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timestamp[s]date 2020-04-14 10:18:02
2025-07-23 08:04:53
| updated_at
timestamp[s]date 2020-04-27 16:04:17
2025-07-23 18:53:44
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timestamp[s]date 2020-04-14 12:01:40
2025-07-23 16:44:42
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|---|---|---|---|---|---|---|---|---|---|---|---|---|
1,227,592,826
| 4,290
|
Update paper link in medmcqa dataset card
|
Updating readme in medmcqa dataset.
|
closed
|
https://github.com/huggingface/datasets/pull/4290
| 2022-05-06T08:52:51
| 2022-09-30T11:51:28
| 2022-09-30T11:49:07
|
{
"login": "monk1337",
"id": 17107749,
"type": "User"
}
|
[
{
"name": "dataset contribution",
"color": "0e8a16"
}
] | true
|
[] |
1,226,821,732
| 4,288
|
Add missing `faiss` import to fix https://github.com/huggingface/datasets/issues/4287
|
This PR fixes the issue recently mentioned in https://github.com/huggingface/datasets/issues/4287 🤗
|
closed
|
https://github.com/huggingface/datasets/pull/4288
| 2022-05-05T15:21:49
| 2022-05-10T12:55:06
| 2022-05-10T12:09:48
|
{
"login": "alvarobartt",
"id": 36760800,
"type": "User"
}
|
[] | true
|
[] |
1,226,806,652
| 4,287
|
"NameError: name 'faiss' is not defined" on `.add_faiss_index` when `device` is not None
|
## Describe the bug
When using `datasets` to calculate the FAISS indices of a dataset, the exception `NameError: name 'faiss' is not defined` is triggered when trying to calculate those on a device (GPU), so `.add_faiss_index(..., device=0)` fails with that exception.
All that assuming that `datasets` is properly installed and `faiss-gpu` too, as well as all the CUDA drivers required.
## Steps to reproduce the bug
```python
# Sample code to reproduce the bug
from transformers import DPRContextEncoder, DPRContextEncoderTokenizer
import torch
torch.set_grad_enabled(False)
ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")
ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")
from datasets import load_dataset
ds = load_dataset('crime_and_punish', split='train[:100]')
ds_with_embeddings = ds.map(lambda example: {'embeddings': ctx_encoder(**ctx_tokenizer(example["line"], return_tensors="pt"))[0][0].numpy()})
ds_with_embeddings.add_faiss_index(column='embeddings', device=0) # default `device=None`
```
## Expected results
A new column named `embeddings` in the dataset that we're adding the index to.
## Actual results
An exception is triggered with the following message `NameError: name 'faiss' is not defined`.
## Environment info
- `datasets` version: 2.1.0
- Platform: Linux-5.13.0-1022-azure-x86_64-with-glibc2.31
- Python version: 3.9.12
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
|
closed
|
https://github.com/huggingface/datasets/issues/4287
| 2022-05-05T15:09:45
| 2022-05-10T13:53:19
| 2022-05-10T13:53:19
|
{
"login": "alvarobartt",
"id": 36760800,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
1,226,758,621
| 4,286
|
Add Lahnda language tag
|
This language is present in [Wikimedia's WIT](https://huggingface.co/datasets/wikimedia/wit_base) dataset.
|
closed
|
https://github.com/huggingface/datasets/pull/4286
| 2022-05-05T14:34:20
| 2022-05-10T12:10:04
| 2022-05-10T12:02:38
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
1,226,374,831
| 4,285
|
Update LexGLUE README.md
|
Update the leaderboard based on the latest results presented in the ACL 2022 version of the article.
|
closed
|
https://github.com/huggingface/datasets/pull/4285
| 2022-05-05T08:36:50
| 2022-05-05T13:39:04
| 2022-05-05T13:33:35
|
{
"login": "iliaschalkidis",
"id": 1626984,
"type": "User"
}
|
[] | true
|
[] |
1,226,200,727
| 4,284
|
Issues in processing very large datasets
|
## Describe the bug
I'm trying to add a feature called "subgraph" to CNN/DM dataset (modifications on run_summarization.py of Huggingface Transformers script) --- I'm not quite sure if I'm doing it the right way, though--- but the main problem appears when the training starts where the error ` [OSError: [Errno 12] Cannot allocate memory]` appears. I suppose this problem roots in RAM issues and how the dataset is loaded during training, but I have no clue of what I can do to fix it. Observing the dataset's cache directory, I see that it takes ~600GB of memory and that's why I believe special care is needed when loading it into the memory.
Here are my modifications to `run_summarization.py` code.
```
# loading pre-computed dictionary where keys are 'id' of article and values are corresponding subgraph
graph_data_train = get_graph_data('train')
graph_data_validation = get_graph_data('val')
...
...
with training_args.main_process_first(desc="train dataset map pre-processing"):
train_dataset = train_dataset.map(
preprocess_function_train,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on train dataset",
)
```
And here is the modified preprocessed function:
```
def preprocess_function_train(examples):
inputs, targets, sub_graphs, ids = [], [], [], []
for i in range(len(examples[text_column])):
if examples[text_column][i] is not None and examples[summary_column][i] is not None:
# if examples['doc_id'][i] in graph_data.keys():
inputs.append(examples[text_column][i])
targets.append(examples[summary_column][i])
sub_graphs.append(graph_data_train[examples['id'][i]])
ids.append(examples['id'][i])
inputs = [prefix + inp for inp in inputs]
model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, padding=padding, truncation=True,
sub_graphs=sub_graphs, ids=ids)
# Setup the tokenizer for targets
with tokenizer.as_target_tokenizer():
labels = tokenizer(targets, max_length=max_target_length, padding=padding, truncation=True)
# If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
# padding in the loss.
if padding == "max_length" and data_args.ignore_pad_token_for_loss:
labels["input_ids"] = [
[(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"]
]
model_inputs["labels"] = labels["input_ids"]
return model_inputs
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.1.0
- Platform: Linux Ubuntu
- Python version: 3.6
- PyArrow version: 6.0.1
|
closed
|
https://github.com/huggingface/datasets/issues/4284
| 2022-05-05T05:01:09
| 2023-07-25T15:12:38
| 2023-07-25T15:12:38
|
{
"login": "sajastu",
"id": 10419055,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
1,225,686,988
| 4,283
|
Fix filesystem docstring
|
This PR untangles the `S3FileSystem` docstring so the [parameters](https://huggingface.co/docs/datasets/master/en/package_reference/main_classes#parameters) are properly displayed.
|
closed
|
https://github.com/huggingface/datasets/pull/4283
| 2022-05-04T17:42:42
| 2022-05-06T16:32:02
| 2022-05-06T06:22:17
|
{
"login": "stevhliu",
"id": 59462357,
"type": "User"
}
|
[] | true
|
[] |
1,225,616,545
| 4,282
|
Don't do unnecessary list type casting to avoid replacing None values by empty lists
|
In certain cases, `None` values are replaced by empty lists when casting feature types.
It happens every time you cast an array of nested lists like [None, [0, 1, 2, 3]] to a different type (to change the integer precision for example). In this case you'd get [[], [0, 1, 2, 3]] for example. This issue comes from PyArrow, see the discussion in https://github.com/huggingface/datasets/issues/3676
This issue also happens when no type casting is needed, because casting is supposed to be a no-op in this case. But as https://github.com/huggingface/datasets/issues/3676 shown, it's not the case and `None` are replaced by empty lists even if we cast to the exact same type.
In this PR I just workaround this bug in the case where no type casting is needed. In particular, I only call `pa.ListArray.from_arrays` only when necessary.
I also added a warning when some `None` are effectively replaced by empty lists. I wanted to raise an error in this case, but maybe we should wait a major update to do so
This PR fixes this particular case, that is occurring in `run_qa.py` in `transformers`:
```python
from datasets import Dataset
ds = Dataset.from_dict({"a": range(4)})
ds = ds.map(lambda x: {"b": [[None, [0]]]}, batched=True, batch_size=1, remove_columns=["a"])
print(ds.to_pandas())
# before:
# b
# 0 [None, [0]]
# 1 [[], [0]]
# 2 [[], [0]]
# 3 [[], [0]]
#
# now:
# b
# 0 [None, [0]]
# 1 [None, [0]]
# 2 [None, [0]]
# 3 [None, [0]]
```
cc @sgugger
|
closed
|
https://github.com/huggingface/datasets/pull/4282
| 2022-05-04T16:37:01
| 2022-05-06T10:43:58
| 2022-05-06T10:37:00
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
1,225,556,939
| 4,281
|
Remove a copy-paste sentence in dataset cards
|
Remove the following copy-paste sentence from dataset cards:
```
We show detailed information for up to 5 configurations of the dataset.
```
|
closed
|
https://github.com/huggingface/datasets/pull/4281
| 2022-05-04T15:41:55
| 2022-05-06T08:38:03
| 2022-05-04T18:33:16
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
1,225,446,844
| 4,280
|
Add missing features to commonsense_qa dataset
|
Fix partially #4275.
|
closed
|
https://github.com/huggingface/datasets/pull/4280
| 2022-05-04T14:24:26
| 2022-05-06T14:23:57
| 2022-05-06T14:16:46
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
1,225,300,273
| 4,279
|
Update minimal PyArrow version warning
|
Update the minimal PyArrow version warning (should've been part of #4250).
|
closed
|
https://github.com/huggingface/datasets/pull/4279
| 2022-05-04T12:26:09
| 2022-05-05T08:50:58
| 2022-05-05T08:43:47
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
1,225,122,123
| 4,278
|
Add missing features to openbookqa dataset for additional config
|
Fix partially #4276.
|
closed
|
https://github.com/huggingface/datasets/pull/4278
| 2022-05-04T09:22:50
| 2022-05-06T13:13:20
| 2022-05-06T13:06:01
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
1,225,002,286
| 4,277
|
Enable label alignment for token classification datasets
|
This PR extends the `Dataset.align_labels_with_mapping()` method to support alignment of label mappings between datasets and models for token classification (e.g. NER).
Example of usage:
```python
from datasets import load_dataset
ner_ds = load_dataset("conll2003", split="train")
# returns [3, 0, 7, 0, 0, 0, 7, 0, 0]
ner_ds[0]["ner_tags"]
# hypothetical model mapping with O <--> B-LOC
label2id = {
"B-LOC": "0",
"B-MISC": "7",
"B-ORG": "3",
"B-PER": "1",
"I-LOC": "6",
"I-MISC": "8",
"I-ORG": "4",
"I-PER": "2",
"O": "5"
}
ner_aligned_ds = ner_ds.align_labels_with_mapping(label2id, "ner_tags")
# returns [3, 5, 7, 5, 5, 5, 7, 5, 5]
ner_aligned_ds[0]["ner_tags"]
```
Context: we need this in AutoTrain to automatically align datasets / models during evaluation. cc @abhishekkrthakur
|
closed
|
https://github.com/huggingface/datasets/pull/4277
| 2022-05-04T07:15:16
| 2022-05-06T15:42:15
| 2022-05-06T15:36:31
|
{
"login": "lewtun",
"id": 26859204,
"type": "User"
}
|
[] | true
|
[] |
1,224,949,252
| 4,276
|
OpenBookQA has missing and inconsistent field names
|
## Describe the bug
OpenBookQA implementation is inconsistent with the original dataset.
