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https://api.github.com/repos/huggingface/datasets/issues/318 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/318/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/318/comments | https://api.github.com/repos/huggingface/datasets/issues/318/events | https://github.com/huggingface/datasets/pull/318 | 646,682,840 | MDExOlB1bGxSZXF1ZXN0NDQwOTExOTYy | 318 | Multitask | {
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} | Following our discussion in #217, I've implemented a first working version of `MultiDataset`.
There's a function `build_multitask()` which takes either individual `nlp.Dataset`s or `dicts` of splits and constructs `MultiDataset`(s). I've added a notebook with example usage.
I've implemented many of the `nlp.Dataset` methods (cache_files, columns, nbytes, num_columns, num_rows, column_names, schema, shape). Some of the other methods are complicated as they change the number of examples. These raise `NotImplementedError`s at the moment.
This will need some tests which I haven't written yet.
There's definitely room for improvements but I think the general approach is sound. | {
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} | [] | closed | false | null | [] | null | 1 | "2020-06-26T23:14:19Z" | "2020-10-27T15:36:52Z" | "2020-10-27T15:36:52Z" | NONE | null | null | null | I intent to add the datasets of the MT Quality Estimation shared tasks to `nlp`. However, they have different subtasks -- such as word-level, sentence-level and document-level quality estimation, each of which having different language pairs, and some of the data reused in different subtasks.
For example, in [QE 2019,](http://www.statmt.org/wmt19/qe-task.html) we had the same English-Russian and English-German data for word-level and sentence-level QE.
I suppose these datasets could have both their word and sentence-level labels inside `nlp.Features`; but what about other subtasks? Should they be considered a different dataset altogether?
I read the discussion on #217 but the case of QE seems a lot simpler. | {
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} | adds support for the AG-News topic classification dataset | {
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] | open | false | null | [] | null | 11 | "2020-06-25T22:30:20Z" | "2020-10-27T15:38:17Z" | null | CONTRIBUTOR | null | null | null | I'm training on large datasets such as Wikipedia and BookCorpus. Following the instructions in [the tutorial notebook](https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb), I see the following recommended for TensorFlow:
```python
train_tf_dataset = train_tf_dataset.filter(remove_none_values, load_from_cache_file=False)
columns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']
train_tf_dataset.set_format(type='tensorflow', columns=columns)
features = {x: train_tf_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.max_len]) for x in columns[:3]}
labels = {"output_1": train_tf_dataset["start_positions"].to_tensor(default_value=0, shape=[None, 1])}
labels["output_2"] = train_tf_dataset["end_positions"].to_tensor(default_value=0, shape=[None, 1])
### Question about this last line ###
tfdataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(8)
```
This code works for something like WikiText-2. However, scaling up to WikiText-103, the last line takes 5-10 minutes to run. I assume it is because tf.data.Dataset.from_tensor_slices() is pulling everything into memory, not lazily loading. This approach won't scale up to datasets 25x larger such as Wikipedia.
So I tried manual batching using `dataset.select()`:
```python
idxs = np.random.randint(len(dataset), size=bsz)
batch = dataset.select(idxs).map(lambda example: {"input_ids": tokenizer(example["text"])})
tf_batch = tf.constant(batch["ids"], dtype=tf.int64)
```
This appears to create a new Apache Arrow dataset with every batch I grab, and then tries to cache it. The runtime of `dataset.select([0, 1])` appears to be much worse than `dataset[:2]`. So using `select()` doesn't seem to be performant enough for a training loop.
Is there a performant scalable way to lazily load batches of nlp Datasets? | {
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} | Adding the [Modified Winograd Schema Challenge](https://github.com/salesforce/decaNLP/blob/master/local_data/schema.txt) dataset which formed part of the [decaNLP](http://decanlp.com/) benchmark. Not sure how much use people would find for it it outside of the benchmark, but it is general purpose.
Code is heavily borrowed from the [decaNLP repo](https://github.com/salesforce/decaNLP/blob/1e9605f246b9e05199b28bde2a2093bc49feeeaa/text/torchtext/datasets/generic.py#L773-L877).
There's a few (possibly overly opinionated) design choices I made:
- I used the train/test/dev split [buried in the decaNLP code](https://github.com/salesforce/decaNLP/blob/1e9605f246b9e05199b28bde2a2093bc49feeeaa/text/torchtext/datasets/generic.py#L852-L855)
- I split out each example into the 2 alternatives. Originally the data uses the format:
```
The city councilmen refused the demonstrators a permit because they [feared/advocated] violence.
Who [feared/advocated] violence?
councilmen/demonstrators
```
I split into the 2 variants:
```
The city councilmen refused the demonstrators a permit because they feared violence.
Who feared violence?
councilmen/demonstrators
The city councilmen refused the demonstrators a permit because they advocated violence.
Who advocated violence?
councilmen/demonstrators
```
I can't see any use for having the options combined into a single example (splitting them is [the way decaNLP processes](https://github.com/salesforce/decaNLP/blob/1e9605f246b9e05199b28bde2a2093bc49feeeaa/text/torchtext/datasets/generic.py#L846-L850)) them. You can't train on both versions with them combined, and splitting the examples later would be a pain to do. I think [winogrande.py](https://github.com/huggingface/nlp/blob/master/datasets/winogrande/winogrande.py) presents the data in this way?
- I've not used the decaNLP framing (appending the options to the question e.g. `Who feared violence?
-- councilmen or demonstrators?`) but left it more generic by adding the options as a new key: `"options":["councilmen","demonstrators"]` This should be an easy thing to change using `map` if needed by a specific application.
Dataset is working as-is but if anyone has any thoughts/preferences on the design decisions here I'm definitely open to different choices. | {
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https://api.github.com/repos/huggingface/datasets/issues/312 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/312/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/312/comments | https://api.github.com/repos/huggingface/datasets/issues/312/events | https://github.com/huggingface/datasets/issues/312 | 645,025,561 | MDU6SXNzdWU2NDUwMjU1NjE= | 312 | [Feature request] Add `shard()` method to dataset | {
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} | [] | closed | false | null | [] | null | 2 | "2020-06-24T22:48:33Z" | "2020-07-06T12:35:36Z" | "2020-07-06T12:35:36Z" | CONTRIBUTOR | null | null | null | Currently, to shard a dataset into 10 pieces on different ranks, you can run
```python
rank = 3 # for example
size = 10
dataset = nlp.load_dataset('wikitext', 'wikitext-2-raw-v1', split=f"train[{rank*10}%:{(rank+1)*10}%]")
```
However, this breaks down if you have a number of ranks that doesn't divide cleanly into 100, such as 64 ranks. Is there interest in adding a method shard() that looks like this?
```python
rank = 3
size = 64
dataset = nlp.load_dataset("wikitext", "wikitext-2-raw-v1", split="train").shard(rank=rank, size=size)
```
TensorFlow has a similar API: https://www.tensorflow.org/api_docs/python/tf/data/Dataset#shard. I'd be happy to contribute this code. | {
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https://api.github.com/repos/huggingface/datasets/issues/311 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/311/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/311/comments | https://api.github.com/repos/huggingface/datasets/issues/311/events | https://github.com/huggingface/datasets/pull/311 | 645,013,131 | MDExOlB1bGxSZXF1ZXN0NDM5NTQ3OTg0 | 311 | Add qa_zre | {
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} | [] | closed | false | null | [] | null | 0 | "2020-06-24T22:17:22Z" | "2020-06-29T16:37:38Z" | "2020-06-29T16:37:38Z" | CONTRIBUTOR | null | 0 | {
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} | Adding the QA-ZRE dataset from ["Zero-Shot Relation Extraction via Reading Comprehension"](http://nlp.cs.washington.edu/zeroshot/).
A common processing step seems to be replacing the `XXX` placeholder with the `subject`. I've left this out as it's something you could easily do with `map`. | {
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https://api.github.com/repos/huggingface/datasets/issues/310 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/310/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/310/comments | https://api.github.com/repos/huggingface/datasets/issues/310/events | https://github.com/huggingface/datasets/pull/310 | 644,806,720 | MDExOlB1bGxSZXF1ZXN0NDM5MzY1MDg5 | 310 | add wikisql | {
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} | [] | closed | false | null | [] | null | 1 | "2020-06-24T18:00:35Z" | "2020-06-25T12:32:25Z" | "2020-06-25T12:32:25Z" | CONTRIBUTOR | null | 0 | {
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} | Adding the [WikiSQL](https://github.com/salesforce/WikiSQL) dataset.
Interesting things to note:
- Have copied the function (`_convert_to_human_readable`) which converts the SQL query to a human-readable (string) format as this is what most people will want when actually using this dataset for NLP applications.
- `conds` was originally a tuple but is converted to a dictionary to support differing types.
Would be nice to add the logical_form metrics too at some point. | {
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https://api.github.com/repos/huggingface/datasets/issues/309 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/309/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/309/comments | https://api.github.com/repos/huggingface/datasets/issues/309/events | https://github.com/huggingface/datasets/pull/309 | 644,783,822 | MDExOlB1bGxSZXF1ZXN0NDM5MzQ1NzYz | 309 | Add narrative qa | {
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} | [] | closed | false | null | [] | null | 11 | "2020-06-24T17:26:18Z" | "2020-09-03T09:02:10Z" | "2020-09-03T09:02:09Z" | NONE | null | 0 | {
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} | Test cases for dummy data don't pass
Only contains data for summaries (not whole story) | {
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https://api.github.com/repos/huggingface/datasets/issues/308 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/308/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/308/comments | https://api.github.com/repos/huggingface/datasets/issues/308/events | https://github.com/huggingface/datasets/pull/308 | 644,195,251 | MDExOlB1bGxSZXF1ZXN0NDM4ODYyMzYy | 308 | Specify utf-8 encoding for MRPC files | {
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} | Fixes #307, again probably a Windows-related issue. | {
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https://api.github.com/repos/huggingface/datasets/issues/307 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/307/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/307/comments | https://api.github.com/repos/huggingface/datasets/issues/307/events | https://github.com/huggingface/datasets/issues/307 | 644,187,262 | MDU6SXNzdWU2NDQxODcyNjI= | 307 | Specify encoding for MRPC | {
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} | [] | closed | false | null | [] | null | 0 | "2020-06-23T22:24:49Z" | "2020-06-25T12:16:09Z" | "2020-06-25T12:16:09Z" | CONTRIBUTOR | null | null | null | Same as #242, but with MRPC: on Windows, I get a `UnicodeDecodeError` when I try to download the dataset:
```python
dataset = nlp.load_dataset('glue', 'mrpc')
```
```python
Downloading and preparing dataset glue/mrpc (download: Unknown size, generated: Unknown size, total: Unknown size) to C:\Users\Python\.cache\huggingface\datasets\glue\mrpc\1.0.0...
