch[type(value)](cls, value)
52 else:
---> 53 return cls.hash_default(value)
54
55 def update(self, value):
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/fingerprint.py in hash_default(cls, value)
44 @classmethod
45 def hash_default(cls, value):
---> 46 return cls.hash_bytes(dumps(value))
47
48 @classmethod
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/utils/py_utils.py in dumps(obj)
387 file = StringIO()
388 with _no_cache_fields(obj):
--> 389 dump(obj, file)
390 return file.getvalue()
391
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/utils/py_utils.py in dump(obj, file)
359 def dump(obj, file):
360 """pickle an object to a file"""
--> 361 Pickler(file, recurse=True).dump(obj)
362 return
363
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/dill/_dill.py in dump(self, obj)
452 raise PicklingError(msg)
453 else:
--> 454 StockPickler.dump(self, obj)
455 stack.clear() # clear record of 'recursion-sensitive' pickled objects
456 return
/usr/lib/python3.7/pickle.py in dump(self, obj)
435 if self.proto >= 4:
436 self.framer.start_framing()
--> 437 self.save(obj)
438 self.write(STOP)
439 self.framer.end_framing()
/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
502 f = self.dispatch.get(t)
503 if f is not None:
--> 504 f(self, obj) # Call unbound method with explicit self
505 return
506
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/datasets/utils/py_utils.py in save_function(pickler, obj)
554 dill._dill._create_function,
555 (obj.__code__, globs, obj.__name__, obj.__defaults__, obj.__closure__, obj.__dict__, fkwdefaults),
--> 556 obj=obj,
557 )
558 else:
/usr/lib/python3.7/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)
636 else:
637 save(func)
--> 638 save(args)
639 write(REDUCE)
640
/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
502 f = self.dispatch.get(t)
503 if f is not None:
--> 504 f(self, obj) # Call unbound method with explicit self
505 return
506
/usr/lib/python3.7/pickle.py in save_tuple(self, obj)
784 write(MARK)
785 for element in obj:
--> 786 save(element)
787
788 if id(obj) in memo:
/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
502 f = self.dispatch.get(t)
503 if f is not None:
--> 504 f(self, obj) # Call unbound method with explicit self
505 return
506
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj)
939 # we only care about session the first pass thru
940 pickler._session = False
--> 941 StockPickler.save_dict(pickler, obj)
942 log.info("# D2")
943 return
/usr/lib/python3.7/pickle.py in save_dict(self, obj)
854
855 self.memoize(obj)
--> 856 self._batch_setitems(obj.items())
857
858 dispatch[dict] = save_dict
/usr/lib/python3.7/pickle.py in _batch_setitems(self, items)
880 for k, v in tmp:
881 save(k)
--> 882 save(v)
883 write(SETITEMS)
884 elif n:
/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
547
548 # Save the reduce() output and finally memoize the object
--> 549 self.save_reduce(obj=obj, *rv)
550
551 def persistent_id(self, obj):
/usr/lib/python3.7/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)
660
661 if state is not None:
--> 662 save(state)
663 write(BUILD)
664
/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
502 f = self.dispatch.get(t)
503 if f is not None:
--> 504 f(self, obj) # Call unbound method with explicit self
505 return
506
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj)
939 # we only care about session the first pass thru
940 pickler._session = False
--> 941 StockPickler.save_dict(pickler, obj)
942 log.info("# D2")
943 return
/usr/lib/python3.7/pickle.py in save_dict(self, obj)
854
855 self.memoize(obj)
--> 856 self._batch_setitems(obj.items())
857
858 dispatch[dict] = save_dict
/usr/lib/python3.7/pickle.py in _batch_setitems(self, items)
880 for k, v in tmp:
881 save(k)
--> 882 save(v)
883 write(SETITEMS)
884 elif n:
/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
502 f = self.dispatch.get(t)
503 if f is not None:
--> 504 f(self, obj) # Call unbound method with explicit self