We need to:
1. The dataset field [question][stem] is flattened into question_stem. Unflatten it to match the original format.
2. Add missing additional fields:
- 'fact1': row['fact1'],
- 'humanScore': row['humanScore'],
- 'clarity': row['clarity'],
- 'turkIdAnonymized': row['turkIdAnonymized']
3. Ensure the structure and every data item in the original OpenBookQA matches our OpenBookQA version.
## Expected results
The structure and every data item in the original OpenBookQA matches our OpenBookQA version.
## Actual results
TBD
## Environment info
- `datasets` version: 2.1.0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
|
closed
|
https://github.com/huggingface/datasets/issues/4276
| 2022-05-04T05:51:52
| 2022-10-11T17:11:53
| 2022-10-05T13:50:03
|
{
"login": "vblagoje",
"id": 458335,
"type": "User"
}
|
[
{
"name": "dataset bug",
"color": "2edb81"
}
] | false
|
[] |
1,224,943,414
| 4,275
|
CommonSenseQA has missing and inconsistent field names
|
## Describe the bug
In short, CommonSenseQA implementation is inconsistent with the original dataset.
More precisely, we need to:
1. Add the dataset matching "id" field. The current dataset, instead, regenerates monotonically increasing id.
2. The [“question”][“stem”] field is flattened into "question". We should match the original dataset and unflatten it
3. Add the missing "question_concept" field in the question tree node
4. Anything else? Go over the data structure of the newly repaired CommonSenseQA and make sure it matches the original
## Expected results
Every data item of the CommonSenseQA should structurally and data-wise match the original CommonSenseQA dataset.
## Actual results
TBD
## Environment info
- `datasets` version: 2.1.0
- Platform: macOS-10.15.7-x86_64-i386-64bit
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.2
|
open
|
https://github.com/huggingface/datasets/issues/4275
| 2022-05-04T05:38:59
| 2022-05-04T11:41:18
| null |
{
"login": "vblagoje",
"id": 458335,
"type": "User"
}
|
[
{
"name": "dataset bug",
"color": "2edb81"
}
] | false
|
[] |
1,224,740,303
| 4,274
|
Add API code examples for IterableDataset
|
This PR adds API code examples for `IterableDataset` and `IterableDatasetDicts`.
|
closed
|
https://github.com/huggingface/datasets/pull/4274
| 2022-05-03T22:44:17
| 2022-05-04T16:29:32
| 2022-05-04T16:22:04
|
{
"login": "stevhliu",
"id": 59462357,
"type": "User"
}
|
[
{
"name": "documentation",
"color": "0075ca"
}
] | true
|
[] |
1,224,681,036
| 4,273
|
leadboard info added for TNE
| null |
closed
|
https://github.com/huggingface/datasets/pull/4273
| 2022-05-03T21:35:41
| 2022-05-05T13:25:24
| 2022-05-05T13:18:13
|
{
"login": "yanaiela",
"id": 8031035,
"type": "User"
}
|
[] | true
|
[] |
1,224,635,660
| 4,272
|
Fix typo in logging docs
|
This PR fixes #4271.
|
closed
|
https://github.com/huggingface/datasets/pull/4272
| 2022-05-03T20:47:57
| 2022-05-04T15:42:27
| 2022-05-04T06:58:36
|
{
"login": "stevhliu",
"id": 59462357,
"type": "User"
}
|
[] | true
|
[] |
1,224,404,403
| 4,271
|
A typo in docs of datasets.disable_progress_bar
|
## Describe the bug
in the docs of V2.1.0 datasets.disable_progress_bar, we should replace "enable" with "disable".
|
closed
|
https://github.com/huggingface/datasets/issues/4271
| 2022-05-03T17:44:56
| 2022-05-04T06:58:35
| 2022-05-04T06:58:35
|
{
"login": "jiangwangyi",
"id": 39762734,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
1,224,244,460
| 4,270
|
Fix style in openbookqa dataset
|
CI in PR:
- #4259
was green, but after merging it to master, a code quality error appeared.
|
closed
|
https://github.com/huggingface/datasets/pull/4270
| 2022-05-03T15:21:34
| 2022-05-06T08:38:06
| 2022-05-03T16:20:52
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
1,223,865,145
| 4,269
|
Add license and point of contact to big_patent dataset
|
Update metadata of big_patent dataset with:
- license
- point of contact
|
closed
|
https://github.com/huggingface/datasets/pull/4269
| 2022-05-03T09:24:07
| 2022-05-06T08:38:09
| 2022-05-03T11:16:19
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
1,223,331,964
| 4,268
|
error downloading bigscience-catalogue-lm-data/lm_en_wiktionary_filtered
|
## Describe the bug
Error generated when attempting to download dataset
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered")
```
## Expected results
A clear and concise description of the expected results.
## Actual results
```
ExpectedMoreDownloadedFiles Traceback (most recent call last)
[<ipython-input-62-4ac5cf959477>](https://localhost:8080/#) in <module>()
1 from datasets import load_dataset
2
----> 3 dataset = load_dataset("bigscience-catalogue-lm-data/lm_en_wiktionary_filtered")
3 frames
[/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py](https://localhost:8080/#) in verify_checksums(expected_checksums, recorded_checksums, verification_name)
31 return
32 if len(set(expected_checksums) - set(recorded_checksums)) > 0:
---> 33 raise ExpectedMoreDownloadedFiles(str(set(expected_checksums) - set(recorded_checksums)))
34 if len(set(recorded_checksums) - set(expected_checksums)) > 0:
35 raise UnexpectedDownloadedFile(str(set(recorded_checksums) - set(expected_checksums)))
ExpectedMoreDownloadedFiles: {'/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz', '/home/leandro/catalogue_data/datasets/lm_en_wiktionary_filtered/data/file-01.jsonl.gz.lock'}
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.3
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
|
closed
|
https://github.com/huggingface/datasets/issues/4268
| 2022-05-02T20:34:25
| 2022-05-06T15:53:30
| 2022-05-03T11:23:48
|
{
"login": "i-am-neo",
"id": 102043285,
"type": "User"
}
|
[
{
"name": "dataset bug",
"color": "2edb81"
}
] | false
|
[] |
1,223,214,275
| 4,267
|
Replace data URL in SAMSum dataset within the same repository
|
Replace data URL with one in the same repository.
|
closed
|
https://github.com/huggingface/datasets/pull/4267
| 2022-05-02T18:38:08
| 2022-05-06T08:38:13
| 2022-05-02T19:03:49
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
1,223,116,436
| 4,266
|
Add HF Speech Bench to Librispeech Dataset Card
|
Adds the HF Speech Bench to Librispeech Dataset Card in place of the Papers With Code Leaderboard. Should improve usage and visibility of this leaderboard! Wondering whether this can also be done for [Common Voice 7](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) and [8](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) through someone with permissions?
cc @patrickvonplaten: more leaderboard promotion!
|
closed
|
https://github.com/huggingface/datasets/pull/4266
| 2022-05-02T16:59:31
| 2022-05-05T08:47:20
| 2022-05-05T08:40:09
|
{
"login": "sanchit-gandhi",
"id": 93869735,
"type": "User"
}
|
[] | true
|
[] |
1,222,723,083
| 4,263
|
Rename imagenet2012 -> imagenet-1k
|
On the Hugging Face Hub, users refer to imagenet2012 (from #4178 ) as imagenet-1k in their model tags.
To correctly link models to imagenet, we should rename this dataset `imagenet-1k`.
Later we can add `imagenet-21k` as a new dataset if we want.
Once this one is merged we can delete the `imagenet2012` dataset repository on the Hub.
EDIT: to complete the rationale on why we should name it `imagenet-1k`:
If users specifically added the tag `imagenet-1k` , then it could be for two reasons (not sure which one is predominant), either they
- wanted to make it explicit that it’s not 21k -> the distinction is important for the community
- or they have been following this convention from other models -> the convention implicitly exists already
|
closed
|
https://github.com/huggingface/datasets/pull/4263
| 2022-05-02T10:26:21
| 2022-05-02T17:50:46
| 2022-05-02T16:32:57
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
1,222,130,749
| 4,262
|
Add YAML tags to Dataset Card rotten tomatoes
|
The dataset card for the rotten tomatoes / MR movie review dataset had some missing YAML tags. Hopefully, this also improves the visibility of this dataset now that paperswithcode and huggingface link to eachother.
|
closed
|
https://github.com/huggingface/datasets/pull/4262
| 2022-05-01T11:59:08
| 2022-05-03T14:27:33
| 2022-05-03T14:20:35
|
{
"login": "mo6zes",
"id": 10004251,
"type": "User"
}
|
[] | true
|
[] |
1,221,883,779
| 4,261
|
data leakage in `webis/conclugen` dataset
|
## Describe the bug
Some samples (argument-conclusion pairs) in the *training* split of the `webis/conclugen` dataset are present in both the *validation* and *test* splits, creating data leakage and distorting model results.
Furthermore, all splits contain duplicate samples.
## Steps to reproduce the bug
```python
from datasets import load_dataset
training = load_dataset("webis/conclugen", "base", split="train")
validation = load_dataset("webis/conclugen", "base", split="validation")
testing = load_dataset("webis/conclugen", "base", split="test")
# collect which sample id's are present in the training split
ids_validation = list()
ids_testing = list()
for train_sample in training:
train_argument = train_sample["argument"]
train_conclusion = train_sample["conclusion"]
train_id = train_sample["id"]
# test if current sample is in validation split
if train_argument in validation["argument"]:
for validation_sample in validation:
validation_argument = validation_sample["argument"]
validation_conclusion = validation_sample["conclusion"]
validation_id = validation_sample["id"]
if train_argument == validation_argument and train_conclusion == validation_conclusion:
ids_validation.append(validation_id)
# test if current sample is in test split
if train_argument in testing["argument"]:
for testing_sample in testing:
testing_argument = testing_sample["argument"]
testing_conclusion = testing_sample["conclusion"]
testing_id = testing_sample["id"]
if train_argument == testing_argument and train_conclusion == testing_conclusion:
ids_testing.append(testing_id)
```
## Expected results
Length of both lists `ids_validation` and `ids_testing` should be zero.
## Actual results
Length of `ids_validation` = `2556`
Length of `ids_testing` = `287`
Furthermore, there seems to be duplicate samples in (at least) the *training* split, since:
`print(len(set(ids_validation)))` = `950`
`print(len(set(ids_testing)))` = `101`
All in all, around 7% of the samples of each the *validation* and *test* split seems to be present in the *training* split.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.18.4
- Platform: macOS-12.3.1-arm64-arm-64bit
- Python version: 3.9.10
- PyArrow version: 7.0.0
|
closed
|
https://github.com/huggingface/datasets/issues/4261
| 2022-04-30T17:43:37
| 2022-05-03T06:04:26
| 2022-05-03T06:04:26
|
{
"login": "xflashxx",
"id": 54585776,
"type": "User"
}
|
[
{
"name": "dataset bug",
"color": "2edb81"
}
] | false
|
[] |
1,221,830,292
| 4,260
|
Add mr_polarity movie review sentiment classification
|
Add the MR (Movie Review) dataset. The original dataset contains sentences from Rotten Tomatoes labeled as either "positive" or "negative".