---------------------------------------------------------------------------
UnicodeDecodeError Traceback (most recent call last)
~\Miniconda3\envs\nlp\lib\site-packages\nlp\builder.py in incomplete_dir(dirname)
369 try:
--> 370 yield tmp_dir
371 if os.path.isdir(dirname):
~\Miniconda3\envs\nlp\lib\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)
430 verify_infos = not save_infos and not ignore_verifications
--> 431 self._download_and_prepare(
432 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
~\Miniconda3\envs\nlp\lib\site-packages\nlp\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
482 # Prepare split will record examples associated to the split
--> 483 self._prepare_split(split_generator, **prepare_split_kwargs)
484 except OSError:
~\Miniconda3\envs\nlp\lib\site-packages\nlp\builder.py in _prepare_split(self, split_generator)
663 generator = self._generate_examples(**split_generator.gen_kwargs)
--> 664 for key, record in utils.tqdm(generator, unit=" examples", total=split_info.num_examples, leave=False):
665 example = self.info.features.encode_example(record)
~\Miniconda3\envs\nlp\lib\site-packages\tqdm\notebook.py in __iter__(self, *args, **kwargs)
217 try:
--> 218 for obj in super(tqdm_notebook, self).__iter__(*args, **kwargs):
219 # return super(tqdm...) will not catch exception
~\Miniconda3\envs\nlp\lib\site-packages\tqdm\std.py in __iter__(self)
1128 try:
-> 1129 for obj in iterable:
1130 yield obj
~\Miniconda3\envs\nlp\lib\site-packages\nlp\datasets\glue\7fc58099eb3983a04c8dac8500b70d27e6eceae63ffb40d7900c977897bb58c6\glue.py in _generate_examples(self, data_file, split, mrpc_files)
514 examples = self._generate_example_mrpc_files(mrpc_files=mrpc_files, split=split)
--> 515 for example in examples:
516 yield example["idx"], example
~\Miniconda3\envs\nlp\lib\site-packages\nlp\datasets\glue\7fc58099eb3983a04c8dac8500b70d27e6eceae63ffb40d7900c977897bb58c6\glue.py in _generate_example_mrpc_files(self, mrpc_files, split)
576 reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
--> 577 for n, row in enumerate(reader):
578 is_row_in_dev = [row["#1 ID"], row["#2 ID"]] in dev_ids
~\Miniconda3\envs\nlp\lib\csv.py in __next__(self)
110 self.fieldnames
--> 111 row = next(self.reader)
112 self.line_num = self.reader.line_num
~\Miniconda3\envs\nlp\lib\encodings\cp1252.py in decode(self, input, final)
22 def decode(self, input, final=False):
---> 23 return codecs.charmap_decode(input,self.errors,decoding_table)[0]
24
UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 1180: character maps to <undefined>
```
The fix is the same: specify `utf-8` encoding when opening the file. The previous fix didn't work as MRPC's download process is different from the others in GLUE.
I am going to propose a new PR :) | {
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} | https://github.com/huggingface/nlp/issues/274
Add functioning PG19 dataset with dummy data
`cos_e.py` was just auto-linted by `make style` | {
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https://api.github.com/repos/huggingface/datasets/issues/305 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/305/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/305/comments | https://api.github.com/repos/huggingface/datasets/issues/305/events | https://github.com/huggingface/datasets/issues/305 | 644,148,149 | MDU6SXNzdWU2NDQxNDgxNDk= | 305 | Importing downloaded package repository fails | {
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] | closed | false | null | [] | null | 0 | "2020-06-23T21:09:05Z" | "2020-07-30T16:44:23Z" | "2020-07-30T16:44:23Z" | MEMBER | null | null | null | The `get_imports` function in `src/nlp/load.py` has a feature to download a package as a zip archive of the github repository and import functions from the unpacked directory. This is used for example in the `metrics/coval.py` file, and would be useful to add BLEURT (@ankparikh).
Currently however, the code seems to have trouble with imports within the package. For example:
```
import nlp
coval = nlp.load_metric('coval')
```
yields:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/yacine/Code/nlp/src/nlp/load.py", line 432, in load_metric
metric_cls = import_main_class(module_path, dataset=False)
File "/home/yacine/Code/nlp/src/nlp/load.py", line 57, in import_main_class
module = importlib.import_module(module_path)
File "/home/yacine/anaconda3/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1006, in _gcd_import
File "<frozen importlib._bootstrap>", line 983, in _find_and_load
File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 728, in exec_module
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "/home/yacine/Code/nlp/src/nlp/metrics/coval/a78807df33ac45edbb71799caf2b3b47e55df4fd690267808fe963a5e8b30952/coval.py", line 21, in <module>
from .coval_backend.conll import reader # From: https://github.com/ns-moosavi/coval
File "/home/yacine/Code/nlp/src/nlp/metrics/coval/a78807df33ac45edbb71799caf2b3b47e55df4fd690267808fe963a5e8b30952/coval_backend/conll/reader.py", line 2, in <module>
from conll import mention
ModuleNotFoundError: No module named 'conll'
```
Not sure what the fix would be there. | {
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https://api.github.com/repos/huggingface/datasets/issues/304 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/304/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/304/comments | https://api.github.com/repos/huggingface/datasets/issues/304/events | https://github.com/huggingface/datasets/issues/304 | 644,091,970 | MDU6SXNzdWU2NDQwOTE5NzA= | 304 | Problem while printing doc string when instantiating multiple metrics. | {
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] | closed | false | null | [] | null | 0 | "2020-06-23T19:32:05Z" | "2020-07-22T09:50:58Z" | "2020-07-22T09:50:58Z" | CONTRIBUTOR | null | null | null | When I load more than one metric and try to print doc string of a particular metric,. It shows the doc strings of all imported metric one after the other which looks quite confusing and clumsy.
Attached [Colab](https://colab.research.google.com/drive/13H0ZgyQ2se0mqJ2yyew0bNEgJuHaJ8H3?usp=sharing) Notebook for problem clarification.. | {
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https://api.github.com/repos/huggingface/datasets/issues/303 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/303/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/303/comments | https://api.github.com/repos/huggingface/datasets/issues/303/events | https://github.com/huggingface/datasets/pull/303 | 643,912,464 | MDExOlB1bGxSZXF1ZXN0NDM4NjI3Nzcw | 303 | allow to move files across file systems | {
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} | Users are allowed to use the `cache_dir` that they want.
Therefore it can happen that we try to move files across filesystems.
We were using `os.rename` that doesn't allow that, so I changed some of them to `shutil.move`.
This should fix #301 | {
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] | closed | false | null | [] | null | 3 | "2020-06-23T14:53:40Z" | "2020-11-25T11:25:33Z" | "2020-11-25T11:25:33Z" | CONTRIBUTOR | null | null | null | An emerging field in NLP is SLP - sign language processing.
I was wondering about adding datasets here, specifically because it's shaping up to be large and easily usable.
The metrics for sign language to text translation are the same.
So, what do you think about (me, or others) adding datasets here?
An example dataset would be [RWTH-PHOENIX-Weather 2014 T](https://www-i6.informatik.rwth-aachen.de/~koller/RWTH-PHOENIX-2014-T/)
For every item in the dataset, the data object includes:
1. video_path - path to mp4 file
2. pose_path - a path to `.pose` file with human pose landmarks
3. openpose_path - a path to a `.json` file with human pose landmarks
4. gloss - string
5. text - string
6. video_metadata - height, width, frames, framerate
------
To make it a tad more complicated - what if sign language libraries add requirements to `nlp`? for example, sign language is commonly annotated using `ilex`, `eaf`, or `srt` files, which are all loadable as text, but there is no reason for the dataset to parse that file by itself, if libraries exist to do so. | {
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https://api.github.com/repos/huggingface/datasets/issues/301 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/301/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/301/comments | https://api.github.com/repos/huggingface/datasets/issues/301/events | https://github.com/huggingface/datasets/issues/301 | 643,763,525 | MDU6SXNzdWU2NDM3NjM1MjU= | 301 | Setting cache_dir gives error on wikipedia download | {
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} | [] | closed | false | null | [] | null | 2 | "2020-06-23T11:31:44Z" | "2020-06-24T07:05:07Z" | "2020-06-24T07:05:07Z" | NONE | null | null | null | First of all thank you for a super handy library! I'd like to download large files to a specific drive so I set `cache_dir=my_path`. This works fine with e.g. imdb and squad. But on wikipedia I get an error:
```
nlp.load_dataset('wikipedia', '20200501.de', split = 'train', cache_dir=my_path)
```
```
OSError Traceback (most recent call last)
<ipython-input-2-23551344d7bc> in <module>
1 import nlp
----> 2 nlp.load_dataset('wikipedia', '20200501.de', split = 'train', cache_dir=path)
~/anaconda3/envs/fastai2/lib/python3.7/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)
522 download_mode=download_mode,
523 ignore_verifications=ignore_verifications,
--> 524 save_infos=save_infos,
525 )
526
~/anaconda3/envs/fastai2/lib/python3.7/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)
385 with utils.temporary_assignment(self, "_cache_dir", tmp_data_dir):
386 reader = ArrowReader(self._cache_dir, self.info)
--> 387 reader.download_from_hf_gcs(self._cache_dir, self._relative_data_dir(with_version=True))
388 downloaded_info = DatasetInfo.from_directory(self._cache_dir)
389 self.info.update(downloaded_info)
~/anaconda3/envs/fastai2/lib/python3.7/site-packages/nlp/arrow_reader.py in download_from_hf_gcs(self, cache_dir, relative_data_dir)
231 remote_dataset_info = os.path.join(remote_cache_dir, "dataset_info.json")
232 downloaded_dataset_info = cached_path(remote_dataset_info)
--> 233 os.rename(downloaded_dataset_info, os.path.join(cache_dir, "dataset_info.json"))
234 if self._info is not None:
235 self._info.update(self._info.from_directory(cache_dir))
OSError: [Errno 18] Invalid cross-device link: '/home/local/NTU/nn/.cache/huggingface/datasets/025fa4fd4f04aaafc9e939260fbc8f0bb190ce14c61310c8ae1ddd1dcb31f88c.9637f367b6711a79ca478be55fe6989b8aea4941b7ef7adc67b89ff403020947' -> '/data/nn/nlp/wikipedia/20200501.de/1.0.0.incomplete/dataset_info.json'
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/300 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/300/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/300/comments | https://api.github.com/repos/huggingface/datasets/issues/300/events | https://github.com/huggingface/datasets/pull/300 | 643,688,304 | MDExOlB1bGxSZXF1ZXN0NDM4NDQ4Mjk1 | 300 | Fix bertscore references | {
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} | I added some type checking for metrics. There was an issue where a metric could interpret a string a a list. A `ValueError` is raised if a string is given instead of a list.
Moreover I added support for both strings and lists of strings for `references` in `bertscore`, as it is the case in the original code.
Both ways work:
```
import nlp
scorer = nlp.load_metric("bertscore")
with open("pred.txt") as p, open("ref.txt") as g:
for lp, lg in zip(p, g):
scorer.add(lp, [lg])
score = scorer.compute(lang="en")
```
```
import nlp
scorer = nlp.load_metric("bertscore")
with open("pred.txt") as p, open("ref.txt") as g:
for lp, lg in zip(p, g):
scorer.add(lp, lg)
score = scorer.compute(lang="en")
```
This should fix #295 and #238 | {
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https://api.github.com/repos/huggingface/datasets/issues/299 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/299/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/299/comments | https://api.github.com/repos/huggingface/datasets/issues/299/events | https://github.com/huggingface/datasets/pull/299 | 643,611,557 | MDExOlB1bGxSZXF1ZXN0NDM4Mzg0NDgw | 299 | remove some print in snli file | {
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} | This PR removes unwanted `print` statements in some files such as `snli.py` | {
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} | [] | closed | false | null | [] | null | 8 | "2020-06-23T07:33:03Z" | "2020-06-26T07:50:44Z" | "2020-06-26T07:50:43Z" | MEMBER | null | 0 | {
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} | # Better support for Numpy format + Add Indexed Datasets
I was working on adding Indexed Datasets but in the meantime I had to also add more support for Numpy arrays in the lib.
## Better support for Numpy format
New features:
- New fast method to convert Numpy arrays from Arrow structure (up to x100 speed up) using Pandas.
- Allow to output Numpy arrays in batched `.map`, which was the only missing part to fully support Numpy arrays.
Pandas offers fast zero-copy Numpy arrays conversion from Arrow structures.
Using it we can speed up the reading of memory-mapped Numpy array stored in Arrow format.
With these changes you can easily compute embeddings of texts using `.map()`. For example:
```python
def embed(text):
tokenized_example = tokenizer.encode(text, return_tensors="pt")
embeddings = bert_encoder(tokenized_examples).numpy()
return embeddings
dset_with_embeddings = dset.map(lambda example: {"embeddings": embed(example["text])})
```
And then reading the embeddings from the arrow format is be very fast.
PS1: Note that right now only 1d arrays are supported.
PS2: It seems possible to do without pandas but it will require more _trickery_.
PS3: I did a simple benchmark with google colab that you can view here:
https://colab.research.google.com/drive/1QlLTR6LRwYOKGJ-hTHmHyolE3wJzvfFg?usp=sharing
## Add Indexed Datasets
For many retrieval tasks it is convenient to index a dataset to be able to run fast queries.
For example for models like DPR, REALM, RAG etc. that are models for Open Domain QA, the retrieval step is very important.