505 return
506
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj)
939 # we only care about session the first pass thru
940 pickler._session = False
--> 941 StockPickler.save_dict(pickler, obj)
942 log.info("# D2")
943 return
/usr/lib/python3.7/pickle.py in save_dict(self, obj)
854
855 self.memoize(obj)
--> 856 self._batch_setitems(obj.items())
857
858 dispatch[dict] = save_dict
/usr/lib/python3.7/pickle.py in _batch_setitems(self, items)
885 k, v = tmp[0]
886 save(k)
--> 887 save(v)
888 write(SETITEM)
889 # else tmp is empty, and we're done
/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
547
548 # Save the reduce() output and finally memoize the object
--> 549 self.save_reduce(obj=obj, *rv)
550
551 def persistent_id(self, obj):
/usr/lib/python3.7/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)
660
661 if state is not None:
--> 662 save(state)
663 write(BUILD)
664
/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
502 f = self.dispatch.get(t)
503 if f is not None:
--> 504 f(self, obj) # Call unbound method with explicit self
505 return
506
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj)
939 # we only care about session the first pass thru
940 pickler._session = False
--> 941 StockPickler.save_dict(pickler, obj)
942 log.info("# D2")
943 return
/usr/lib/python3.7/pickle.py in save_dict(self, obj)
854
855 self.memoize(obj)
--> 856 self._batch_setitems(obj.items())
857
858 dispatch[dict] = save_dict
/usr/lib/python3.7/pickle.py in _batch_setitems(self, items)
880 for k, v in tmp:
881 save(k)
--> 882 save(v)
883 write(SETITEMS)
884 elif n:
/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
547
548 # Save the reduce() output and finally memoize the object
--> 549 self.save_reduce(obj=obj, *rv)
550
551 def persistent_id(self, obj):
/usr/lib/python3.7/pickle.py in save_reduce(self, func, args, state, listitems, dictitems, obj)
660
661 if state is not None:
--> 662 save(state)
663 write(BUILD)
664
/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
502 f = self.dispatch.get(t)
503 if f is not None:
--> 504 f(self, obj) # Call unbound method with explicit self
505 return
506
~/.cache/pypoetry/virtualenvs/masters-utTTC0p8-py3.7/lib/python3.7/site-packages/dill/_dill.py in save_module_dict(pickler, obj)
939 # we only care about session the first pass thru
940 pickler._session = False
--> 941 StockPickler.save_dict(pickler, obj)
942 log.info("# D2")
943 return
/usr/lib/python3.7/pickle.py in save_dict(self, obj)
854
855 self.memoize(obj)
--> 856 self._batch_setitems(obj.items())
857
858 dispatch[dict] = save_dict
/usr/lib/python3.7/pickle.py in _batch_setitems(self, items)
885 k, v = tmp[0]
886 save(k)
--> 887 save(v)
888 write(SETITEM)
889 # else tmp is empty, and we're done
/usr/lib/python3.7/pickle.py in save(self, obj, save_persistent_id)
522 reduce = getattr(obj, "__reduce_ex__", None)
523 if reduce is not None:
--> 524 rv = reduce(self.proto)
525 else:
526 reduce = getattr(obj, "__reduce__", None)
TypeError: can't pickle SwigPyObject objects
```
Which I have no idea how to solve/deal with it
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https://api.github.com/repos/huggingface/datasets/issues/1804 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/1804/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/1804/comments | https://api.github.com/repos/huggingface/datasets/issues/1804/events | https://github.com/huggingface/datasets/pull/1804 | 798,483,881 | MDExOlB1bGxSZXF1ZXN0NTY1MjkzMTc3 | 1,804 | Add SICK dataset | {
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} | [] | closed | false | null | [] | null | [] | 1,612,195,064,000 | 1,612,547,188,000 | 1,612,540,165,000 | CONTRIBUTOR | null | Adds the SICK dataset (http://marcobaroni.org/composes/sick.html).
Closes #1772.
Edit: also closes #1632, which is the original issue requesting the dataset. The newer one is a duplicate. | {
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