Homepage: [https://www.cs.cornell.edu/people/pabo/movie-review-data/](https://www.cs.cornell.edu/people/pabo/movie-review-data/)
paperswithcode: [https://paperswithcode.com/dataset/mr](https://paperswithcode.com/dataset/mr)
- [ ] I was not able to generate dummy data, the original dataset files have ".pos" and ".neg" as file extensions so the auto-generator does not work. Is it fine like this or should dummy data be added?
|
closed
|
https://github.com/huggingface/datasets/pull/4260
| 2022-04-30T13:19:33
| 2022-04-30T14:16:25
| 2022-04-30T14:16:25
|
{
"login": "mo6zes",
"id": 10004251,
"type": "User"
}
|
[] | true
|
[] |
1,221,768,025
| 4,259
|
Fix bug in choices labels in openbookqa dataset
|
This PR fixes the Bug in the openbookqa dataset as mentioned in this issue #3550.
Fix #3550.
cc. @lhoestq @mariosasko
|
closed
|
https://github.com/huggingface/datasets/pull/4259
| 2022-04-30T07:41:39
| 2022-05-04T06:31:31
| 2022-05-03T15:14:21
|
{
"login": "manandey",
"id": 6687858,
"type": "User"
}
|
[] | true
|
[] |
1,221,637,727
| 4,258
|
Fix/start token mask issue and update documentation
|
This pr fixes a couple bugs:
1) the perplexity was calculated with a 0 in the attention mask for the start token, which was causing high perplexity scores that were not correct
2) the documentation was not updated
|
closed
|
https://github.com/huggingface/datasets/pull/4258
| 2022-04-29T22:42:44
| 2022-05-02T16:33:20
| 2022-05-02T16:26:12
|
{
"login": "TristanThrush",
"id": 20826878,
"type": "User"
}
|
[] | true
|
[] |
1,221,393,137
| 4,257
|
Create metric card for Mahalanobis Distance
|
proposing a metric card to better explain how Mahalanobis distance works (last one for now :sweat_smile:
|
closed
|
https://github.com/huggingface/datasets/pull/4257
| 2022-04-29T18:37:27
| 2022-05-02T14:50:18
| 2022-05-02T14:43:24
|
{
"login": "sashavor",
"id": 14205986,
"type": "User"
}
|
[] | true
|
[] |
1,221,379,625
| 4,256
|
Create metric card for MSE
|
Proposing a metric card for Mean Squared Error
|
closed
|
https://github.com/huggingface/datasets/pull/4256
| 2022-04-29T18:21:22
| 2022-05-02T14:55:42
| 2022-05-02T14:48:47
|
{
"login": "sashavor",
"id": 14205986,
"type": "User"
}
|
[] | true
|
[] |
1,221,142,899
| 4,255
|
No google drive URL for pubmed_qa
|
I hosted the data files in https://huggingface.co/datasets/pubmed_qa. This is allowed because the data is under the MIT license.
cc @stas00
|
closed
|
https://github.com/huggingface/datasets/pull/4255
| 2022-04-29T15:55:46
| 2022-04-29T16:24:55
| 2022-04-29T16:18:56
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
1,220,204,395
| 4,254
|
Replace data URL in SAMSum dataset and support streaming
|
This PR replaces data URL in SAMSum dataset:
- original host (arxiv.org) does not allow HTTP Range requests
- we have hosted the data on the Hub (license: CC BY-NC-ND 4.0)
Moreover, it implements support for streaming.
Fix #4146.
Related to: #4236.
CC: @severo
|
closed
|
https://github.com/huggingface/datasets/pull/4254
| 2022-04-29T08:21:43
| 2022-05-06T08:38:16
| 2022-04-29T16:26:09
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
1,219,286,408
| 4,253
|
Create metric cards for mean IOU
|
Proposing a metric card for mIoU :rocket:
sorry for spamming you with review requests, @albertvillanova ! :hugs:
|
closed
|
https://github.com/huggingface/datasets/pull/4253
| 2022-04-28T20:58:27
| 2022-04-29T17:44:47
| 2022-04-29T17:38:06
|
{
"login": "sashavor",
"id": 14205986,
"type": "User"
}
|
[] | true
|
[] |
1,219,151,100
| 4,252
|
Creating metric card for MAE
|
Initial proposal for MAE metric card
|
closed
|
https://github.com/huggingface/datasets/pull/4252
| 2022-04-28T19:04:33
| 2022-04-29T16:59:11
| 2022-04-29T16:52:30
|
{
"login": "sashavor",
"id": 14205986,
"type": "User"
}
|
[] | true
|
[] |
1,219,116,354
| 4,251
|
Metric card for the XTREME-S dataset
|
Proposing a metric card for the XTREME-S dataset :hugs:
|
closed
|
https://github.com/huggingface/datasets/pull/4251
| 2022-04-28T18:32:19
| 2022-04-29T16:46:11
| 2022-04-29T16:38:46
|
{
"login": "sashavor",
"id": 14205986,
"type": "User"
}
|
[] | true
|
[] |
1,219,093,830
| 4,250
|
Bump PyArrow Version to 6
|
Fixes #4152
This PR updates the PyArrow version to 6 in setup.py, CI job files .circleci/config.yaml and .github/workflows/benchmarks.yaml files.
This will fix ArrayND error which exists in pyarrow 5.
|
closed
|
https://github.com/huggingface/datasets/pull/4250
| 2022-04-28T18:10:50
| 2022-05-04T09:36:52
| 2022-05-04T09:29:46
|
{
"login": "dnaveenr",
"id": 17746528,
"type": "User"
}
|
[] | true
|
[] |
1,218,524,424
| 4,249
|
Support streaming XGLUE dataset
|
Support streaming XGLUE dataset.
Fix #4247.
CC: @severo
|
closed
|
https://github.com/huggingface/datasets/pull/4249
| 2022-04-28T10:27:23
| 2022-05-06T08:38:21
| 2022-04-28T16:08:03
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
1,218,460,444
| 4,248
|
conll2003 dataset loads original data.
|
## Describe the bug
I load `conll2003` dataset to use refined data like [this](https://huggingface.co/datasets/conll2003/viewer/conll2003/train) preview, but it is original data that contains `'-DOCSTART- -X- -X- O'` text.
Is this a bug or should I use another dataset_name like `lhoestq/conll2003` ?
## Steps to reproduce the bug
```python
import datasets
from datasets import load_dataset
dataset = load_dataset("conll2003")
```
## Expected results
{
"chunk_tags": [11, 12, 12, 21, 13, 11, 11, 21, 13, 11, 12, 13, 11, 21, 22, 11, 12, 17, 11, 21, 17, 11, 12, 12, 21, 22, 22, 13, 11, 0],
"id": "0",
"ner_tags": [0, 3, 4, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"pos_tags": [12, 22, 22, 38, 15, 22, 28, 38, 15, 16, 21, 35, 24, 35, 37, 16, 21, 15, 24, 41, 15, 16, 21, 21, 20, 37, 40, 35, 21, 7],
"tokens": ["The", "European", "Commission", "said", "on", "Thursday", "it", "disagreed", "with", "German", "advice", "to", "consumers", "to", "shun", "British", "lamb", "until", "scientists", "determine", "whether", "mad", "cow", "disease", "can", "be", "transmitted", "to", "sheep", "."]
}
## Actual results
```python
print(dataset)
DatasetDict({
train: Dataset({
features: ['text'],
num_rows: 219554
})
test: Dataset({
features: ['text'],
num_rows: 50350
})
validation: Dataset({
features: ['text'],
num_rows: 55044
})
})
```
```python
for i in range(20):
print(dataset['train'][i])
{'text': '-DOCSTART- -X- -X- O'}
{'text': ''}
{'text': 'EU NNP B-NP B-ORG'}
{'text': 'rejects VBZ B-VP O'}
{'text': 'German JJ B-NP B-MISC'}
{'text': 'call NN I-NP O'}
{'text': 'to TO B-VP O'}
{'text': 'boycott VB I-VP O'}
{'text': 'British JJ B-NP B-MISC'}
{'text': 'lamb NN I-NP O'}
{'text': '. . O O'}
{'text': ''}
{'text': 'Peter NNP B-NP B-PER'}
{'text': 'Blackburn NNP I-NP I-PER'}
{'text': ''}
{'text': 'BRUSSELS NNP B-NP B-LOC'}
{'text': '1996-08-22 CD I-NP O'}
{'text': ''}
{'text': 'The DT B-NP O'}
{'text': 'European NNP I-NP B-ORG'}
```
|
closed
|
https://github.com/huggingface/datasets/issues/4248
| 2022-04-28T09:33:31
| 2022-07-18T07:15:48
| 2022-07-18T07:15:48
|
{
"login": "sue991",
"id": 26458611,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
1,218,320,882
| 4,247
|
The data preview of XGLUE
|
It seems that something wrong with the data previvew of XGLUE
|
closed
|
https://github.com/huggingface/datasets/issues/4247
| 2022-04-28T07:30:50
| 2022-04-29T08:23:28
| 2022-04-28T16:08:03
|
{
"login": "czq1999",
"id": 49108847,
"type": "User"
}
|
[] | false
|
[] |
1,218,320,293
| 4,246
|
Support to load dataset with TSV files by passing only dataset name
|
This PR implements support to load a dataset (w/o script) containing TSV files by passing only the dataset name (no need to pass `sep='\t'`):
```python
ds = load_dataset("dataset/name")
```
The refactoring allows for future builder kwargs customizations based on file extension.
Related to #4238.
|
closed
|
https://github.com/huggingface/datasets/pull/4246
| 2022-04-28T07:30:15
| 2022-05-06T08:38:28
| 2022-05-06T08:14:07
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
1,217,959,400
| 4,245
|
Add code examples for DatasetDict
|
This PR adds code examples for `DatasetDict` in the API reference :)
|
closed
|
https://github.com/huggingface/datasets/pull/4245
| 2022-04-27T22:52:22
| 2022-04-29T18:19:34
| 2022-04-29T18:13:03
|
{
"login": "stevhliu",
"id": 59462357,
"type": "User"
}
|
[
{
"name": "documentation",
"color": "0075ca"
}
] | true
|
[] |
1,217,732,221
| 4,244
|
task id update
|
changed multi input text classification as task id instead of category
|
closed
|
https://github.com/huggingface/datasets/pull/4244
| 2022-04-27T18:28:14
| 2022-05-04T10:43:53
| 2022-05-04T10:36:37
|
{
"login": "nazneenrajani",
"id": 3278583,
"type": "User"
}
|
[] | true
|
[] |
1,217,689,909
| 4,243
|
WIP: Initial shades loading script and readme
| null |
closed
|
https://github.com/huggingface/datasets/pull/4243
| 2022-04-27T17:45:43
| 2022-10-03T09:36:35
| 2022-10-03T09:36:35
|
{
"login": "shayne-longpre",
"id": 69018523,
"type": "User"
}
|
[
{
"name": "dataset contribution",
"color": "0e8a16"
}
] | true
|
[] |
1,217,665,960
| 4,242
|
Update auth when mirroring datasets on the hub
|
We don't need to use extraHeaders anymore for rate limits anymore. Anyway extraHeaders was not working with git LFS because it was passing the wrong auth to S3.
|
closed
|
https://github.com/huggingface/datasets/pull/4242
| 2022-04-27T17:22:31
| 2022-04-27T17:37:04
| 2022-04-27T17:30:42
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
1,217,423,686
| 4,241
|
NonMatchingChecksumError when attempting to download GLUE
|
## Describe the bug
I am trying to download the GLUE dataset from the NLP module but get an error (see below).
## Steps to reproduce the bug
```python
import nlp
nlp.__version__ # '0.2.0'
nlp.load_dataset('glue', name="rte", download_mode="force_redownload")
```
## Expected results
I expect the dataset to download without an error.