Therefore I added two ways to add an index to a column of a dataset:
1) You can index it using a Dense Index like Faiss. It is used to index vectors.
Faiss is a library for efficient similarity search and clustering of dense vectors.
It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM.
2) You can index it using a Sparse Index like Elasticsearch. It is used to index text and run queries based on BM25 similarity.
Example of usage:
```python
ds = nlp.load_dataset('crime_and_punish', split='train')
ds_with_embeddings = ds.map(lambda example: {'embeddings': embed(example['line']})) # `embed` outputs a `np.array`
ds_with_embeddings.add_vector_index(column='embeddings')
scores, retrieved_examples = ds_with_embeddings.get_nearest(column='embeddings', query=embed('my new query'), k=10)
```
```python
ds = nlp.load_dataset('crime_and_punish', split='train')
es_client = elasticsearch.Elasticsearch()
ds.add_text_index(column='line', es_client=es_client, index_name="my_es_index")
scores, retrieved_examples = ds.get_nearest(column='line', query='my new query', k=10)
```
PS4: Faiss allows to specify many options for the [index](https://github.com/facebookresearch/faiss/wiki/The-index-factory) and for [GPU settings](https://github.com/facebookresearch/faiss/wiki/Faiss-on-the-GPU). I made sure that the user has full control over those settings.
## Tests
I added tests for Faiss, Elasticsearch and indexed datasets.
I had to edit the CI config because all the test scripts were not being run by CircleCI.
------------------
I'd be really happy to have some feedbacks :) | {
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] | closed | false | null | [] | null | 3 | "2020-06-23T00:38:42Z" | "2020-07-17T17:43:06Z" | "2020-07-17T17:43:06Z" | NONE | null | null | null | Selecting `natural_questions` or `newsroom` dataset in the online demo results in an error similar to the following.
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https://api.github.com/repos/huggingface/datasets/issues/296 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/296/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/296/comments | https://api.github.com/repos/huggingface/datasets/issues/296/events | https://github.com/huggingface/datasets/issues/296 | 643,423,717 | MDU6SXNzdWU2NDM0MjM3MTc= | 296 | snli -1 labels | {
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} | [] | closed | false | null | [] | null | 4 | "2020-06-22T23:33:30Z" | "2020-06-23T14:41:59Z" | "2020-06-23T14:41:58Z" | CONTRIBUTOR | null | null | null | I'm trying to train a model on the SNLI dataset. Why does it have so many -1 labels?
```
import nlp
from collections import Counter
data = nlp.load_dataset('snli')['train']
print(Counter(data['label']))
Counter({0: 183416, 2: 183187, 1: 182764, -1: 785})
```
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} | [] | closed | false | null | [] | null | 0 | "2020-06-22T17:28:57Z" | "2020-06-23T14:47:37Z" | "2020-06-23T14:47:37Z" | NONE | null | null | null | Hi,
I am the author of `bert_score`. Recently, we received [ an issue ](https://github.com/Tiiiger/bert_score/issues/62) reporting a problem in using `bert_score` from the `nlp` package (also see #238 in this repo). After looking into this, I realized that the problem arises from the format `nlp.Metric` takes input.
Here is a minimal example:
```python
import nlp
scorer = nlp.load_metric("bertscore")
with open("pred.txt") as p, open("ref.txt") as g:
for lp, lg in zip(p, g):
scorer.add(lp, lg)
score = scorer.compute(lang="en")
```
The problem in the above code is that `scorer.add()` expects a list of strings as input for the references. As a result, the `scorer` here would take a list of characters in `lg` to be the references. The correct implementation would be calling
```python
scorer.add(lp, [lg])
```
I just want to raise this issue to you to prevent future user errors of a similar kind. I assume some simple type checking can prevent this from happening?
Thanks! | {
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https://api.github.com/repos/huggingface/datasets/issues/294 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/294/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/294/comments | https://api.github.com/repos/huggingface/datasets/issues/294/events | https://github.com/huggingface/datasets/issues/294 | 643,181,179 | MDU6SXNzdWU2NDMxODExNzk= | 294 | Cannot load arxiv dataset on MacOS? | {
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] | closed | false | null | [] | null | 4 | "2020-06-22T15:46:55Z" | "2020-06-30T15:25:10Z" | "2020-06-30T15:25:10Z" | CONTRIBUTOR | null | null | null | I am having trouble loading the `"arxiv"` config from the `"scientific_papers"` dataset on MacOS. When I try loading the dataset with:
```python
arxiv = nlp.load_dataset("scientific_papers", "arxiv")
```
I get the following stack trace:
```bash
JSONDecodeError Traceback (most recent call last)
<ipython-input-2-8e00c55d5a59> in <module>
----> 1 arxiv = nlp.load_dataset("scientific_papers", "arxiv")
~/miniconda3/envs/t2t/lib/python3.7/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)
522 download_mode=download_mode,
523 ignore_verifications=ignore_verifications,
--> 524 save_infos=save_infos,
525 )
526
~/miniconda3/envs/t2t/lib/python3.7/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)
430 verify_infos = not save_infos and not ignore_verifications
431 self._download_and_prepare(
--> 432 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
433 )
434 # Sync info
~/miniconda3/envs/t2t/lib/python3.7/site-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
481 try:
482 # Prepare split will record examples associated to the split
--> 483 self._prepare_split(split_generator, **prepare_split_kwargs)
484 except OSError:
485 raise OSError("Cannot find data file. " + (self.manual_download_instructions or ""))
~/miniconda3/envs/t2t/lib/python3.7/site-packages/nlp/builder.py in _prepare_split(self, split_generator)
662
663 generator = self._generate_examples(**split_generator.gen_kwargs)
--> 664 for key, record in utils.tqdm(generator, unit=" examples", total=split_info.num_examples, leave=False):
665 example = self.info.features.encode_example(record)
666 writer.write(example)
~/miniconda3/envs/t2t/lib/python3.7/site-packages/tqdm/std.py in __iter__(self)
1106 fp_write=getattr(self.fp, 'write', sys.stderr.write))
1107
-> 1108 for obj in iterable:
1109 yield obj
1110 # Update and possibly print the progressbar.
~/miniconda3/envs/t2t/lib/python3.7/site-packages/nlp/datasets/scientific_papers/107a416c0e1958cb846f5934b5aae292f7884a5b27e86af3f3ef1a093e058bbc/scientific_papers.py in _generate_examples(self, path)
114 # "section_names": list[str], list of section names.
115 # "sections": list[list[str]], list of sections (list of paragraphs)
--> 116 d = json.loads(line)
117 summary = "\n".join(d["abstract_text"])
118 # In original paper, <S> and </S> are not used in vocab during training
~/miniconda3/envs/t2t/lib/python3.7/json/__init__.py in loads(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
346 parse_int is None and parse_float is None and
347 parse_constant is None and object_pairs_hook is None and not kw):
--> 348 return _default_decoder.decode(s)
349 if cls is None:
350 cls = JSONDecoder
~/miniconda3/envs/t2t/lib/python3.7/json/decoder.py in decode(self, s, _w)
335
336 """
--> 337 obj, end = self.raw_decode(s, idx=_w(s, 0).end())
338 end = _w(s, end).end()
339 if end != len(s):
~/miniconda3/envs/t2t/lib/python3.7/json/decoder.py in raw_decode(self, s, idx)
351 """
352 try:
--> 353 obj, end = self.scan_once(s, idx)
354 except StopIteration as err:
355 raise JSONDecodeError("Expecting value", s, err.value) from None
JSONDecodeError: Unterminated string starting at: line 1 column 46983 (char 46982)
163502 examples [02:10, 2710.68 examples/s]
```
I am not sure how to trace back to the specific JSON file that has the "Unterminated string". Also, I do not get this error on colab so I suspect it may be MacOS specific. Copy pasting the relevant lines from `transformers-cli env` below:
- Platform: Darwin-19.5.0-x86_64-i386-64bit
- Python version: 3.7.5
- PyTorch version (GPU?): 1.5.0 (False)
- Tensorflow version (GPU?): 2.2.0 (False)
Any ideas? | {
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} | This PR disables testing for community datasets on aws.
It should fix the CI that is currently failing. | {
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} | Thank you for featuring the x_stance dataset in your library. This PR updates some metadata:
- Citation: Replace preprint with proceedings
- URL: Use a URL with long-term availability
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I would appreciate if you could help me with this issue. | {
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} | This PR makes the following update to the xsum dataset:
- Manual download is not required anymore
- dataset can be loaded as follow: `nlp.load_dataset('xsum')`
**Important**
Instead of using on outdated url to download the data: "https://raw.githubusercontent.com/EdinburghNLP/XSum/master/XSum-Dataset/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json"
a more up-to-date url stored here: https://s3.amazonaws.com/datasets.huggingface.co/summarization/xsum.tar.gz is used
, so that the user does not need to manually download the data anymore.
There might be slight breaking changes here for xsum. | {
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_np_qint8 = np.dtype([("qint8", np.int8, 1)])
/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:470: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:471: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint16 = np.dtype([("qint16", np.int16, 1)])
/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:472: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:473: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint32 = np.dtype([("qint32", np.int32, 1)])
/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:476: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
np_resource = np.dtype([("resource", np.ubyte, 1)])
/Users/parasol_tree/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6
return f(*args, **kwds)
/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
from ._conv import register_converters as _register_converters
Traceback (most recent call last):
File "/Users/parasol_tree/Resource/019 - Github/AcademicEnglishToolkit /test.py", line 7, in <module>
import nlp
File "/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/nlp/__init__.py", line 27, in <module>
from .arrow_dataset import Dataset
File "/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/nlp/arrow_dataset.py", line 31, in <module>
from nlp.utils.py_utils import dumps
File "/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/nlp/utils/__init__.py", line 20, in <module>
from .download_manager import DownloadManager, GenerateMode
File "/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/nlp/utils/download_manager.py", line 25, in <module>
from .py_utils import flatten_nested, map_nested, size_str
File "/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/nlp/utils/py_utils.py", line 244, in <module>
class Pickler(dill.Pickler):
File "/Users/parasol_tree/anaconda3/lib/python3.6/site-packages/nlp/utils/py_utils.py", line 247, in Pickler
dispatch = dill._dill.MetaCatchingDict(dill.Pickler.dispatch.copy())
AttributeError: module 'dill' has no attribute '_dill' | {
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} | Fix #280
The imports were wrong | {
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} | I completed all the steps in https://github.com/huggingface/nlp/blob/master/CONTRIBUTING.md#how-to-add-a-dataset and push the code for ANLI. Please let me know if there are any errors. | {
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} | This PR replaces the static `DatasetBulider` variable `MANUAL_DOWNLOAD_INSTRUCTIONS` by a property function `manual_download_instructions()`.
Some datasets like XTREME and all WMT need the manual data dir only for a small fraction of the possible configs.
After some brainstorming with @mariamabarham and @lhoestq, we came to the conclusion that having a property function `manual_download_instructions()` gives us more flexibility to decide on a per config basis in the dataset builder if manual download instructions are needed.
Also this PR should unblock solves a bug with `wmt16 - ro-en`
@sshleifer from this branch you should be able to succesfully run
```python
import nlp
ds = nlp.load_dataset('./datasets/wmt16', 'ro-en')
```
and once this PR is merged S3 should be synched so that
```python
import nlp
ds = nlp.load_dataset("wmt16", "ro-en")
```
works as well.
**Important**: Since `MANUAL_DOWNLOAD_INSTRUCTIONS` was not really exposed to the user, this PR should not be a problem regarding backward compatibility. | {
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] | null | 0 | "2020-06-18T14:48:45Z" | "2020-06-22T17:30:46Z" | "2020-06-22T17:30:46Z" | CONTRIBUTOR | null | null | null | The citations are all of a different format, some have "```" and have text inside, others are proper bibtex.
Can we make it so that they all are proper citations, i.e. parse by the bibtex spec:
https://bibtexparser.readthedocs.io/en/master/ | {
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} | Some datasets are hosted on gcs (wikipedia for example). In this PR I make sure that, when a user loads such datasets, the file_instructions are built using the dataset_info.json from gcs and not from the info extracted from the local `dataset_infos.json` (the one that contain the info for each config). Indeed local files may end up outdated.