## Actual results
```
INFO:nlp.load:Checking /home/richier/.cache/huggingface/datasets/5fe6ab0df8a32a3371b2e6a969d31d855a19563724fb0d0f163748c270c0ac60.2ea96febf19981fae5f13f0a43d4e2aa58bc619bc23acf06de66675f425a5538.py for additional imports.
INFO:nlp.load:Found main folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue
INFO:nlp.load:Found specific version folder for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4
INFO:nlp.load:Found script file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.py
INFO:nlp.load:Found dataset infos file from https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/dataset_infos.json to /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/dataset_infos.json
INFO:nlp.load:Found metadata file for dataset https://s3.amazonaws.com/datasets.huggingface.co/nlp/datasets/glue/glue.py at /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4/glue.json
INFO:nlp.info:Loading Dataset Infos from /home/richier/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/datasets/glue/637080968c182118f006d3ea39dd9937940e81cfffc8d79836eaae8bba307fc4
INFO:nlp.builder:Generating dataset glue (/home/richier/.cache/huggingface/datasets/glue/rte/1.0.0)
INFO:nlp.builder:Dataset not on Hf google storage. Downloading and preparing it from source
INFO:nlp.utils.file_utils:Couldn't get ETag version for url https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb
INFO:nlp.utils.file_utils:https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb not found in cache or force_download set to True, downloading to /home/richier/.cache/huggingface/datasets/downloads/tmpldt3n805
Downloading and preparing dataset glue/rte (download: 680.81 KiB, generated: 1.83 MiB, total: 2.49 MiB) to /home/richier/.cache/huggingface/datasets/glue/rte/1.0.0...
Downloading: 100%|██████████| 73.0/73.0 [00:00<00:00, 73.9kB/s]
INFO:nlp.utils.file_utils:storing https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb in cache at /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64
INFO:nlp.utils.file_utils:creating metadata file for /home/richier/.cache/huggingface/datasets/downloads/e8b62ee44e6f8b6aea761935928579ffe1aa55d161808c482e0725abbdcf9c64
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
<ipython-input-7-669a8343dcc1> in <module>
----> 1 nlp.load_dataset('glue', name="rte", download_mode="force_redownload")
~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/load.py in load_dataset(path, name, version, data_dir, data_files, split, cache_dir, download_config, download_mode, ignore_verifications, save_infos, **config_kwargs)
518 download_mode=download_mode,
519 ignore_verifications=ignore_verifications,
--> 520 save_infos=save_infos,
521 )
522
~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, save_infos, try_from_hf_gcs, dl_manager, **download_and_prepare_kwargs)
418 verify_infos = not save_infos and not ignore_verifications
419 self._download_and_prepare(
--> 420 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
421 )
422 # Sync info
~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
458 # Checksums verification
459 if verify_infos:
--> 460 verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums())
461 for split_generator in split_generators:
462 if str(split_generator.split_info.name).lower() == "all":
~/anaconda3/envs/py36_bert_ee_torch1_11/lib/python3.6/site-packages/nlp/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums)
34 bad_urls = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
35 if len(bad_urls) > 0:
---> 36 raise NonMatchingChecksumError(str(bad_urls))
37 logger.info("All the checksums matched successfully.")
38
NonMatchingChecksumError: ['https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb']
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: Linux-4.18.0-348.20.1.el8_5.x86_64-x86_64-with-redhat-8.5-Ootpa
- Python version: 3.6.13
- PyArrow version: 6.0.1
- Pandas version: 1.1.5
|
closed
|
https://github.com/huggingface/datasets/issues/4241
| 2022-04-27T14:14:21
| 2022-04-28T07:45:27
| 2022-04-28T07:45:27
|
{
"login": "drussellmrichie",
"id": 9650729,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
1,217,287,594
| 4,240
|
Fix yield for crd3
|
Modified the `_generate_examples` function to consider all the turns for a chunk id as a single example
Modified the features accordingly
```
"turns": [
{
"names": datasets.features.Sequence(datasets.Value("string")),
"utterances": datasets.features.Sequence(datasets.Value("string")),
"number": datasets.Value("int32"),
}
],
}
```
I wasn't able to run `datasets-cli dummy_data datasets` command. Is there a workaround for this?
|
closed
|
https://github.com/huggingface/datasets/pull/4240
| 2022-04-27T12:31:36
| 2022-04-29T12:41:41
| 2022-04-29T12:41:41
|
{
"login": "shanyas10",
"id": 21066979,
"type": "User"
}
|
[] | true
|
[] |
1,217,269,689
| 4,239
|
Small fixes in ROC AUC docs
|
The list of use cases did not render on GitHub with the prepended spacing.
Additionally, some typo's we're fixed.
|
closed
|
https://github.com/huggingface/datasets/pull/4239
| 2022-04-27T12:15:50
| 2022-05-02T13:28:57
| 2022-05-02T13:22:03
|
{
"login": "wschella",
"id": 9478856,
"type": "User"
}
|
[] | true
|
[] |
1,217,168,123
| 4,238
|
Dataset caching policy
|
## Describe the bug
I cannot clean cache of my datasets files, despite I have updated the `csv` files on the repository [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences). The original file had a line with bad characters, causing the following error
```
[/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values)
852 if value not in self._str2int:
853 value = str(value).strip()
--> 854 output.append(self._str2int[str(value)])
855 else:
856 # No names provided, try to integerize
KeyError: '\\N'
```
The file now is cleanup up, but I still get the error. This happens even if I inspect the local cached contents, and cleanup the files locally:
```python
from datasets import load_dataset_builder
dataset_builder = load_dataset_builder("loretoparisi/tatoeba-sentences")
print(dataset_builder.cache_dir)
print(dataset_builder.info.features)
print(dataset_builder.info.splits)
```
```
Using custom data configuration loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd
/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-e59b8ad92f1bb8dd/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519
None
None
```
and removing files located at `/root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-*`.
Is there any remote file caching policy in place? If so, is it possibile to programmatically disable it?
Currently it seems that the file `test.csv` on the repo [here](https://huggingface.co/datasets/loretoparisi/tatoeba-sentences/blob/main/test.csv) is cached remotely. In fact I download locally the file from raw link, the file is up-to-date; but If I use it within `datasets` as shown above, it gives to me always the first revision of the file, not the last.
Thank you.
## Steps to reproduce the bug
```python
from datasets import load_dataset,Features,Value,ClassLabel
class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"]
features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')})
num_labels = features['label'].num_classes
data_files = { "train": "train.csv", "test": "test.csv" }
sentences = load_dataset(
"loretoparisi/tatoeba-sentences",
data_files=data_files,
delimiter='\t',
column_names=['label', 'text'],
)
# You can make this part faster with num_proc=<some int>
sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features)
sentences = sentences.shuffle()
```
## Expected results
Properly tokenize dataset file `test.csv` without issues.
## Actual results
Specify the actual results or traceback.
```
Downloading data files: 100%
2/2 [00:16<00:00, 7.34s/it]
Downloading data: 100%
391M/391M [00:12<00:00, 36.6MB/s]
Downloading data: 100%
92.4M/92.4M [00:02<00:00, 40.0MB/s]
Extracting data files: 100%
2/2 [00:00<00:00, 47.66it/s]
Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-efeff8965c730a2c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data.
100%
2/2 [00:00<00:00, 25.94it/s]
11%
942339/8256449 [01:55<13:11, 9245.85ex/s]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
[<ipython-input-3-6a9867fad8d6>](https://localhost:8080/#) in <module>()
12 )
13 # You can make this part faster with num_proc=<some int>
---> 14 sentences = sentences.map(lambda ex: {"label" : features["label"].str2int(ex["label"]) if ex["label"] is not None else None}, features=features)
15 sentences = sentences.shuffle()
10 frames
[/usr/local/lib/python3.7/dist-packages/datasets/features/features.py](https://localhost:8080/#) in str2int(self, values)
852 if value not in self._str2int:
853 value = str(value).strip()
--> 854 output.append(self._str2int[str(value)])
855 else:
856 # No names provided, try to integerize
KeyError: '\\N'
```
## Environment info
```
- `datasets` version: 2.1.0
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- PyArrow version: 6.0.1
- Pandas version: 1.3.5
- ```
```
- `transformers` version: 4.18.0
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- Huggingface_hub version: 0.5.1
- PyTorch version (GPU?): 1.11.0+cu113 (True)
- Tensorflow version (GPU?): 2.8.0 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>
- ```
|
closed
|
https://github.com/huggingface/datasets/issues/4238
| 2022-04-27T10:42:11
| 2022-04-27T16:29:25
| 2022-04-27T16:28:50
|
{
"login": "loretoparisi",
"id": 163333,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
1,217,121,044
| 4,237
|
Common Voice 8 doesn't show datasets viewer
|
https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0
|
closed
|
https://github.com/huggingface/datasets/issues/4237
| 2022-04-27T10:05:20
| 2022-05-10T12:17:05
| 2022-05-10T12:17:04
|
{
"login": "patrickvonplaten",
"id": 23423619,
"type": "User"
}
|
[
{
"name": "dataset-viewer",
"color": "E5583E"
}
] | false
|
[] |
1,217,115,691
| 4,236
|
Replace data URL in big_patent dataset and support streaming
|
This PR replaces the Google Drive URL with our Hub one, once the data owners have approved to host their data on the Hub.
Moreover, this PR makes the dataset streamable.
Fix #4217.
|
closed
|
https://github.com/huggingface/datasets/pull/4236
| 2022-04-27T10:01:13
| 2022-06-10T08:10:55
| 2022-05-02T18:21:15
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
1,216,952,640
| 4,235
|
How to load VERY LARGE dataset?
|
### System Info
```shell
I am using transformer trainer while meeting the issue.
The trainer requests torch.utils.data.Dataset as input, which loads the whole dataset into the memory at once. Therefore, when the dataset is too large to load, there's nothing I can do except using IterDataset, which loads samples of data seperately, and results in low efficiency.
I wonder if there are any tricks like Sharding in huggingface trainer.
Looking forward to your reply.
```
### Who can help?
Trainer: @sgugger
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
None
### Expected behavior
```shell
I wonder if there are any tricks like fairseq Sharding very large datasets https://fairseq.readthedocs.io/en/latest/getting_started.html.
Thanks a lot!
```
|
closed
|
https://github.com/huggingface/datasets/issues/4235
| 2022-04-27T07:50:13
| 2023-07-25T15:07:57
| 2023-07-25T15:07:57
|
{
"login": "CaoYiqingT",
"id": 45160643,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
1,216,818,846
| 4,234
|
Autoeval config
|
Added autoeval config to imdb as pilot
|
closed
|
https://github.com/huggingface/datasets/pull/4234
| 2022-04-27T05:32:10
| 2022-05-06T13:20:31
| 2022-05-05T18:20:58
|
{
"login": "nazneenrajani",
"id": 3278583,
"type": "User"
}
|
[] | true
|
[] |
1,216,665,044
| 4,233
|
Autoeval
| null |
closed
|
https://github.com/huggingface/datasets/pull/4233
| 2022-04-27T01:32:09
| 2022-04-27T05:29:30
| 2022-04-27T01:32:23
|
{
"login": "nazneenrajani",
"id": 3278583,
"type": "User"
}
|
[] | true
|
[] |
1,216,659,444
| 4,232
|
adding new tag to tasks.json and modified for existing datasets
| null |
closed
|
https://github.com/huggingface/datasets/pull/4232
| 2022-04-27T01:21:09
| 2022-05-03T14:23:56
| 2022-05-03T14:16:39
|
{
"login": "nazneenrajani",
"id": 3278583,
"type": "User"
}
|
[] | true
|
[] |
1,216,651,960
| 4,231
|
Fix invalid url to CC-Aligned dataset
|
The CC-Aligned dataset url has changed to https://data.statmt.org/cc-aligned/, the old address http://www.statmt.org/cc-aligned/ is no longer valid
|
closed
|
https://github.com/huggingface/datasets/pull/4231
| 2022-04-27T01:07:01
| 2022-05-16T17:01:13
| 2022-05-16T16:53:12
|
{
"login": "juntang-zhuang",
"id": 44451229,
"type": "User"
}
|
[] | true
|
[] |
1,216,643,661
| 4,230
|
Why the `conll2003` dataset on huggingface only contains the `en` subset? Where is the German data?
|
But on huggingface datasets:
Where is the German data?
|
closed
|
https://github.com/huggingface/datasets/issues/4230
| 2022-04-27T00:53:52
| 2023-07-25T15:10:15
| 2023-07-25T15:10:15
|
{
"login": "beyondguo",
"id": 37113676,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
}
] | false
|
[] |
1,216,638,968
| 4,229
|
new task tag
|
multi-input-text-classification tag for classification datasets that take more than one input
|
closed
|
https://github.com/huggingface/datasets/pull/4229
| 2022-04-27T00:47:08
| 2022-04-27T00:48:28
| 2022-04-27T00:48:17
|
{
"login": "nazneenrajani",
"id": 3278583,
"type": "User"
}
|
[] | true
|
[] |
1,216,523,043
| 4,228
|
new task tag
|
multi-input-text-classification tag for classification datasets that take more than one input
|
closed
|
https://github.com/huggingface/datasets/pull/4228
| 2022-04-26T22:00:33
| 2022-04-27T00:48:31
| 2022-04-27T00:46:31
|
{
"login": "nazneenrajani",
"id": 3278583,
"type": "User"
}
|
[] | true
|
[] |
1,216,455,316
| 4,227
|
Add f1 metric card, update docstring in py file
| null |
closed
|
https://github.com/huggingface/datasets/pull/4227
| 2022-04-26T20:41:03
| 2022-05-03T12:50:23
| 2022-05-03T12:43:33
|
{
"login": "emibaylor",
"id": 27527747,
"type": "User"
}
|
[] | true
|
[] |
1,216,331,073
| 4,226
|
Add pearsonr mc, update functionality to match the original docs
|
- adds pearsonr metric card
- adds ability to return p-value
- p-value was mentioned in the original docs as a return value, but there was no option to return it. I updated the _compute function slightly to have an option to return the p-value.
|
closed
|
https://github.com/huggingface/datasets/pull/4226
| 2022-04-26T18:30:46
| 2022-05-03T17:09:24
| 2022-05-03T17:02:28
|
{
"login": "emibaylor",
"id": 27527747,
"type": "User"
}
|
[] | true
|
[] |
1,216,213,464
| 4,225
|
autoeval config
|
add train eval index for autoeval
|
closed
|
https://github.com/huggingface/datasets/pull/4225
| 2022-04-26T16:38:34
| 2022-04-27T00:48:31
| 2022-04-26T22:00:26
|
{
"login": "nazneenrajani",
"id": 3278583,
"type": "User"
}
|
[] | true
|
[] |
1,216,209,667
| 4,224
|
autoeval config
|
add train eval index for autoeval
|
closed
|
https://github.com/huggingface/datasets/pull/4224
| 2022-04-26T16:35:19
| 2022-04-26T16:36:45
| 2022-04-26T16:36:45
|
{
"login": "nazneenrajani",
"id": 3278583,
"type": "User"
}
|
[] | true
|
[] |
1,216,107,082
| 4,223
|
Add Accuracy Metric Card
|
- adds accuracy metric card
- updates docstring in accuracy.py
- adds .json file with metric card and docstring information
|
closed
|
https://github.com/huggingface/datasets/pull/4223
| 2022-04-26T15:10:46
| 2022-05-03T14:27:45
| 2022-05-03T14:20:47
|
{
"login": "emibaylor",
"id": 27527747,
"type": "User"
}
|
[] | true
|
[] |
1,216,056,439
| 4,222
|
Fix description links in dataset cards
|
I noticed many links were not properly displayed (only text, no link) on the Hub because of wrong syntax, e.g.: https://huggingface.co/datasets/big_patent
This PR fixes all description links in dataset cards.
|
closed
|
https://github.com/huggingface/datasets/pull/4222
| 2022-04-26T14:36:25
| 2022-05-06T08:38:38
| 2022-04-26T16:52:29
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
1,215,911,182
| 4,221
|
Dictionary Feature
|
Hi, I'm trying to create the loading script for a dataset in which one feature is a list of dictionaries, which afaik doesn't fit very well the values and structures supported by Value and Sequence. Is there any suggested workaround, am I missing something?
Thank you in advance.
|
closed
|
https://github.com/huggingface/datasets/issues/4221
| 2022-04-26T12:50:18
| 2022-04-29T14:52:19
| 2022-04-28T17:04:58
|
{
"login": "jordiae",
"id": 2944532,
"type": "User"
}
|
[
{
"name": "question",
"color": "d876e3"
}
] | false
|
[] |
1,215,225,802
| 4,220
|
Altered faiss installation comment
| null |
closed
|
https://github.com/huggingface/datasets/pull/4220
| 2022-04-26T01:20:43
| 2022-05-09T17:29:34
| 2022-05-09T17:22:09
|
{
"login": "vishalsrao",
"id": 36671559,
"type": "User"
}
|
[] | true
|
[] |
1,214,934,025
| 4,219
|
Add F1 Metric Card
| null |
closed
|
https://github.com/huggingface/datasets/pull/4219
| 2022-04-25T19:14:56
| 2022-04-26T20:44:18
| 2022-04-26T20:37:46
|
{
"login": "emibaylor",
"id": 27527747,
"type": "User"
}
|
[] | true
|
[] |
1,214,748,226
| 4,218
|
Make code for image downloading from image urls cacheable
|
Fix #4199
|
closed
|
https://github.com/huggingface/datasets/pull/4218
| 2022-04-25T16:17:59
| 2022-04-26T17:00:24
| 2022-04-26T13:38:26
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
1,214,688,141
| 4,217
|
Big_Patent dataset broken
|
## Dataset viewer issue for '*big_patent*'
**Link:** *[link to the dataset viewer page](https://huggingface.co/datasets/big_patent/viewer/all/train)*
*Unable to view because it says FileNotFound, also cannot download it through the python API*
Am I the one who added this dataset ? No
|
closed
|
https://github.com/huggingface/datasets/issues/4217
| 2022-04-25T15:31:45
| 2022-05-26T06:29:43
| 2022-05-02T18:21:15
|
{
"login": "Matthew-Larsen",
"id": 54189843,
"type": "User"
}
|
[
{
"name": "hosted-on-google-drive",
"color": "8B51EF"
}
] | false
|
[] |
1,214,614,029
| 4,216
|
Avoid recursion error in map if example is returned as dict value
|
I noticed this bug while answering [this question](https://discuss.huggingface.co/t/correct-way-to-create-a-dataset-from-a-csv-file/15686/11?u=mariosasko).
This code replicates the bug:
```python
from datasets import Dataset
dset = Dataset.from_dict({"en": ["aa", "bb"], "fr": ["cc", "dd"]})
dset.map(lambda ex: {"translation": ex})
```
and this is the fix for it (before this PR):
```python
from datasets import Dataset
dset = Dataset.from_dict({"en": ["aa", "bb"], "fr": ["cc", "dd"]})
dset.map(lambda ex: {"translation": dict(ex)})
```
Internally, this can be fixed by merging two dicts via dict unpacking (instead of `dict.update) `in `Dataset.map`, which avoids creating recursive dictionaries.
P.S. `{**a, **b}` is slightly more performant than `a.update(b)` in my bencmarks.
|
closed
|
https://github.com/huggingface/datasets/pull/4216
| 2022-04-25T14:40:32
| 2022-05-04T17:20:06
| 2022-05-04T17:12:52
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
1,214,579,162
| 4,215
|
Add `drop_last_batch` to `IterableDataset.map`
|
Addresses this comment: https://github.com/huggingface/datasets/pull/3801#pullrequestreview-901736921
|
closed
|
https://github.com/huggingface/datasets/pull/4215
| 2022-04-25T14:15:19
| 2022-05-03T15:56:07
| 2022-05-03T15:48:54
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
1,214,572,430
| 4,214
|
Skip checksum computation in Imagefolder by default
|
Avoids having to set `ignore_verifications=True` in `load_dataset("imagefolder", ...)` to skip checksum verification and speed up loading.
The user can still pass `DownloadConfig(record_checksums=True)` to not skip this part.
|
closed
|
https://github.com/huggingface/datasets/pull/4214
| 2022-04-25T14:10:41
| 2022-05-03T15:28:32
| 2022-05-03T15:21:29
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
1,214,510,010
| 4,213
|
ETT time series dataset
|
Ready for review.
|
closed
|
https://github.com/huggingface/datasets/pull/4213
| 2022-04-25T13:26:18
| 2022-05-05T12:19:21
| 2022-05-05T12:10:35
|
{
"login": "kashif",
"id": 8100,
"type": "User"
}
|
[] | true
|
[] |
1,214,498,582
| 4,212
|
[Common Voice] Make sure bytes are correctly deleted if `path` exists
|
`path` should be set to local path inside audio feature if exist so that bytes can correctly be deleted.
|
closed
|
https://github.com/huggingface/datasets/pull/4212
| 2022-04-25T13:18:26
| 2022-04-26T22:54:28
| 2022-04-26T22:48:27
|
{
"login": "patrickvonplaten",
"id": 23423619,
"type": "User"
}
|
[] | true
|
[] |
1,214,361,837
| 4,211
|
DatasetDict containing Datasets with different features when pushed to hub gets remapped features
|
Hi there,
I am trying to load a dataset to the Hub. This dataset is a `DatasetDict` composed of various splits. Some splits have a different `Feature` mapping. Locally, the DatasetDict preserves the individual features but if I `push_to_hub` and then `load_dataset`, the features are all the same.
Dataset and code to reproduce available [here](https://huggingface.co/datasets/pietrolesci/robust_nli).