Furthermore, to avoid outdated dataset_infos.json, I now make sure that each time you run `load_dataset` it also tries to update the file locally.
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Thanks for this fantastic library, it makes it very easy to do prototyping for NLP projects interchangeably between TF/Pytorch.
Unfortunately, there is data that cannot easily be shared publicly as it may contain sensitive information.
Is there support/a plan to support such data with NLP, e.g. by reading it from local sources?
Use case flow could look like this: use NLP to prototype an approach on similar, public data and apply the resulting prototype on sensitive/private data without the need to rethink data processing pipelines.
Many thanks for your responses ahead of time and kind regards,
MFreidank | {
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} | [] | closed | false | null | [] | null | 0 | "2020-06-17T19:10:54Z" | "2020-06-19T08:33:41Z" | "2020-06-19T08:33:41Z" | NONE | null | null | null | I can't seem to import squad v2 metrics.
**squad_metric = nlp.load_metric('squad_v2')**
**This throws me an error.:**
```
ImportError Traceback (most recent call last)
<ipython-input-8-170b6a170555> in <module>
----> 1 squad_metric = nlp.load_metric('squad_v2')
~/env/lib64/python3.6/site-packages/nlp/load.py in load_metric(path, name, process_id, num_process, data_dir, experiment_id, in_memory, download_config, **metric_init_kwargs)
426 """
427 module_path = prepare_module(path, download_config=download_config, dataset=False)
--> 428 metric_cls = import_main_class(module_path, dataset=False)
429 metric = metric_cls(
430 name=name,
~/env/lib64/python3.6/site-packages/nlp/load.py in import_main_class(module_path, dataset)
55 """
56 importlib.invalidate_caches()
---> 57 module = importlib.import_module(module_path)
58
59 if dataset:
/usr/lib64/python3.6/importlib/__init__.py in import_module(name, package)
124 break
125 level += 1
--> 126 return _bootstrap._gcd_import(name[level:], package, level)
127
128
/usr/lib64/python3.6/importlib/_bootstrap.py in _gcd_import(name, package, level)
/usr/lib64/python3.6/importlib/_bootstrap.py in _find_and_load(name, import_)
/usr/lib64/python3.6/importlib/_bootstrap.py in _find_and_load_unlocked(name, import_)
/usr/lib64/python3.6/importlib/_bootstrap.py in _load_unlocked(spec)
/usr/lib64/python3.6/importlib/_bootstrap_external.py in exec_module(self, module)
/usr/lib64/python3.6/importlib/_bootstrap.py in _call_with_frames_removed(f, *args, **kwds)
~/env/lib64/python3.6/site-packages/nlp/metrics/squad_v2/a15e787c76889174874386d3def75321f0284c11730d2a57e28fe1352c9b5c7a/squad_v2.py in <module>
16
17 import nlp
---> 18 from .evaluate import evaluate
19
20 _CITATION = """\
ImportError: cannot import name 'evaluate'
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/279 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/279/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/279/comments | https://api.github.com/repos/huggingface/datasets/issues/279/events | https://github.com/huggingface/datasets/issues/279 | 640,611,692 | MDU6SXNzdWU2NDA2MTE2OTI= | 279 | Dataset Preprocessing Cache with .map() function not working as expected | {
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} | [] | closed | false | null | [] | null | 5 | "2020-06-17T17:17:21Z" | "2021-07-06T21:43:28Z" | "2021-04-18T23:43:49Z" | NONE | null | null | null | I've been having issues with reproducibility when loading and processing datasets with the `.map` function. I was only able to resolve them by clearing all of the cache files on my system.
Is there a way to disable using the cache when processing a dataset? As I make minor processing changes on the same dataset, I want to be able to be certain the data is being re-processed rather than loaded from a cached file.
Could you also help me understand a bit more about how the caching functionality is used for pre-processing? E.g. how is it determined when to load from a cache vs. reprocess.
I was particularly having an issue where the correct dataset splits were loaded, but as soon as I applied the `.map()` function to each split independently, they somehow all exited this process having been converted to the test set.
Thanks! | {
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https://api.github.com/repos/huggingface/datasets/issues/278 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/278/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/278/comments | https://api.github.com/repos/huggingface/datasets/issues/278/events | https://github.com/huggingface/datasets/issues/278 | 640,518,917 | MDU6SXNzdWU2NDA1MTg5MTc= | 278 | MemoryError when loading German Wikipedia | {
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} | [] | closed | false | null | [] | null | 7 | "2020-06-17T15:06:21Z" | "2020-06-19T12:53:02Z" | "2020-06-19T12:53:02Z" | NONE | null | null | null | Hi, first off let me say thank you for all the awesome work you're doing at Hugging Face across all your projects (NLP, Transformers, Tokenizers) - they're all amazing contributions to us working with NLP models :)
I'm trying to download the German Wikipedia dataset as follows:
```
wiki = nlp.load_dataset("wikipedia", "20200501.de", split="train")
```
However, when I do so, I get the following error:
```
Downloading and preparing dataset wikipedia/20200501.de (download: Unknown size, generated: Unknown size, total: Unknown size) to /home/ubuntu/.cache/huggingface/datasets/wikipedia/20200501.de/1.0.0...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/ubuntu/anaconda3/envs/albert/lib/python3.7/site-packages/nlp/load.py", line 520, in load_dataset
save_infos=save_infos,
File "/home/ubuntu/anaconda3/envs/albert/lib/python3.7/site-packages/nlp/builder.py", line 433, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/ubuntu/anaconda3/envs/albert/lib/python3.7/site-packages/nlp/builder.py", line 824, in _download_and_prepare
"\n\t`{}`".format(usage_example)
nlp.builder.MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/
If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory).
Example of usage:
`load_dataset('wikipedia', '20200501.de', beam_runner='DirectRunner')`
```
So, following on from the example usage at the bottom, I tried specifying `beam_runner='DirectRunner`, however when I do this after about 20 min after the data has all downloaded, I get a `MemoryError` as warned.
This isn't an issue for the English or French Wikipedia datasets (I've tried both), as neither seem to require that `beam_runner` be specified. Can you please clarify why this is an issue for the German dataset?
My nlp version is 0.2.1.
Thank you! | {
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https://api.github.com/repos/huggingface/datasets/issues/277 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/277/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/277/comments | https://api.github.com/repos/huggingface/datasets/issues/277/events | https://github.com/huggingface/datasets/issues/277 | 640,163,053 | MDU6SXNzdWU2NDAxNjMwNTM= | 277 | Empty samples in glue/qqp | {
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} | [] | closed | false | null | [] | null | 2 | "2020-06-17T05:54:52Z" | "2020-06-21T00:21:45Z" | "2020-06-21T00:21:45Z" | CONTRIBUTOR | null | null | null | ```
qqp = nlp.load_dataset('glue', 'qqp')
print(qqp['train'][310121])
print(qqp['train'][362225])
```
```
{'question1': 'How can I create an Android app?', 'question2': '', 'label': 0, 'idx': 310137}
{'question1': 'How can I develop android app?', 'question2': '', 'label': 0, 'idx': 362246}
```
Notice that question 2 is empty string.
BTW, I have checked and these two are the only naughty ones in all splits of qqp. | {
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https://api.github.com/repos/huggingface/datasets/issues/276 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/276/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/276/comments | https://api.github.com/repos/huggingface/datasets/issues/276/events | https://github.com/huggingface/datasets/pull/276 | 639,490,858 | MDExOlB1bGxSZXF1ZXN0NDM1MDY5Nzg5 | 276 | Fix metric compute (original_instructions missing) | {
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} | [] | closed | false | null | [] | null | 2 | "2020-06-16T08:52:01Z" | "2020-06-18T07:41:45Z" | "2020-06-18T07:41:44Z" | MEMBER | null | 0 | {
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} | When loading arrow data we added in cc8d250 a way to specify the instructions that were used to store them with the loaded dataset.
However metrics load data the same way but don't need instructions (we use one single file).
In this PR I just make `original_instructions` optional when reading files to load a `Dataset` object.
This should fix #269 | {
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https://api.github.com/repos/huggingface/datasets/issues/275 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/275/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/275/comments | https://api.github.com/repos/huggingface/datasets/issues/275/events | https://github.com/huggingface/datasets/issues/275 | 639,439,052 | MDU6SXNzdWU2Mzk0MzkwNTI= | 275 | NonMatchingChecksumError when loading pubmed dataset | {
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] | closed | false | null | [] | null | 1 | "2020-06-16T07:31:51Z" | "2020-06-19T07:37:07Z" | "2020-06-19T07:37:07Z" | NONE | null | null | null | I get this error when i run `nlp.load_dataset('scientific_papers', 'pubmed', split = 'train[:50%]')`.
The error is:
```
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
<ipython-input-2-7742dea167d0> in <module>()
----> 1 df = nlp.load_dataset('scientific_papers', 'pubmed', split = 'train[:50%]')
2 df = pd.DataFrame(df)
3 gc.collect()
3 frames
/usr/local/lib/python3.6/dist-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
/usr/local/lib/python3.6/dist-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)
431 verify_infos = not save_infos and not ignore_verifications
432 self._download_and_prepare(
--> 433 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
434 )
435 # Sync info
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
468 # Checksums verification
469 if verify_infos:
--> 470 verify_checksums(self.info.download_checksums, dl_manager.get_recorded_sizes_checksums())
471 for split_generator in split_generators:
472 if str(split_generator.split_info.name).lower() == "all":
/usr/local/lib/python3.6/dist-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://drive.google.com/uc?id=1b3rmCSIoh6VhD4HKWjI4HOW-cSwcwbeC&export=download', 'https://drive.google.com/uc?id=1lvsqvsFi3W-pE1SqNZI0s8NR9rC1tsja&export=download']
```
I'm currently working on google colab.
That is quite strange because yesterday it was fine.
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] | closed | false | null | [] | null | 4 | "2020-06-15T21:02:26Z" | "2020-07-06T15:35:02Z" | "2020-07-06T15:35:02Z" | CONTRIBUTOR | null | null | null | Hi, and thanks for all your open-sourced work, as always!
I was wondering if you would be open to adding PG-19 to your collection of datasets. https://github.com/deepmind/pg19 It is often used for benchmarking long-range language modeling. | {
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https://api.github.com/repos/huggingface/datasets/issues/273 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/273/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/273/comments | https://api.github.com/repos/huggingface/datasets/issues/273/events | https://github.com/huggingface/datasets/pull/273 | 638,968,054 | MDExOlB1bGxSZXF1ZXN0NDM0NjM0MzU4 | 273 | update cos_e to add cos_e v1.0 | {
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} | This PR updates the cos_e dataset to add v1.0 as requested here #163
@nazneenrajani | {
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https://api.github.com/repos/huggingface/datasets/issues/271 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/271/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/271/comments | https://api.github.com/repos/huggingface/datasets/issues/271/events | https://github.com/huggingface/datasets/pull/271 | 638,135,754 | MDExOlB1bGxSZXF1ZXN0NDMzOTg3NDkw | 271 | Fix allociné dataset configuration | {
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} | This is a patch for #244. According to the [live nlp viewer](url), the Allociné dataset must be loaded with :
```python
dataset = load_dataset('allocine', 'allocine')
```
This is redundant, as there is only one "dataset configuration", and should only be:
```python
dataset = load_dataset('allocine')
```
This is my mistake, because the code for [`allocine.py`](https://github.com/huggingface/nlp/blob/master/datasets/allocine/allocine.py) was inspired by [`imdb.py`](https://github.com/huggingface/nlp/blob/master/datasets/imdb/imdb.py), which also force the user to specify the "dataset configuration" (even if there is only one).