In short:
I have 3 feature mapping
```python
Tri_features = Features(
{
"idx": Value(dtype="int64"),
"premise": Value(dtype="string"),
"hypothesis": Value(dtype="string"),
"label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]),
}
)
Ent_features = Features(
{
"idx": Value(dtype="int64"),
"premise": Value(dtype="string"),
"hypothesis": Value(dtype="string"),
"label": ClassLabel(num_classes=2, names=["non-entailment", "entailment"]),
}
)
Con_features = Features(
{
"idx": Value(dtype="int64"),
"premise": Value(dtype="string"),
"hypothesis": Value(dtype="string"),
"label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"]),
}
)
```
Then I create different datasets
```python
dataset_splits = {}
for split in df["split"].unique():
print(split)
df_split = df.loc[df["split"] == split].copy()
if split in Tri_dataset:
df_split["label"] = df_split["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2})
ds = Dataset.from_pandas(df_split, features=Tri_features)
elif split in Ent_bin_dataset:
df_split["label"] = df_split["label"].map({"non-entailment": 0, "entailment": 1})
ds = Dataset.from_pandas(df_split, features=Ent_features)
elif split in Con_bin_dataset:
df_split["label"] = df_split["label"].map({"non-contradiction": 0, "contradiction": 1})
ds = Dataset.from_pandas(df_split, features=Con_features)
else:
print("ERROR:", split)
dataset_splits[split] = ds
datasets = DatasetDict(dataset_splits)
```
I then push to hub
```python
datasets.push_to_hub("pietrolesci/robust_nli", token="<token>")
```
Finally, I load it from the hub
```python
datasets_loaded_from_hub = load_dataset("pietrolesci/robust_nli")
```
And I get that
```python
datasets["LI_TS"].features != datasets_loaded_from_hub["LI_TS"].features
```
since
```python
"label": ClassLabel(num_classes=2, names=["non-contradiction", "contradiction"])
```
gets remapped to
```python
"label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"])
```
|
closed
|
https://github.com/huggingface/datasets/issues/4211
| 2022-04-25T11:22:54
| 2023-04-06T19:25:50
| 2022-05-20T15:15:30
|
{
"login": "pietrolesci",
"id": 61748653,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
1,214,089,130
| 4,210
|
TypeError: Cannot cast array data from dtype('O') to dtype('int64') according to the rule 'safe'
|
### System Info
```shell
- `transformers` version: 4.18.0
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- Huggingface_hub version: 0.5.1
- PyTorch version (GPU?): 1.10.0+cu111 (True)
- Tensorflow version (GPU?): 2.8.0 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>
```
### Who can help?
@LysandreJik
### Information
- [ ] The official example scripts
- [X] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [X] My own task or dataset (give details below)
### Reproduction
```python
from datasets import load_dataset,Features,Value,ClassLabel
class_names = ["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu","nob","zsm","est","kat","pol","lat","urd","sqi","isl","fry","afr","ron","fao","san","bre","tat","yid","uig","uzb","srp","qya","dan","pes","slk","eus","cycl","acm","tgl","lvs","kaz","hye","hin","lit","ben","cat","bos","hrv","tha","orv","cha","mon","lzh","scn","gle","mkd","slv","frm","glg","vol","ain","jbo","tok","ina","nds","mal","tlh","roh","ltz","oss","ido","gla","mlt","sco","ast","jav","oci","ile","ota","xal","tel","sjn","nov","khm","tpi","ang","aze","tgk","tuk","chv","hsb","dsb","bod","sme","cym","mri","ksh","kmr","ewe","kab","ber","tpw","udm","lld","pms","lad","grn","mlg","xho","pnb","grc","hat","lao","npi","cor","nah","avk","mar","guj","pan","kir","myv","prg","sux","crs","ckt","bak","zlm","hil","cbk","chr","nav","lkt","enm","arq","lin","abk","pcd","rom","gsw","tam","zul","awa","wln","amh","bar","hbo","mhr","bho","mrj","ckb","osx","pfl","mgm","sna","mah","hau","kan","nog","sin","glv","dng","kal","liv","vro","apc","jdt","fur","che","haw","yor","crh","pdc","ppl","kin","shs","mnw","tet","sah","kum","ngt","nya","pus","hif","mya","moh","wol","tir","ton","lzz","oar","lug","brx","non","mww","hak","nlv","ngu","bua","aym","vec","ibo","tkl","bam","kha","ceb","lou","fuc","smo","gag","lfn","arg","umb","tyv","kjh","oji","cyo","urh","kzj","pam","srd","lmo","swg","mdf","gil","snd","tso","sot","zza","tsn","pau","som","egl","ady","asm","ori","dtp","cho","max","kam","niu","sag","ilo","kaa","fuv","nch","hoc","iba","gbm","sun","war","mvv","pap","ary","kxi","csb","pag","cos","rif","kek","krc","aii","ban","ssw","tvl","mfe","tah","bvy","bcl","hnj","nau","nst","afb","quc","min","tmw","mad","bjn","mai","cjy","got","hsn","gan","tzl","dws","ldn","afh","sgs","krl","vep","rue","tly","mic","ext","izh","sma","jam","cmo","mwl","kpv","koi","bis","ike","run","evn","ryu","mnc","aoz","otk","kas","aln","akl","yua","shy","fkv","gos","fij","thv","zgh","gcf","cay","xmf","tig","div","lij","rap","hrx","cpi","tts","gaa","tmr","iii","ltg","bzt","syc","emx","gom","chg","osp","stq","frr","fro","nys","toi","new","phn","jpa","rel","drt","chn","pli","laa","bal","hdn","hax","mik","ajp","xqa","pal","crk","mni","lut","ayl","ood","sdh","ofs","nus","kiu","diq","qxq","alt","bfz","klj","mus","srn","guc","lim","zea","shi","mnr","bom","sat","szl"]
features = Features({ 'label': ClassLabel(names=class_names), 'text': Value('string')})
num_labels = features['label'].num_classes
data_files = { "train": "train.csv", "test": "test.csv" }
sentences = load_dataset("loretoparisi/tatoeba-sentences",
data_files=data_files,
delimiter='\t',
column_names=['label', 'text'],
features = features
```
ERROR:
```
ClassLabel(num_classes=403, names=['cmn', 'deu', 'rus', 'fra', 'eng', 'jpn', 'spa', 'ita', 'kor', 'vie', 'nld', 'epo', 'por', 'tur', 'heb', 'hun', 'ell', 'ind', 'ara', 'arz', 'fin', 'bul', 'yue', 'swe', 'ukr', 'bel', 'que', 'ces', 'swh', 'nno', 'wuu', 'nob', 'zsm', 'est', 'kat', 'pol', 'lat', 'urd', 'sqi', 'isl', 'fry', 'afr', 'ron', 'fao', 'san', 'bre', 'tat', 'yid', 'uig', 'uzb', 'srp', 'qya', 'dan', 'pes', 'slk', 'eus', 'cycl', 'acm', 'tgl', 'lvs', 'kaz', 'hye', 'hin', 'lit', 'ben', 'cat', 'bos', 'hrv', 'tha', 'orv', 'cha', 'mon', 'lzh', 'scn', 'gle', 'mkd', 'slv', 'frm', 'glg', 'vol', 'ain', 'jbo', 'tok', 'ina', 'nds', 'mal', 'tlh', 'roh', 'ltz', 'oss', 'ido', 'gla', 'mlt', 'sco', 'ast', 'jav', 'oci', 'ile', 'ota', 'xal', 'tel', 'sjn', 'nov', 'khm', 'tpi', 'ang', 'aze', 'tgk', 'tuk', 'chv', 'hsb', 'dsb', 'bod', 'sme', 'cym', 'mri', 'ksh', 'kmr', 'ewe', 'kab', 'ber', 'tpw', 'udm', 'lld', 'pms', 'lad', 'grn', 'mlg', 'xho', 'pnb', 'grc', 'hat', 'lao', 'npi', 'cor', 'nah', 'avk', 'mar', 'guj', 'pan', 'kir', 'myv', 'prg', 'sux', 'crs', 'ckt', 'bak', 'zlm', 'hil', 'cbk', 'chr', 'nav', 'lkt', 'enm', 'arq', 'lin', 'abk', 'pcd', 'rom', 'gsw', 'tam', 'zul', 'awa', 'wln', 'amh', 'bar', 'hbo', 'mhr', 'bho', 'mrj', 'ckb', 'osx', 'pfl', 'mgm', 'sna', 'mah', 'hau', 'kan', 'nog', 'sin', 'glv', 'dng', 'kal', 'liv', 'vro', 'apc', 'jdt', 'fur', 'che', 'haw', 'yor', 'crh', 'pdc', 'ppl', 'kin', 'shs', 'mnw', 'tet', 'sah', 'kum', 'ngt', 'nya', 'pus', 'hif', 'mya', 'moh', 'wol', 'tir', 'ton', 'lzz', 'oar', 'lug', 'brx', 'non', 'mww', 'hak', 'nlv', 'ngu', 'bua', 'aym', 'vec', 'ibo', 'tkl', 'bam', 'kha', 'ceb', 'lou', 'fuc', 'smo', 'gag', 'lfn', 'arg', 'umb', 'tyv', 'kjh', 'oji', 'cyo', 'urh', 'kzj', 'pam', 'srd', 'lmo', 'swg', 'mdf', 'gil', 'snd', 'tso', 'sot', 'zza', 'tsn', 'pau', 'som', 'egl', 'ady', 'asm', 'ori', 'dtp', 'cho', 'max', 'kam', 'niu', 'sag', 'ilo', 'kaa', 'fuv', 'nch', 'hoc', 'iba', 'gbm', 'sun', 'war', 'mvv', 'pap', 'ary', 'kxi', 'csb', 'pag', 'cos', 'rif', 'kek', 'krc', 'aii', 'ban', 'ssw', 'tvl', 'mfe', 'tah', 'bvy', 'bcl', 'hnj', 'nau', 'nst', 'afb', 'quc', 'min', 'tmw', 'mad', 'bjn', 'mai', 'cjy', 'got', 'hsn', 'gan', 'tzl', 'dws', 'ldn', 'afh', 'sgs', 'krl', 'vep', 'rue', 'tly', 'mic', 'ext', 'izh', 'sma', 'jam', 'cmo', 'mwl', 'kpv', 'koi', 'bis', 'ike', 'run', 'evn', 'ryu', 'mnc', 'aoz', 'otk', 'kas', 'aln', 'akl', 'yua', 'shy', 'fkv', 'gos', 'fij', 'thv', 'zgh', 'gcf', 'cay', 'xmf', 'tig', 'div', 'lij', 'rap', 'hrx', 'cpi', 'tts', 'gaa', 'tmr', 'iii', 'ltg', 'bzt', 'syc', 'emx', 'gom', 'chg', 'osp', 'stq', 'frr', 'fro', 'nys', 'toi', 'new', 'phn', 'jpa', 'rel', 'drt', 'chn', 'pli', 'laa', 'bal', 'hdn', 'hax', 'mik', 'ajp', 'xqa', 'pal', 'crk', 'mni', 'lut', 'ayl', 'ood', 'sdh', 'ofs', 'nus', 'kiu', 'diq', 'qxq', 'alt', 'bfz', 'klj', 'mus', 'srn', 'guc', 'lim', 'zea', 'shi', 'mnr', 'bom', 'sat', 'szl'], id=None)
Value(dtype='string', id=None)
Using custom data configuration loretoparisi--tatoeba-sentences-7b2c5e991f398f39
Downloading and preparing dataset csv/loretoparisi--tatoeba-sentences to /root/.cache/huggingface/datasets/csv/loretoparisi--tatoeba-sentences-7b2c5e991f398f39/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519...
Downloading data files: 100%
2/2 [00:18<00:00, 8.06s/it]
Downloading data: 100%
391M/391M [00:13<00:00, 35.3MB/s]
Downloading data: 100%
92.4M/92.4M [00:02<00:00, 36.5MB/s]
Failed to read file '/root/.cache/huggingface/datasets/downloads/933132df9905194ea9faeb30cabca8c49318795612f6495fcb941a290191dd5d' with error <class 'ValueError'>: invalid literal for int() with base 10: 'cmn'
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens()
TypeError: Cannot cast array data from dtype('O') to dtype('int64') according to the rule 'safe'
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
15 frames
/usr/local/lib/python3.7/dist-packages/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens()
ValueError: invalid literal for int() with base 10: 'cmn'
```
while loading without `features` it loads without errors
```
sentences = load_dataset("loretoparisi/tatoeba-sentences",
data_files=data_files,
delimiter='\t',
column_names=['label', 'text']
)
```
but the `label` col seems to be wrong (without the `ClassLabel` object):
```
sentences['train'].features
{'label': Value(dtype='string', id=None),
'text': Value(dtype='string', id=None)}
```
The dataset was https://huggingface.co/datasets/loretoparisi/tatoeba-sentences
Dataset format is:
```
ces Nechci vědět, co je tam uvnitř.
ces Kdo o tom chce slyšet?
deu Tom sagte, er fühle sich nicht wohl.
ber Mel-iyi-d anida-t tura ?
hun Gondom lesz rá rögtön.
ber Mel-iyi-d anida-tt tura ?
deu Ich will dich nicht reden hören.
```
### Expected behavior
```shell
correctly load train and test files.
```
|
closed
|
https://github.com/huggingface/datasets/issues/4210
| 2022-04-25T07:28:42
| 2022-05-31T12:16:31
| 2022-05-31T12:16:31
|
{
"login": "loretoparisi",
"id": 163333,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
1,213,716,426
| 4,208
|
Add CMU MoCap Dataset
|
Resolves #3457
Dataset Request : Add CMU Graphics Lab Motion Capture dataset [#3457](https://github.com/huggingface/datasets/issues/3457)
This PR adds the CMU MoCap Dataset.
The authors didn't respond even after multiple follow ups, so I ended up crawling the website to get categories, subcategories and description information. Some of the subjects do not have category/subcategory/description as well. I am using a subject to categories, subcategories and description map (metadata file).
Currently the loading of the dataset works for "asf/amc" and "avi" formats since they have a single download link. But "c3d" and "mpg" have multiple download links (part archives) and dl_manager.download_and_extract() extracts the files to multiple paths, is there a way to extract these multiple archives into one folder ? Any other way to go about this ?
Any suggestions/inputs on this would be helpful. Thank you.
|
closed
|
https://github.com/huggingface/datasets/pull/4208
| 2022-04-24T17:31:08
| 2022-10-03T09:38:24
| 2022-10-03T09:36:30
|
{
"login": "dnaveenr",
"id": 17746528,
"type": "User"
}
|
[
{
"name": "dataset contribution",
"color": "0e8a16"
}
] | true
|
[] |
1,213,604,615
| 4,207
|
[Minor edit] Fix typo in class name
|
Typo: `datasets.DatsetDict` -> `datasets.DatasetDict`
|
closed
|
https://github.com/huggingface/datasets/pull/4207
| 2022-04-24T09:49:37
| 2022-05-05T13:17:47
| 2022-05-05T13:17:47
|
{
"login": "cakiki",
"id": 3664563,
"type": "User"
}
|
[] | true
|
[] |
1,212,715,581
| 4,206
|
Add Nerval Metric
|
This PR adds readme.md and ner_val.py to metrics.
Nerval is a python package that helps evaluate NER models. It creates classification report and confusion matrix at entity level.
|
closed
|
https://github.com/huggingface/datasets/pull/4206
| 2022-04-22T19:45:00
| 2023-07-11T09:34:56
| 2023-07-11T09:34:55
|
{
"login": "maridda",
"id": 49372461,
"type": "User"
}
|
[
{
"name": "transfer-to-evaluate",
"color": "E3165C"
}
] | true
|
[] |
1,212,466,138
| 4,205
|
Fix `convert_file_size_to_int` for kilobits and megabits
|
Minor change to fully align this function with the recent change in Transformers (https://github.com/huggingface/transformers/pull/16891)
|
closed
|
https://github.com/huggingface/datasets/pull/4205
| 2022-04-22T14:56:21
| 2022-05-03T15:28:42
| 2022-05-03T15:21:48
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
1,212,431,764
| 4,204
|
Add Recall Metric Card
|
What this PR mainly does:
- add metric card for recall metric
- update docs in recall python file
Note: I've also included a .json file with all of the metric card information. I've started compiling the relevant information in this type of .json files, and then using a script I wrote to generate the formatted metric card, as well as the docs to go in the .py file. I figured I'd upload the .json because it could be useful, especially if I also make a PR with the script I'm using (let me know if that's something you think would be beneficial!)
|
closed
|
https://github.com/huggingface/datasets/pull/4204
| 2022-04-22T14:24:26
| 2022-05-03T13:23:23
| 2022-05-03T13:16:24
|
{
"login": "emibaylor",
"id": 27527747,
"type": "User"
}
|
[] | true
|
[] |
1,212,431,067
| 4,203
|
Add Precision Metric Card
|
What this PR mainly does:
- add metric card for precision metric
- update docs in precision python file
Note: I've also included a .json file with all of the metric card information. I've started compiling the relevant information in this type of .json files, and then using a script I wrote to generate the formatted metric card, as well as the docs to go in the .py file. I figured I'd upload the .json because it could be useful, especially if I also make a PR with the script I'm using (let me know if that's something you think would be beneficial!)
|
closed
|
https://github.com/huggingface/datasets/pull/4203
| 2022-04-22T14:23:48
| 2022-05-03T14:23:40
| 2022-05-03T14:16:46
|
{
"login": "emibaylor",
"id": 27527747,
"type": "User"
}
|
[] | true
|
[] |
1,212,326,288
| 4,202
|
Fix some type annotation in doc
| null |
closed
|
https://github.com/huggingface/datasets/pull/4202
| 2022-04-22T12:53:31
| 2022-04-22T15:03:00
| 2022-04-22T14:56:43
|
{
"login": "thomasw21",
"id": 24695242,
"type": "User"
}
|
[] | true
|
[] |
1,212,086,420
| 4,201
|
Update GH template for dataset viewer issues
|
Update template to use new issue forms instead.
With this PR we can check if this new feature is useful for us.
Once validated, we can update the other templates.
CC: @severo
|
closed
|
https://github.com/huggingface/datasets/pull/4201
| 2022-04-22T09:34:44
| 2022-05-06T08:38:43
| 2022-04-26T08:45:55
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
1,211,980,110
| 4,200
|
Add to docs how to load from local script
|
This option was missing from the docs guide (it was only explained in the docstring of `load_dataset`). Although this is an infrequent use case, there might be some users interested in it.
Related to #4192
CC: @stevhliu
|
closed
|
https://github.com/huggingface/datasets/pull/4200
| 2022-04-22T08:08:25
| 2022-05-06T08:39:25
| 2022-04-23T05:47:25
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
1,211,953,308
| 4,199
|
Cache miss during reload for datasets using image fetch utilities through map
|
## Describe the bug
It looks like that result of `.map` operation dataset are missing the cache when you reload the script and always run from scratch. In same interpretor session, they are able to find the cache and reload it. But, when you exit the interpretor and reload it, the downloading starts from scratch.
## Steps to reproduce the bug
Using the example provided in `red_caps` dataset.
```python
from concurrent.futures import ThreadPoolExecutor
from functools import partial
import io
import urllib
import PIL.Image
import datasets
from datasets import load_dataset
from datasets.utils.file_utils import get_datasets_user_agent
def fetch_single_image(image_url, timeout=None, retries=0):
for _ in range(retries + 1):
try:
request = urllib.request.Request(
image_url,
data=None,
headers={"user-agent": get_datasets_user_agent()},
)
with urllib.request.urlopen(request, timeout=timeout) as req:
image = PIL.Image.open(io.BytesIO(req.read()))
break
except Exception:
image = None
return image
def fetch_images(batch, num_threads, timeout=None, retries=0):
fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries)
with ThreadPoolExecutor(max_workers=num_threads) as executor:
batch["image"] = list(executor.map(lambda image_urls: [fetch_single_image_with_args(image_url) for image_url in image_urls], batch["image_url"]))
return batch
def process_image_urls(batch):
processed_batch_image_urls = []
for image_url in batch["image_url"]:
processed_example_image_urls = []
image_url_splits = re.findall(r"http\S+", image_url)
for image_url_split in image_url_splits:
if "imgur" in image_url_split and "," in image_url_split:
for image_url_part in image_url_split.split(","):
if not image_url_part:
continue
image_url_part = image_url_part.strip()
root, ext = os.path.splitext(image_url_part)
if not root.startswith("http"):
root = "http://i.imgur.com/" + root
root = root.split("#")[0]
if not ext:
ext = ".jpg"
ext = re.split(r"[?%]", ext)[0]
image_url_part = root + ext
processed_example_image_urls.append(image_url_part)
else:
processed_example_image_urls.append(image_url_split)
processed_batch_image_urls.append(processed_example_image_urls)
batch["image_url"] = processed_batch_image_urls
return batch
dset = load_dataset("red_caps", "jellyfish")
dset = dset.map(process_image_urls, batched=True, num_proc=4)
features = dset["train"].features.copy()
features["image"] = datasets.Sequence(datasets.Image())
num_threads = 5
dset = dset.map(fetch_images, batched=True, batch_size=50, features=features, fn_kwargs={"num_threads": num_threads})
```
Run this in an interpretor or as a script twice and see that the cache is missed the second time.
## Expected results
At reload there should not be any cache miss
## Actual results
Every time script is run, cache is missed and dataset is built from scratch.
## Environment info
- `datasets` version: 2.1.1.dev0
- Platform: Linux-4.19.0-20-cloud-amd64-x86_64-with-glibc2.10
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.1
|
closed
|
https://github.com/huggingface/datasets/issues/4199
| 2022-04-22T07:47:08
| 2022-04-26T17:00:32
| 2022-04-26T13:38:26
|
{
"login": "apsdehal",
"id": 3616806,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
1,211,456,559
| 4,198
|
There is no dataset
|
## Dataset viewer issue for '*name of the dataset*'
**Link:** *link to the dataset viewer page*
*short description of the issue*
Am I the one who added this dataset ? Yes-No
|
closed
|
https://github.com/huggingface/datasets/issues/4198
| 2022-04-21T19:19:26
| 2022-05-03T11:29:05
| 2022-04-22T06:12:25
|
{
"login": "wilfoderek",
"id": 1625647,
"type": "User"
}
|
[] | false
|
[] |
1,211,342,558
| 4,197
|
Add remove_columns=True
|
This should fix all the issue we have with in place operations in mapping functions. This is crucial as where we do some weird things like:
```
def apply(batch):
batch_size = len(batch["id"])
batch["text"] = ["potato" for _ range(batch_size)]
return {}
# Columns are: {"id": int}
dset.map(apply, batched=True, remove_columns="text") # crashes because `text` is not in the original columns
dset.map(apply, batched=True) # mapped datasets has `text` column
```
In this PR we suggest to have `remove_columns=True` so that we ignore the input completely, and just use the output to generate mapped dataset. This means that inplace operations won't have any effects anymore.
|
closed
|
https://github.com/huggingface/datasets/pull/4197
| 2022-04-21T17:28:13
| 2023-09-24T10:02:32
| 2022-04-22T14:45:30
|
{
"login": "thomasw21",
"id": 24695242,
"type": "User"
}
|
[] | true
|
[] |
1,211,271,261
| 4,196
|
Embed image and audio files in `save_to_disk`
|
Following https://github.com/huggingface/datasets/pull/4184, currently a dataset saved using `save_to_disk` doesn't actually contain the bytes of the image or audio files. Instead it stores the path to your local files.