I believe this PR should solve this issue, making the Allociné dataset more convenient to use. | {
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```python
ModuleNotFoundError: No module named 'langdetect'
Traceback:
File "/home/sasha/.local/lib/python3.7/site-packages/streamlit/ScriptRunner.py", line 322, in _run_script
exec(code, module.__dict__)
File "/home/sasha/nlp_viewer/run.py", line 54, in <module>
configs = get_confs(option.id)
File "/home/sasha/.local/lib/python3.7/site-packages/streamlit/caching.py", line 591, in wrapped_func
return get_or_create_cached_value()
File "/home/sasha/.local/lib/python3.7/site-packages/streamlit/caching.py", line 575, in get_or_create_cached_value
return_value = func(*args, **kwargs)
File "/home/sasha/nlp_viewer/run.py", line 48, in get_confs
builder_cls = nlp.load.import_main_class(module_path, dataset=True)
File "/home/sasha/.local/lib/python3.7/site-packages/nlp/load.py", line 57, in import_main_class
module = importlib.import_module(module_path)
File "/usr/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1006, in _gcd_import
File "<frozen importlib._bootstrap>", line 983, in _find_and_load
File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 728, in exec_module
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "/home/sasha/.local/lib/python3.7/site-packages/nlp/datasets/c4/88bb1b1435edad3fb772325710c4a43327cbf4a23b9030094556e6f01e14ec19/c4.py", line 29, in <module>
from .c4_utils import (
File "/home/sasha/.local/lib/python3.7/site-packages/nlp/datasets/c4/88bb1b1435edad3fb772325710c4a43327cbf4a23b9030094556e6f01e14ec19/c4_utils.py", line 29, in <module>
import langdetect
``` | {
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```python
import nlp
rte_metric = nlp.load_metric('glue', name="rte")
rte_metric.compute(
[0, 0, 1, 1],
[0, 1, 0, 1],
)
```
```
181 # Read the predictions and references
182 reader = ArrowReader(path=self.data_dir, info=None)
--> 183 self.data = reader.read_files(node_files)
184
185 # Release all of our locks
TypeError: read_files() missing 1 required positional argument: 'original_instructions'
```
I believe this might have been introduced with cc8d2508b75f7ba0e5438d0686ee02dcec43c7f4, which added the `original_instructions` argument. Elsewhere, an empty-string default is provided--perhaps that could be done here too? | {
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] | null | 1 | "2020-06-12T01:09:37Z" | "2020-06-19T08:24:19Z" | "2020-06-19T08:24:19Z" | CONTRIBUTOR | null | null | null | I believe it is from `wmt16`
When I run
```python
wmt = nlp.load_dataset('wmt16')
```
I get:
```python
AssertionError: The dataset wmt16 with config cs-en requires manual data.
Please follow the manual download instructions: Some of the wmt configs here, require a manual download.
Please look into wmt.py to see the exact path (and file name) that has to
be downloaded.
.
Manual data can be loaded with `nlp.load(wmt16, data_dir='<path/to/manual/data>')
```
There is no wmt.py,as the error message suggests, and wmt16.py doesn't have manual download instructions.
Any idea how to do this?
Thanks in advance!
| {
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https://api.github.com/repos/huggingface/datasets/issues/266 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/266/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/266/comments | https://api.github.com/repos/huggingface/datasets/issues/266/events | https://github.com/huggingface/datasets/pull/266 | 637,156,392 | MDExOlB1bGxSZXF1ZXN0NDMzMTk1NDgw | 266 | Add sort, shuffle, test_train_split and select methods | {
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} | Add a bunch of methods to reorder/split/select rows in a dataset:
- `dataset.select(indices)`: Create a new dataset with rows selected following the list/array of indices (which can have a different size than the dataset and contain duplicated indices, the only constrain is that all the integers in the list must be smaller than the dataset size, otherwise we're indexing outside the dataset...)
- `dataset.sort(column_name)`: sort a dataset according to a column (has to be a column with a numpy compatible type)
- `dataset.shuffle(seed)`: shuffle a dataset rows
- `dataset.train_test_split(test_size, train_size)`: Return a dictionary with two random train and test subsets (`train` and `test` ``Dataset`` splits)
All these methods are **not** in-place which means they return new ``Dataset``.
This is the default behavior in the library.
Fix #147 #166 #259 | {
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} | When a user installs `nlp` on google colab, then google colab doesn't update pyarrow, and the runtime needs to be restarted to use the updated version of pyarrow.
This is an issue because `nlp` requires the updated version to work correctly.
In this PR I added en error that is shown to the user in google colab if the user tries to `import nlp` without having restarted the runtime. The error tells the user to restart the runtime. | {
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https://api.github.com/repos/huggingface/datasets/issues/264 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/264/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/264/comments | https://api.github.com/repos/huggingface/datasets/issues/264/events | https://github.com/huggingface/datasets/pull/264 | 637,106,170 | MDExOlB1bGxSZXF1ZXN0NDMzMTU0ODQ4 | 264 | Fix small issues creating dataset | {
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} | Fix many small issues mentioned in #249:
- don't force to install apache beam for commands
- fix None cache dir when using `dl_manager.download_custom`
- added new extras in `setup.py` named `dev` that contains tests and quality dependencies
- mock dataset sizes when running tests with dummy data
- add a note about the naming convention of datasets (camel case - snake case) in CONTRIBUTING.md
This should help users create their datasets.
Next step is the `add_dataset.md` docs :) | {
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https://api.github.com/repos/huggingface/datasets/issues/263 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/263/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/263/comments | https://api.github.com/repos/huggingface/datasets/issues/263/events | https://github.com/huggingface/datasets/issues/263 | 637,028,015 | MDU6SXNzdWU2MzcwMjgwMTU= | 263 | [Feature request] Support for external modality for language datasets | {
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] | closed | false | null | [] | null | 5 | "2020-06-11T13:42:18Z" | "2022-02-10T13:26:35Z" | "2022-02-10T13:26:35Z" | CONTRIBUTOR | null | null | null | # Background
In recent years many researchers have advocated that learning meanings from text-based only datasets is just like asking a human to "learn to speak by listening to the radio" [[E. Bender and A. Koller,2020](https://openreview.net/forum?id=GKTvAcb12b), [Y. Bisk et. al, 2020](https://arxiv.org/abs/2004.10151)]. Therefore, the importance of multi-modal datasets for the NLP community is of paramount importance for next-generation models. For this reason, I raised a [concern](https://github.com/huggingface/nlp/pull/236#issuecomment-639832029) related to the best way to integrate external features in NLP datasets (e.g., visual features associated with an image, audio features associated with a recording, etc.). This would be of great importance for a more systematic way of representing data for ML models that are learning from multi-modal data.
# Language + Vision
## Use case
Typically, people working on Language+Vision tasks, have a reference dataset (either in JSON or JSONL format) and for each example, they have an identifier that specifies the reference image. For a practical example, you can refer to the [GQA](https://cs.stanford.edu/people/dorarad/gqa/download.html#seconddown) dataset.
Currently, images are represented by either pooling-based features (average pooling of ResNet or VGGNet features, see [DeVries et.al, 2017](https://arxiv.org/abs/1611.08481), [Shekhar et.al, 2019](https://www.aclweb.org/anthology/N19-1265.pdf)) where you have a single vector for every image. Another option is to use a set of feature maps for every image extracted from a specific layer of a CNN (see [Xu et.al, 2015](https://arxiv.org/abs/1502.03044)). A more recent option, especially with large-scale multi-modal transformers [Li et. al, 2019](https://arxiv.org/abs/1908.03557), is to use FastRCNN features.
For all these types of features, people use one of the following formats:
1. [HD5F](https://pypi.org/project/h5py/)
2. [NumPy](https://numpy.org/doc/stable/reference/generated/numpy.savez.html)
3. [LMDB](https://lmdb.readthedocs.io/en/release/)
## Implementation considerations
I was thinking about possible ways of implementing this feature. As mentioned above, depending on the model, different visual features can be used. This step usually relies on another model (say ResNet-101) that is used to generate the visual features for each image used in the dataset. Typically, this step is done in a separate script that completes the feature generation procedure. The usual processing steps for these datasets are the following:
1. Download dataset
2. Download images associated with the dataset
3. Write a script that generates the visual features for every image and store them in a specific file
4. Create a DataLoader that maps the visual features to the corresponding language example
In my personal projects, I've decided to ignore HD5F because it doesn't have out-of-the-box support for multi-processing (see this PyTorch [issue](https://github.com/pytorch/pytorch/issues/11929)). I've been successfully using a NumPy compressed file for each image so that I can store any sort of information in it.
For ease of use of all these Language+Vision datasets, it would be really handy to have a way to associate the visual features with the text and store them in an efficient way. That's why I immediately thought about the HuggingFace NLP backend based on Apache Arrow. The assumption here is that the external modality will be mapped to a N-dimensional tensor so easily represented by a NumPy array.
Looking forward to hearing your thoughts about it! | {
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https://api.github.com/repos/huggingface/datasets/issues/262 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/262/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/262/comments | https://api.github.com/repos/huggingface/datasets/issues/262/events | https://github.com/huggingface/datasets/pull/262 | 636,702,849 | MDExOlB1bGxSZXF1ZXN0NDMyODI3Mzcz | 262 | Add new dataset ANLI Round 1 | {
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} | [] | closed | false | null | [] | null | 1 | "2020-06-11T04:14:57Z" | "2020-06-12T22:03:03Z" | "2020-06-12T22:03:03Z" | CONTRIBUTOR | null | 0 | {
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} | Adding new dataset [ANLI](https://github.com/facebookresearch/anli/).
I'm not familiar with how to add new dataset. Let me know if there is any issue. I only include round 1 data here. There will be round 2, round 3 and more in the future with potentially different format. I think it will be better to separate them. | {
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https://api.github.com/repos/huggingface/datasets/issues/261 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/261/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/261/comments | https://api.github.com/repos/huggingface/datasets/issues/261/events | https://github.com/huggingface/datasets/issues/261 | 636,372,380 | MDU6SXNzdWU2MzYzNzIzODA= | 261 | Downloading dataset error with pyarrow.lib.RecordBatch | {
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} | [] | closed | false | null | [] | null | 2 | "2020-06-10T16:04:19Z" | "2020-06-11T14:35:12Z" | "2020-06-11T14:35:12Z" | NONE | null | null | null | I am trying to download `sentiment140` and I have the following error
```
/usr/local/lib/python3.6/dist-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
/usr/local/lib/python3.6/dist-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
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
472 try:
473 # Prepare split will record examples associated to the split
--> 474 self._prepare_split(split_generator, **prepare_split_kwargs)
475 except OSError:
476 raise OSError("Cannot find data file. " + (self.MANUAL_DOWNLOAD_INSTRUCTIONS or ""))
/usr/local/lib/python3.6/dist-packages/nlp/builder.py in _prepare_split(self, split_generator)
652 for key, record in utils.tqdm(generator, unit=" examples", total=split_info.num_examples, leave=False):
653 example = self.info.features.encode_example(record)
--> 654 writer.write(example)
655 num_examples, num_bytes = writer.finalize()
656
/usr/local/lib/python3.6/dist-packages/nlp/arrow_writer.py in write(self, example, writer_batch_size)
143 self._build_writer(pa_table=pa.Table.from_pydict(example))
144 if writer_batch_size is not None and len(self.current_rows) >= writer_batch_size:
--> 145 self.write_on_file()
146
147 def write_batch(
/usr/local/lib/python3.6/dist-packages/nlp/arrow_writer.py in write_on_file(self)
127 else:
128 # All good
--> 129 self._write_array_on_file(pa_array)
130 self.current_rows = []
131
/usr/local/lib/python3.6/dist-packages/nlp/arrow_writer.py in _write_array_on_file(self, pa_array)
96 def _write_array_on_file(self, pa_array):
97 """Write a PyArrow Array"""
---> 98 pa_batch = pa.RecordBatch.from_struct_array(pa_array)
99 self._num_bytes += pa_array.nbytes
100 self.pa_writer.write_batch(pa_batch)
AttributeError: type object 'pyarrow.lib.RecordBatch' has no attribute 'from_struct_array'
```
I installed the last version and ran the following command:
```python
import nlp
sentiment140 = nlp.load_dataset('sentiment140', cache_dir='/content')
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/260 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/260/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/260/comments | https://api.github.com/repos/huggingface/datasets/issues/260/events | https://github.com/huggingface/datasets/pull/260 | 636,261,118 | MDExOlB1bGxSZXF1ZXN0NDMyNDY3NDM5 | 260 | Consistency fixes | {
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} | [] | closed | false | null | [] | null | 0 | "2020-06-10T13:44:42Z" | "2020-06-11T10:34:37Z" | "2020-06-11T10:34:36Z" | MEMBER | null | 0 | {
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} | A few bugs I've found while hacking | {
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https://api.github.com/repos/huggingface/datasets/issues/259 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/259/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/259/comments | https://api.github.com/repos/huggingface/datasets/issues/259/events | https://github.com/huggingface/datasets/issues/259 | 636,239,529 | MDU6SXNzdWU2MzYyMzk1Mjk= | 259 | documentation missing how to split a dataset | {
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} | [] | closed | false | null | [] | null | 7 | "2020-06-10T13:18:13Z" | "2023-03-14T13:56:07Z" | "2020-06-18T22:20:24Z" | NONE | null | null | null | I am trying to understand how to split a dataset ( as arrow_dataset).
I know I can do something like this to access a split which is already in the original dataset :
`ds_test = nlp.load_dataset('imdb, split='test') `
But how can I split ds_test into a test and a validation set (without reading the data into memory and keeping the arrow_dataset as container)?
I guess it has something to do with the module split :-) but there is no real documentation in the code but only a reference to a longer description:
> See the [guide on splits](https://github.com/huggingface/nlp/tree/master/docs/splits.md) for more information.
But the guide seems to be missing.
To clarify: I know that this has been modelled after the dataset of tensorflow and that some of the documentation there can be used [like this one](https://www.tensorflow.org/datasets/splits). But to come back to the example above: I cannot simply split the testset doing this:
`ds_test = nlp.load_dataset('imdb, split='test'[:5000]) `
`ds_val = nlp.load_dataset('imdb, split='test'[5000:])`
because the imdb test data is sorted by class (probably not a good idea anyway)
| {
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https://api.github.com/repos/huggingface/datasets/issues/258 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/258/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/258/comments | https://api.github.com/repos/huggingface/datasets/issues/258/events | https://github.com/huggingface/datasets/issues/258 | 635,859,525 | MDU6SXNzdWU2MzU4NTk1MjU= | 258 | Why is dataset after tokenization far more larger than the orginal one ? | {
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} | [] | closed | false | null | [] | null | 4 | "2020-06-10T01:27:07Z" | "2020-06-10T12:46:34Z" | "2020-06-10T12:46:34Z" | CONTRIBUTOR | null | null | null | I tokenize wiki dataset by `map` and cache the results.
```
def tokenize_tfm(example):
example['input_ids'] = hf_fast_tokenizer.convert_tokens_to_ids(hf_fast_tokenizer.tokenize(example['text']))
return example
wiki = nlp.load_dataset('wikipedia', '20200501.en', cache_dir=cache_dir)['train']
wiki.map(tokenize_tfm, cache_file_name=cache_dir/"wikipedia/20200501.en/1.0.0/tokenized_wiki.arrow")
```
and when I see their size
```
ls -l --block-size=M
17460M wikipedia-train.arrow
47511M tokenized_wiki.arrow
```
The tokenized one is over 2x size of original one.
Is there something I did wrong ? | {
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https://api.github.com/repos/huggingface/datasets/issues/257 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/257/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/257/comments | https://api.github.com/repos/huggingface/datasets/issues/257/events | https://github.com/huggingface/datasets/issues/257 | 635,620,979 | MDU6SXNzdWU2MzU2MjA5Nzk= | 257 | Tokenizer pickling issue fix not landed in `nlp` yet? | {
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} | [] | closed | false | null | [] | null | 2 | "2020-06-09T17:12:34Z" | "2020-06-10T21:45:32Z" | "2020-06-09T17:26:53Z" | NONE | null | null | null | Unless I recreate an arrow_dataset from my loaded nlp dataset myself (which I think does not use the cache by default), I get the following error when applying the map function:
```
dataset = nlp.load_dataset('cos_e')
tokenizer = GPT2TokenizerFast.from_pretrained('gpt2', cache_dir=cache_dir)
for split in dataset.keys():
dataset[split].map(lambda x: some_function(x, tokenizer))
```
```
06/09/2020 10:09:19 - INFO - nlp.builder - Constructing Dataset for split train[:10], from /home/sarahw/.cache/huggingface/datasets/cos_e/default/0.0.1
Traceback (most recent call last):
File "generation/input_to_label_and_rationale.py", line 390, in <module>
main()
File "generation/input_to_label_and_rationale.py", line 263, in main
dataset[split] = dataset[split].map(lambda x: input_to_explanation_plus_label(x, tokenizer, max_length, datasource=data_args.task_name, wt5=(model_class=='t5'), expl_only=model_args.rationale_only), batched=False)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/site-packages/nlp/arrow_dataset.py", line 522, in map
cache_file_name = self._get_cache_file_path(function, cache_kwargs)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/site-packages/nlp/arrow_dataset.py", line 381, in _get_cache_file_path
function_bytes = dumps(function)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/site-packages/nlp/utils/py_utils.py", line 257, in dumps
dump(obj, file)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/site-packages/nlp/utils/py_utils.py", line 250, in dump
Pickler(file).dump(obj)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/site-packages/dill/_dill.py", line 445, in dump
StockPickler.dump(self, obj)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 485, in dump
self.save(obj)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 558, in save
f(self, obj) # Call unbound method with explicit self
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/site-packages/dill/_dill.py", line 1410, in save_function
pickler.save_reduce(_create_function, (obj.__code__,
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 690, in save_reduce
save(args)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 558, in save
f(self, obj) # Call unbound method with explicit self
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 899, in save_tuple
save(element)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 558, in save
f(self, obj) # Call unbound method with explicit self
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 899, in save_tuple
save(element)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 558, in save
f(self, obj) # Call unbound method with explicit self
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/site-packages/dill/_dill.py", line 1147, in save_cell
pickler.save_reduce(_create_cell, (f,), obj=obj)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 690, in save_reduce
save(args)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 558, in save
f(self, obj) # Call unbound method with explicit self
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 884, in save_tuple
save(element)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 601, in save
self.save_reduce(obj=obj, *rv)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 715, in save_reduce
save(state)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 558, in save
f(self, obj) # Call unbound method with explicit self
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/site-packages/dill/_dill.py", line 912, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 969, in save_dict
self._batch_setitems(obj.items())
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 995, in _batch_setitems
save(v)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 601, in save
self.save_reduce(obj=obj, *rv)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 715, in save_reduce
save(state)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 558, in save
f(self, obj) # Call unbound method with explicit self
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/site-packages/dill/_dill.py", line 912, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 969, in save_dict
self._batch_setitems(obj.items())
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 995, in _batch_setitems
save(v)
File "/home/sarahw/miniconda3/envs/project_huggingface/lib/python3.8/pickle.py", line 576, in save
rv = reduce(self.proto)
TypeError: cannot pickle 'Tokenizer' object
```
Fix seems to be in the tokenizers [`0.8.0.dev1 pre-release`](https://github.com/huggingface/tokenizers/issues/87), which I can't install with any package managers. | {
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} | [] | closed | false | null | [] | null | 1 | "2020-06-09T10:16:01Z" | "2020-06-12T08:31:27Z" | "2020-06-12T08:31:27Z" | CONTRIBUTOR | null | 0 | {
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} | [] | closed | false | null | [] | null | 1 | "2020-06-09T02:22:13Z" | "2020-06-09T08:41:38Z" | "2020-06-09T08:41:38Z" | NONE | null | null | null | As mentioned in #117, it's currently not possible to remove a sample of the dataset.
But it is a important use case : After applying some preprocessing, some samples might be empty for example. We should be able to remove these samples from the dataset, or at least mark them as `removed` so when iterating the dataset, we don't iterate these samples.
I think it should be a feature. What do you think ?
---
Any work-around in the meantime ? | {
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https://api.github.com/repos/huggingface/datasets/issues/253 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/253/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/253/comments | https://api.github.com/repos/huggingface/datasets/issues/253/events | https://github.com/huggingface/datasets/pull/253 | 634,791,939 | MDExOlB1bGxSZXF1ZXN0NDMxMjgwOTYz | 253 | add flue dataset | {
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} | [] | closed | false | null | [] | null | 10 | "2020-06-08T17:11:09Z" | "2023-09-24T09:46:03Z" | "2020-07-16T07:50:59Z" | CONTRIBUTOR | null | 0 | {
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} | This PR add the Flue dataset as requested in this issue #223 . @lbourdois made a detailed description in that issue.
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https://api.github.com/repos/huggingface/datasets/issues/252 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/252/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/252/comments | https://api.github.com/repos/huggingface/datasets/issues/252/events | https://github.com/huggingface/datasets/issues/252 | 634,563,239 | MDU6SXNzdWU2MzQ1NjMyMzk= | 252 | NonMatchingSplitsSizesError error when reading the IMDB dataset | {
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} | [] | closed | false | null | [] | null | 4 | "2020-06-08T12:26:24Z" | "2021-08-27T15:20:58Z" | "2020-06-08T14:01:26Z" | NONE | null | null | null | Hi!
I am trying to load the `imdb` dataset with this line:
`dataset = nlp.load_dataset('imdb', data_dir='/A/PATH', cache_dir='/A/PATH')`
but I am getting the following error:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/mounts/Users/cisintern/antmarakis/anaconda3/lib/python3.7/site-packages/nlp/load.py", line 517, in load_dataset
save_infos=save_infos,
File "/mounts/Users/cisintern/antmarakis/anaconda3/lib/python3.7/site-packages/nlp/builder.py", line 363, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/mounts/Users/cisintern/antmarakis/anaconda3/lib/python3.7/site-packages/nlp/builder.py", line 421, in _download_and_prepare
verify_splits(self.info.splits, split_dict)
File "/mounts/Users/cisintern/antmarakis/anaconda3/lib/python3.7/site-packages/nlp/utils/info_utils.py", line 70, in verify_splits
raise NonMatchingSplitsSizesError(str(bad_splits))
nlp.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=33442202, num_examples=25000, dataset_name='imdb'), 'recorded': SplitInfo(name='train', num_bytes=5929447, num_examples=4537, dataset_name='imdb')}, {'expected': SplitInfo(name='unsupervised', num_bytes=67125548, num_examples=50000, dataset_name='imdb'), 'recorded': SplitInfo(name='unsupervised', num_bytes=0, num_examples=0, dataset_name='imdb')}]
```
Am I overlooking something? Thanks! | {
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} | [] | closed | false | null | [] | null | 0 | "2020-06-08T11:56:50Z" | "2020-06-12T08:13:00Z" | "2020-06-12T08:12:58Z" | MEMBER | null | 0 | {
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} | Moves all the dataset info down one level from `dataset.info.XXX` to `dataset.XXX`
This way it's easier to access `dataset.feature['label']` for instance
Also, add the original split instructions used to create the dataset in `dataset.split`
Ex:
```
from nlp import load_dataset
stsb = load_dataset('glue', name='stsb', split='train')
stsb.split
>>> NamedSplit('train')
``` | {
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} | [] | closed | false | null | [] | null | 1 | "2020-06-08T09:13:00Z" | "2020-08-25T07:04:56Z" | "2020-06-08T09:16:59Z" | MEMBER | null | 0 | {
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} | There was a line from the original tfds script that was still there and causing issues when loading the c4 script. This one should fix #233 and allow anyone to load the c4 script to generate the dataset | {
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] | null | 2 | "2020-06-07T12:58:54Z" | "2020-06-12T08:28:51Z" | "2020-06-12T08:28:51Z" | CONTRIBUTOR | null | null | null | Hi, I successfully created a dataset and has made a pr #248.
But I have encountered several problems when I was creating it, and those should be easy to fix.
1. Not found dataset_info.json
should be fixed by #241 , eager to wait it be merged.