Adding `embed_external_files` and set it to True by default to save_to_disk would be kind of a breaking change since some users will get bigger Arrow files when updating the lib, but the advantages are nice:
- the resulting dataset is self contained, in case you want to delete your cache for example or share it with someone else
- users also upload these Arrow files to cloud storage via the fs parameter, and in this case they would expect to upload a self-contained dataset
- consistency with push_to_hub
This can be implemented at the same time as sharding for `save_to_disk` for efficiency, and reuse the helpers from `push_to_hub` to embed the external files.
cc @mariosasko
|
closed
|
https://github.com/huggingface/datasets/issues/4196
| 2022-04-21T16:25:18
| 2022-12-14T18:22:59
| 2022-12-14T18:22:59
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | false
|
[] |
1,210,958,602
| 4,194
|
Support lists of multi-dimensional numpy arrays
|
Fix #4191.
CC: @SaulLu
|
closed
|
https://github.com/huggingface/datasets/pull/4194
| 2022-04-21T12:22:26
| 2022-05-12T15:16:34
| 2022-05-12T15:08:40
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
1,210,734,701
| 4,193
|
Document save_to_disk and push_to_hub on images and audio files
|
Following https://github.com/huggingface/datasets/pull/4187, I explained in the documentation of `save_to_disk` and `push_to_hub` how they handle image and audio data.
|
closed
|
https://github.com/huggingface/datasets/pull/4193
| 2022-04-21T09:04:36
| 2022-04-22T09:55:55
| 2022-04-22T09:49:31
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
1,210,692,554
| 4,192
|
load_dataset can't load local dataset,Unable to find ...
|
Traceback (most recent call last):
File "/home/gs603/ahf/pretrained/model.py", line 48, in <module>
dataset = load_dataset("json",data_files="dataset/dataset_infos.json")
File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1675, in load_dataset
**config_kwargs,
File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1496, in load_dataset_builder
data_files=data_files,
File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 1155, in dataset_module_factory
download_mode=download_mode,
File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/load.py", line 800, in get_module
data_files = DataFilesDict.from_local_or_remote(patterns, use_auth_token=self.downnload_config.use_auth_token)
File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 582, in from_local_or_remote
if not isinstance(patterns_for_key, DataFilesList)
File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 544, in from_local_or_remote
data_files = resolve_patterns_locally_or_by_urls(base_path, patterns, allowed_extensions)
File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 194, in resolve_patterns_locally_or_by_urls
for path in _resolve_single_pattern_locally(base_path, pattern, allowed_extensions):
File "/home/gs603/miniconda3/envs/coderepair/lib/python3.7/site-packages/datasets/data_files.py", line 144, in _resolve_single_pattern_locally
raise FileNotFoundError(error_msg)
FileNotFoundError: Unable to find '/home/gs603/ahf/pretrained/dataset/dataset_infos.json' at /home/gs603/ahf/pretrained
the code is in the model.py,why I can't use the load_dataset function to load my local dataset?
|
closed
|
https://github.com/huggingface/datasets/issues/4192
| 2022-04-21T08:28:58
| 2022-04-25T16:51:57
| 2022-04-22T07:39:53
|
{
"login": "ahf876828330",
"id": 33253979,
"type": "User"
}
|
[
{
"name": "bug",
"color": "d73a4a"
}
] | false
|
[] |
1,210,028,090
| 4,191
|
feat: create an `Array3D` column from a list of arrays of dimension 2
|
**Is your feature request related to a problem? Please describe.**
It is possible to create an `Array2D` column from a list of arrays of dimension 1. Similarly, I think it might be nice to be able to create a `Array3D` column from a list of lists of arrays of dimension 1.
To illustrate my proposal, let's take the following toy dataset t:
```python
import numpy as np
from datasets import Dataset, features
data_map = {
1: np.array([[0.2, 0,4],[0.19, 0,3]]),
2: np.array([[0.1, 0,4],[0.19, 0,3]]),
}
def create_toy_ds():
my_dict = {"id":[1, 2]}
return Dataset.from_dict(my_dict)
ds = create_toy_ds()
```
The following 2D processing works without any errors raised:
```python
def prepare_dataset_2D(batch):
batch["pixel_values"] = [data_map[index] for index in batch["id"]]
return batch
ds_2D = ds.map(
prepare_dataset_2D,
batched=True,
remove_columns=ds.column_names,
features=features.Features({"pixel_values": features.Array2D(shape=(2, 3), dtype="float32")})
)
```
The following 3D processing doesn't work:
```python
def prepare_dataset_3D(batch):
batch["pixel_values"] = [[data_map[index]] for index in batch["id"]]
return batch
ds_3D = ds.map(
prepare_dataset_3D,
batched=True,
remove_columns=ds.column_names,
features=features.Features({"pixel_values": features.Array3D(shape=(1, 2, 3, dtype="float32")})
)
```
The error raised is:
```
---------------------------------------------------------------------------
ArrowInvalid Traceback (most recent call last)
[<ipython-input-6-676547e4cd41>](https://localhost:8080/#) in <module>()
3 batched=True,
4 remove_columns=ds.column_names,
----> 5 features=features.Features({"pixel_values": features.Array3D(shape=(1, 2, 3), dtype="float32")})
6 )
12 frames
[/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in map(self, function, with_indices, with_rank, 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, desc)
1971 new_fingerprint=new_fingerprint,
1972 disable_tqdm=disable_tqdm,
-> 1973 desc=desc,
1974 )
1975 else:
[/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in wrapper(*args, **kwargs)
518 self: "Dataset" = kwargs.pop("self")
519 # apply actual function
--> 520 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
521 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
522 for dataset in datasets:
[/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in wrapper(*args, **kwargs)
485 }
486 # apply actual function
--> 487 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
488 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
489 # re-apply format to the output
[/usr/local/lib/python3.7/dist-packages/datasets/fingerprint.py](https://localhost:8080/#) in wrapper(*args, **kwargs)
456 # Call actual function
457
--> 458 out = func(self, *args, **kwargs)
459
460 # Update fingerprint of in-place transforms + update in-place history of transforms
[/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in _map_single(self, function, with_indices, with_rank, 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, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)
2354 writer.write_table(batch)
2355 else:
-> 2356 writer.write_batch(batch)
2357 if update_data and writer is not None:
2358 writer.finalize() # close_stream=bool(buf_writer is None)) # We only close if we are writing in a file
[/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py](https://localhost:8080/#) in write_batch(self, batch_examples, writer_batch_size)
505 col_try_type = try_features[col] if try_features is not None and col in try_features else None
506 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col)
--> 507 arrays.append(pa.array(typed_sequence))
508 inferred_features[col] = typed_sequence.get_inferred_type()
509 schema = inferred_features.arrow_schema if self.pa_writer is None else self.schema
/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.array()
/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol()
[/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py](https://localhost:8080/#) in __arrow_array__(self, type)
175 storage = list_of_np_array_to_pyarrow_listarray(data, type=pa_type.value_type)
176 else:
--> 177 storage = pa.array(data, pa_type.storage_dtype)
178 return pa.ExtensionArray.from_storage(pa_type, storage)
179
/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib.array()
/usr/local/lib/python3.7/dist-packages/pyarrow/array.pxi in pyarrow.lib._sequence_to_array()
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowInvalid: Can only convert 1-dimensional array values
```
**Describe the solution you'd like**
No error in the second scenario and an identical result to the following snippets.
**Describe alternatives you've considered**
There are other alternatives that work such as:
```python
def prepare_dataset_3D_bis(batch):
batch["pixel_values"] = [[data_map[index].tolist()] for index in batch["id"]]
return batch
ds_3D_bis = ds.map(
prepare_dataset_3D_bis,
batched=True,
remove_columns=ds.column_names,
features=features.Features({"pixel_values": features.Array3D(shape=(1, 2, 3), dtype="float32")})
)
```
or
```python
def prepare_dataset_3D_ter(batch):
batch["pixel_values"] = [data_map[index][np.newaxis, :, :] for index in batch["id"]]
return batch
ds_3D_ter = ds.map(
prepare_dataset_3D_ter,
batched=True,
remove_columns=ds.column_names,
features=features.Features({"pixel_values": features.Array3D(shape=(1, 2, 3), dtype="float32")})
)
```
But both solutions require the user to be aware that `data_map[index]` is an `np.array` type.
cc @lhoestq as we discuss this offline :smile:
|
closed
|
https://github.com/huggingface/datasets/issues/4191
| 2022-04-20T18:04:32
| 2022-05-12T15:08:40
| 2022-05-12T15:08:40
|
{
"login": "SaulLu",
"id": 55560583,
"type": "User"
}
|
[
{
"name": "enhancement",
"color": "a2eeef"
}
] | false
|
[] |
1,209,901,677
| 4,190
|
Deprecate `shard_size` in `push_to_hub` in favor of `max_shard_size`
|
This PR adds a `max_shard_size` param to `push_to_hub` and deprecates `shard_size` in favor of this new param to have a more descriptive name (a shard has at most the `shard_size` bytes in `push_to_hub`) for the param and to align the API with [Transformers](https://github.com/huggingface/transformers/blob/ff06b177917384137af2d9585697d2d76c40cdfc/src/transformers/modeling_utils.py#L1350).
|
closed
|
https://github.com/huggingface/datasets/pull/4190
| 2022-04-20T16:08:01
| 2022-04-22T13:58:25
| 2022-04-22T13:52:00
|
{
"login": "mariosasko",
"id": 47462742,
"type": "User"
}
|
[] | true
|
[] |
1,209,881,351
| 4,189
|
Document how to use FAISS index for special operations
|
Document how to use FAISS index for special operations, by accessing the index itself.
Close #4029.
|
closed
|
https://github.com/huggingface/datasets/pull/4189
| 2022-04-20T15:51:56
| 2022-05-06T08:43:10
| 2022-05-06T08:35:52
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
1,209,740,957
| 4,188
|
Support streaming cnn_dailymail dataset
|
Support streaming cnn_dailymail dataset.
Fix #3969.
CC: @severo
|
closed
|
https://github.com/huggingface/datasets/pull/4188
| 2022-04-20T14:04:36
| 2022-05-11T13:39:06
| 2022-04-20T15:52:49
|
{
"login": "albertvillanova",
"id": 8515462,
"type": "User"
}
|
[] | true
|
[] |
1,209,721,532
| 4,187
|
Don't duplicate data when encoding audio or image
|
Right now if you pass both the `bytes` and a local `path` for audio or image data, then the `bytes` are unnecessarily written in the Arrow file, while we could just keep the local `path`.
This PR discards the `bytes` when the audio or image file exists locally.
In particular it's common for audio datasets builders to provide both the bytes and the local path in order to work for both streaming (using the bytes) and non-streaming mode (using a local file - which is often required for audio).
cc @patrickvonplaten
|
closed
|
https://github.com/huggingface/datasets/pull/4187
| 2022-04-20T13:50:37
| 2022-04-21T09:17:00
| 2022-04-21T09:10:47
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
1,209,463,599
| 4,186
|
Fix outdated docstring about default dataset config
| null |
closed
|
https://github.com/huggingface/datasets/pull/4186
| 2022-04-20T10:04:51
| 2022-04-22T12:54:44
| 2022-04-22T12:48:31
|
{
"login": "lhoestq",
"id": 42851186,
"type": "User"
}
|
[] | true
|
[] |
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