2. Forced to install `apach_beam`
If we should install it, then it might be better to include it in the pakcage dependency or specified in `CONTRIBUTING.md`
```
Traceback (most recent call last):
File "nlp-cli", line 10, in <module>
from nlp.commands.run_beam import RunBeamCommand
File "/home/yisiang/nlp/src/nlp/commands/run_beam.py", line 6, in <module>
import apache_beam as beam
ModuleNotFoundError: No module named 'apache_beam'
```
3. `cached_dir` is `None`
```
File "/home/yisiang/nlp/src/nlp/datasets/bookscorpus/aea0bd5142d26df645a8fce23d6110bb95ecb81772bb2a1f29012e329191962c/bookscorpus.py", line 88, in _split_generators
downloaded_path_or_paths = dl_manager.download_custom(_GDRIVE_FILE_ID, download_file_from_google_drive)
File "/home/yisiang/nlp/src/nlp/utils/download_manager.py", line 128, in download_custom
downloaded_path_or_paths = map_nested(url_to_downloaded_path, url_or_urls)
File "/home/yisiang/nlp/src/nlp/utils/py_utils.py", line 172, in map_nested
return function(data_struct)
File "/home/yisiang/nlp/src/nlp/utils/download_manager.py", line 126, in url_to_downloaded_path
return os.path.join(self._download_config.cache_dir, hash_url_to_filename(url))
File "/home/yisiang/miniconda3/envs/nlppr/lib/python3.7/posixpath.py", line 80, in join
a = os.fspath(a)
```
This is because this line
https://github.com/huggingface/nlp/blob/2e0a8639a79b1abc848cff5c669094d40bba0f63/src/nlp/commands/test.py#L30-L32
And I add `--cache_dir="...."` to `python nlp-cli test datasets/<your-dataset-folder> --save_infos --all_configs` in the doc, finally I could pass this error.
But it seems to ignore my arg and use `/home/yisiang/.cache/huggingface/datasets/bookscorpus/plain_text/1.0.0` as cahe_dir
4. There is no `pytest`
So maybe in the doc we should specify a step to install pytest
5. Not enough capacity in my `/tmp`
When run test for dummy data, I don't know why it ask me for 5.6g to download something,
```
def download_and_prepare
...
if not utils.has_sufficient_disk_space(self.info.size_in_bytes or 0, directory=self._cache_dir_root):
raise IOError(
"Not enough disk space. Needed: {} (download: {}, generated: {})".format(
utils.size_str(self.info.size_in_bytes or 0),
utils.size_str(self.info.download_size or 0),
> utils.size_str(self.info.dataset_size or 0),
)
)
E OSError: Not enough disk space. Needed: 5.62 GiB (download: 1.10 GiB, generated: 4.52 GiB)
```
I add a `processed_temp_dir="some/dir"; raw_temp_dir="another/dir"` to 71, and the test passed
https://github.com/huggingface/nlp/blob/a67a6c422dece904b65d18af65f0e024e839dbe8/tests/test_dataset_common.py#L70-L72
I suggest we can create tmp dir under the `/home/user/tmp` but not `/tmp`, because take our lab server for example, everyone use `/tmp` thus it has not much capacity. Or at least we can improve error message, so the user know is what directory has no space and how many has it lefted. Or we could do both.
6. name of datasets
I was surprised by the dataset name `books_corpus`, and didn't know it is from `class BooksCorpus(nlp.GeneratorBasedBuilder)` . I change it to `Bookscorpus` afterwards. I think this point shold be also on the doc.
7. More thorough doc to how to create `dataset.py`
I believe there will be.
**Feel free to close this issue** if you think these are solved. | {
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} | [] | closed | false | null | [] | null | 11 | "2020-06-07T12:54:56Z" | "2020-06-12T08:45:03Z" | "2020-06-12T08:45:02Z" | CONTRIBUTOR | null | 0 | {
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} | 1. I knew there is a branch `toronto_books_corpus`
- After I downloaded it, I found it is all non-english, and only have one row.
- It seems that it cites the wrong paper
- according to papar using it, it is called `BooksCorpus` but not `TornotoBooksCorpus`
2. It use a text mirror in google drive
- `bookscorpus.py` include a function `download_file_from_google_drive` , maybe you will want to put it elsewhere.
- text mirror is found in this [comment on the issue](https://github.com/soskek/bookcorpus/issues/24#issuecomment-556024973), and it said to have the same statistics as the one in the paper.
- You may want to download it and put it on your gs in case of it disappears someday.
3. Copyright ?
The paper has said
> **The BookCorpus Dataset.** In order to train our sentence similarity model we collected a corpus of 11,038 books ***from the web***. These are __**free books written by yet unpublished authors**__. We only included books that had more than 20K words in order to filter out perhaps noisier shorter stories. The dataset has books in 16 different genres, e.g., Romance (2,865 books), Fantasy (1,479), Science fiction (786), Teen (430), etc. Table 2 highlights the summary statistics of our book corpus.
and we have changed the form (not books), so I don't think it should have that problems. Or we can state that use it at your own risk or only for academic use. I know @thomwolf should know these things more.
This should solved #131 | {
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https://api.github.com/repos/huggingface/datasets/issues/247 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/247/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/247/comments | https://api.github.com/repos/huggingface/datasets/issues/247/events | https://github.com/huggingface/datasets/pull/247 | 632,380,078 | MDExOlB1bGxSZXF1ZXN0NDI5MTMwMzQ2 | 247 | Make all dataset downloads deterministic by applying `sorted` to glob and os.listdir | {
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} | [] | closed | false | null | [] | null | 3 | "2020-06-06T11:02:10Z" | "2020-06-08T09:18:16Z" | "2020-06-08T09:18:14Z" | MEMBER | null | 0 | {
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} | This PR makes all datasets loading deterministic by applying `sorted()` to all `glob.glob` and `os.listdir` statements.
Are there other "non-deterministic" functions apart from `glob.glob()` and `os.listdir()` that you can think of @thomwolf @lhoestq @mariamabarham @jplu ?
**Important**
It does break backward compatibility for these datasets because
1. When loading the complete dataset the order in which the examples are saved is different now
2. When loading only part of a split, the examples themselves might be different.
@patrickvonplaten - the nlp / longformer notebook has to be updated since the examples might now be different | {
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https://api.github.com/repos/huggingface/datasets/issues/246 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/246/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/246/comments | https://api.github.com/repos/huggingface/datasets/issues/246/events | https://github.com/huggingface/datasets/issues/246 | 632,380,054 | MDU6SXNzdWU2MzIzODAwNTQ= | 246 | What is the best way to cache a dataset? | {
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} | [] | closed | false | null | [] | null | 2 | "2020-06-06T11:02:07Z" | "2020-07-09T09:15:07Z" | "2020-07-09T09:15:07Z" | NONE | null | null | null | For example if I want to use streamlit with a nlp dataset:
```
@st.cache
def load_data():
return nlp.load_dataset('squad')
```
This code raises the error "uncachable object"
Right now I just fixed with a constant for my specific case:
```
@st.cache(hash_funcs={pyarrow.lib.Buffer: lambda b: 0})
```
But I was curious to know what is the best way in general
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https://api.github.com/repos/huggingface/datasets/issues/245 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/245/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/245/comments | https://api.github.com/repos/huggingface/datasets/issues/245/events | https://github.com/huggingface/datasets/issues/245 | 631,985,108 | MDU6SXNzdWU2MzE5ODUxMDg= | 245 | SST-2 test labels are all -1 | {
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} | [] | closed | false | null | [] | null | 10 | "2020-06-05T21:41:42Z" | "2021-12-08T00:47:32Z" | "2020-06-06T16:56:41Z" | CONTRIBUTOR | null | null | null | I'm trying to test a model on the SST-2 task, but all the labels I see in the test set are -1.
```
>>> import nlp
>>> glue = nlp.load_dataset('glue', 'sst2')
>>> glue
{'train': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 67349), 'validation': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 872), 'test': Dataset(schema: {'sentence': 'string', 'label': 'int64', 'idx': 'int32'}, num_rows: 1821)}
>>> list(l['label'] for l in glue['test'])
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https://api.github.com/repos/huggingface/datasets/issues/244 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/244/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/244/comments | https://api.github.com/repos/huggingface/datasets/issues/244/events | https://github.com/huggingface/datasets/pull/244 | 631,869,155 | MDExOlB1bGxSZXF1ZXN0NDI4NjgxMTcx | 244 | Add Allociné Dataset | {
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} | [] | closed | false | null | [] | null | 3 | "2020-06-05T19:19:26Z" | "2020-06-11T07:47:26Z" | "2020-06-11T07:47:26Z" | CONTRIBUTOR | null | 0 | {
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} | This is a french binary sentiment classification dataset, which was used to train this model: https://huggingface.co/tblard/tf-allocine.
Basically, it's a french "IMDB" dataset, with more reviews.
More info on [this repo](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert). | {
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https://api.github.com/repos/huggingface/datasets/issues/243 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/243/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/243/comments | https://api.github.com/repos/huggingface/datasets/issues/243/events | https://github.com/huggingface/datasets/pull/243 | 631,735,848 | MDExOlB1bGxSZXF1ZXN0NDI4NTY2MTEy | 243 | Specify utf-8 encoding for GLUE | {
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This makes the GLUE-MNLI dataset readable on my machine, not sure if it's a Windows-only bug. | {
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} | [] | closed | false | null | [] | null | 2 | "2020-06-05T16:30:01Z" | "2020-06-09T16:06:47Z" | "2020-06-08T08:45:03Z" | CONTRIBUTOR | null | null | null | When I run
```python
dataset = nlp.load_dataset('glue', 'mnli')
```
I get an encoding error (could it be because I'm using Windows?) :
```python
# Lots of error log lines later...
~\Miniconda3\envs\nlp\lib\site-packages\tqdm\std.py in __iter__(self)
1128 try:
-> 1129 for obj in iterable:
1130 yield obj
~\Miniconda3\envs\nlp\lib\site-packages\nlp\datasets\glue\5256cc2368cf84497abef1f1a5f66648522d5854b225162148cb8fc78a5a91cc\glue.py in _generate_examples(self, data_file, split, mrpc_files)
529
--> 530 for n, row in enumerate(reader):
531 if is_cola_non_test:
~\Miniconda3\envs\nlp\lib\csv.py in __next__(self)
110 self.fieldnames
--> 111 row = next(self.reader)
112 self.line_num = self.reader.line_num
~\Miniconda3\envs\nlp\lib\encodings\cp1252.py in decode(self, input, final)
22 def decode(self, input, final=False):
---> 23 return codecs.charmap_decode(input,self.errors,decoding_table)[0]
24
UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 6744: character maps to <undefined>
```
Anyway this can be solved by specifying to decode in UTF when reading the csv file. I am proposing a PR if that's okay. | {
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} | If the cache dir of a dataset is empty, the dataset fails to load and throws a FileNotFounfError. We could end up with empty cache dir because there was a line in the code that created the cache dir without using a temp dir. Using a temp dir is useful as it gets renamed to the real cache dir only if the full process is successful.
So I removed this bad line, and I also reordered things a bit to make sure that we always use a temp dir. I also added warning if we still end up with empty cache dirs in the future.
This should fix #239
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} | [] | closed | false | null | [] | null | 4 | "2020-06-05T09:03:26Z" | "2020-06-08T09:18:14Z" | "2020-06-08T09:18:14Z" | MEMBER | null | null | null | When calling:
```python
import nlp
dataset = nlp.load_dataset("trivia_qa", split="validation[:1%]")
```
the resulting dataset is not deterministic over different google colabs.
After talking to @thomwolf, I suspect the reason to be the use of `glob.glob` in line:
https://github.com/huggingface/nlp/blob/2e0a8639a79b1abc848cff5c669094d40bba0f63/datasets/trivia_qa/trivia_qa.py#L180
which seems to return an ordering of files that depends on the filesystem:
https://stackoverflow.com/questions/6773584/how-is-pythons-glob-glob-ordered
I think we should go through all the dataset scripts and make sure to have deterministic behavior.
A simple solution for `glob.glob()` would be to just replace it with `sorted(glob.glob())` to have everything sorted by name.
What do you think @lhoestq? | {
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https://api.github.com/repos/huggingface/datasets/issues/239 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/239/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/239/comments | https://api.github.com/repos/huggingface/datasets/issues/239/events | https://github.com/huggingface/datasets/issues/239 | 631,340,440 | MDU6SXNzdWU2MzEzNDA0NDA= | 239 | [Creating new dataset] Not found dataset_info.json | {
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] | null | 5 | "2020-06-05T06:15:04Z" | "2020-06-07T13:01:04Z" | "2020-06-07T13:01:04Z" | CONTRIBUTOR | null | null | null | Hi, I am trying to create Toronto Book Corpus. #131
I ran
`~/nlp % python nlp-cli test datasets/bookcorpus --save_infos --all_configs`
but this doesn't create `dataset_info.json` and try to use it
```
INFO:nlp.load:Checking datasets/bookcorpus/bookcorpus.py for additional imports.
INFO:filelock:Lock 139795325778640 acquired on datasets/bookcorpus/bookcorpus.py.lock
INFO:nlp.load:Found main folder for dataset datasets/bookcorpus/bookcorpus.py at /home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/datasets/bookcorpus
INFO:nlp.load:Found specific version folder for dataset datasets/bookcorpus/bookcorpus.py at /home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/datasets/bookcorpus/8e84759446cf68d0b0deb3417e60cc331f30a3bbe58843de18a0f48e87d1efd9
INFO:nlp.load:Found script file from datasets/bookcorpus/bookcorpus.py to /home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/datasets/bookcorpus/8e84759446cf68d0b0deb3417e60cc331f30a3bbe58843de18a0f48e87d1efd9/bookcorpus.py
INFO:nlp.load:Couldn't find dataset infos file at datasets/bookcorpus/dataset_infos.json
INFO:nlp.load:Found metadata file for dataset datasets/bookcorpus/bookcorpus.py at /home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/datasets/bookcorpus/8e84759446cf68d0b0deb3417e60cc331f30a3bbe58843de18a0f48e87d1efd9/bookcorpus.json
INFO:filelock:Lock 139795325778640 released on datasets/bookcorpus/bookcorpus.py.lock
INFO:nlp.builder:Overwrite dataset info from restored data version.
INFO:nlp.info:Loading Dataset info from /home/yisiang/.cache/huggingface/datasets/book_corpus/plain_text/1.0.0
Traceback (most recent call last):
File "nlp-cli", line 37, in <module>
service.run()
File "/home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/commands/test.py", line 78, in run
builders.append(builder_cls(name=config.name, data_dir=self._data_dir))
File "/home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/builder.py", line 610, in __init__
super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)
File "/home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/builder.py", line 152, in __init__
self.info = DatasetInfo.from_directory(self._cache_dir)
File "/home/yisiang/miniconda3/envs/ml/lib/python3.7/site-packages/nlp/info.py", line 157, in from_directory
with open(os.path.join(dataset_info_dir, DATASET_INFO_FILENAME), "r") as f:
FileNotFoundError: [Errno 2] No such file or directory: '/home/yisiang/.cache/huggingface/datasets/book_corpus/plain_text/1.0.0/dataset_info.json'
```
btw, `ls /home/yisiang/.cache/huggingface/datasets/book_corpus/plain_text/1.0.0/` show me nothing is in the directory.
I have also pushed the script to my fork [bookcorpus.py](https://github.com/richardyy1188/nlp/blob/bookcorpusdev/datasets/bookcorpus/bookcorpus.py).
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https://api.github.com/repos/huggingface/datasets/issues/238 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/238/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/238/comments | https://api.github.com/repos/huggingface/datasets/issues/238/events | https://github.com/huggingface/datasets/issues/238 | 631,260,143 | MDU6SXNzdWU2MzEyNjAxNDM= | 238 | [Metric] Bertscore : Warning : Empty candidate sentence; Setting recall to be 0. | {
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] | closed | false | null | [] | null | 1 | "2020-06-05T02:14:47Z" | "2020-06-29T17:10:19Z" | "2020-06-29T17:10:19Z" | NONE | null | null | null | When running BERT-Score, I'm meeting this warning :
> Warning: Empty candidate sentence; Setting recall to be 0.
Code :
```
import nlp
metric = nlp.load_metric("bertscore")
scores = metric.compute(["swag", "swags"], ["swags", "totally something different"], lang="en", device=0)
```
---
**What am I doing wrong / How can I hide this warning ?** | {
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https://api.github.com/repos/huggingface/datasets/issues/237 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/237/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/237/comments | https://api.github.com/repos/huggingface/datasets/issues/237/events | https://github.com/huggingface/datasets/issues/237 | 631,199,940 | MDU6SXNzdWU2MzExOTk5NDA= | 237 | Can't download MultiNLI | {
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```python
dataset = load_dataset('multi_nli')
```
I get this long error:
```python
---------------------------------------------------------------------------
OSError Traceback (most recent call last)
<ipython-input-13-3b11f6be4cb9> in <module>
1 # Load a dataset and print the first examples in the training set
2 # nli_dataset = nlp.load_dataset('multi_nli')
----> 3 dataset = load_dataset('multi_nli')
4 # nli_dataset = nlp.load_dataset('multi_nli', split='validation_matched[:10%]')
5 # print(nli_dataset['train'][0])
~\Miniconda3\envs\nlp\lib\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)
514
515 # Download and prepare data
--> 516 builder_instance.download_and_prepare(
517 download_config=download_config,
518 download_mode=download_mode,
~\Miniconda3\envs\nlp\lib\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)
417 with utils.temporary_assignment(self, "_cache_dir", tmp_data_dir):
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 )
~\Miniconda3\envs\nlp\lib\site-packages\nlp\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
455 split_dict = SplitDict(dataset_name=self.name)
456 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs)
--> 457 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
458 # Checksums verification
459 if verify_infos:
~\Miniconda3\envs\nlp\lib\site-packages\nlp\datasets\multi_nli\60774175381b9f3f1e6ae1028229e3cdb270d50379f45b9f2c01008f50f09e6b\multi_nli.py in _split_generators(self, dl_manager)
99 def _split_generators(self, dl_manager):
100
--> 101 downloaded_dir = dl_manager.download_and_extract(
102 "http://storage.googleapis.com/tfds-data/downloads/multi_nli/multinli_1.0.zip"
103 )
~\Miniconda3\envs\nlp\lib\site-packages\nlp\utils\download_manager.py in download_and_extract(self, url_or_urls)
214 extracted_path(s): `str`, extracted paths of given URL(s).
215 """
--> 216 return self.extract(self.download(url_or_urls))
217
218 def get_recorded_sizes_checksums(self):
~\Miniconda3\envs\nlp\lib\site-packages\nlp\utils\download_manager.py in extract(self, path_or_paths)
194 path_or_paths.
195 """
--> 196 return map_nested(
197 lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,
198 )
~\Miniconda3\envs\nlp\lib\site-packages\nlp\utils\py_utils.py in map_nested(function, data_struct, dict_only, map_tuple)
168 return tuple(mapped)
169 # Singleton
--> 170 return function(data_struct)
171
172
~\Miniconda3\envs\nlp\lib\site-packages\nlp\utils\download_manager.py in <lambda>(path)
195 """
196 return map_nested(
--> 197 lambda path: cached_path(path, extract_compressed_file=True, force_extract=False), path_or_paths,
198 )
199
~\Miniconda3\envs\nlp\lib\site-packages\nlp\utils\file_utils.py in cached_path(url_or_filename, download_config, **download_kwargs)
231 if is_zipfile(output_path):
232 with ZipFile(output_path, "r") as zip_file:
--> 233 zip_file.extractall(output_path_extracted)
234 zip_file.close()
235 elif tarfile.is_tarfile(output_path):
~\Miniconda3\envs\nlp\lib\zipfile.py in extractall(self, path, members, pwd)
1644
1645 for zipinfo in members:
-> 1646 self._extract_member(zipinfo, path, pwd)
1647
1648 @classmethod
~\Miniconda3\envs\nlp\lib\zipfile.py in _extract_member(self, member, targetpath, pwd)
1698
1699 with self.open(member, pwd=pwd) as source, \
-> 1700 open(targetpath, "wb") as target:
1701 shutil.copyfileobj(source, target)
1702
OSError: [Errno 22] Invalid argument: 'C:\\Users\\Python\\.cache\\huggingface\\datasets\\3e12413b8ec69f22dfcfd54a79d1ba9e7aac2e18e334bbb6b81cca64fd16bffc\\multinli_1.0\\Icon\r'
```
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} | Hello,
Thanks for the amazing library that you put together. I'm Alessandro Suglia, the first author of CompGuessWhat?!, a recently released dataset for grounded language learning accepted to ACL 2020 ([https://compguesswhat.github.io](https://compguesswhat.github.io)).
This pull-request adds the CompGuessWhat?! splits that have been extracted from the original dataset. This is only part of our evaluation framework because there is also an additional split of the dataset that has a completely different set of games. I didn't integrate it yet because I didn't know what would be the best practice in this case. Let me clarify the scenario.
In our paper, we have a main dataset (let's call it `compguesswhat-gameplay`) and a zero-shot dataset (let's call it `compguesswhat-zs-gameplay`). In the current code of the pull-request, I have only integrated `compguesswhat-gameplay`. I was thinking that it would be nice to have the `compguesswhat-zs-gameplay` in the same dataset class by simply specifying some particular option to the `nlp.load_dataset()` factory. For instance:
```python
cgw = nlp.load_dataset("compguesswhat")
cgw_zs = nlp.load_dataset("compguesswhat", zero_shot=True)
```
The other option would be to have a separate dataset class. Any preferences? | {
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} | ## Adding an *experimental datasets* folder
After using the 🤗nlp library for some time, I find that while it makes it super easy to create new memory-mapped datasets with lots of cool utilities, a lot of what I want to do doesn't work well with the current `MockDownloader` based testing paradigm, making it hard to share my work with the community.
My suggestion would be to add a **datasets\_experimental** folder so we can start making these new datasets public without having to completely re-think testing for every single one. We would allow contributors to submit dataset PRs in this folder, but require an explanation for why the current testing suite doesn't work for them. We can then aggregate the feedback and periodically see what's missing from the current tests.
I have added a **datasets\_experimental** folder to the repository and S3 bucket with two initial datasets: ELI5 (explainlikeimfive) and a Wikipedia Snippets dataset to support indexing (wiki\_snippets)
### ELI5
#### Dataset description
This allows people to download the [ELI5: Long Form Question Answering](https://arxiv.org/abs/1907.09190) dataset, along with two variants based on the r/askscience and r/AskHistorians. Full Reddit dumps for each month are downloaded from [pushshift](https://files.pushshift.io/reddit/), filtered for submissions and comments from the desired subreddits, then deleted one at a time to save space. The resulting dataset is split into a training, validation, and test dataset for r/explainlikeimfive, r/askscience, and r/AskHistorians respectively, where each item is a question along with all of its high scoring answers.
#### Issues with the current testing
1. the list of files to be downloaded is not pre-defined, but rather determined by parsing an index web page at run time. This is necessary as the name and compression type of the dump files changes from month to month as the pushshift website is maintained. Currently, the dummy folder requires the user to know which files will be downloaded.
2. to save time, the script works on the compressed files using the corresponding python packages rather than first running `download\_and\_extract` then filtering the extracted files.
### Wikipedia Snippets
#### Dataset description
This script creates a *snippets* version of a source Wikipedia dataset: each article is split into passages of fixed length which can then be indexed using ElasticSearch or a dense indexer. The script currently handles all **wikipedia** and **wiki40b** source datasets, and allows the user to choose the passage length and how much overlap they want across passages. In addition to the passage text, each snippet also has the article title, list of titles of sections covered by the text, and information to map the passage back to the initial dataset at the paragraph and character level.
#### Issues with the current testing
1. The DatasetBuilder needs to call `nlp.load_dataset()`. Currently, testing is not recursive (the test doesn't know where to find the dummy data for the source dataset)
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https://api.github.com/repos/huggingface/datasets/issues/234 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/234/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/234/comments | https://api.github.com/repos/huggingface/datasets/issues/234/events | https://github.com/huggingface/datasets/issues/234 | 630,534,427 | MDU6SXNzdWU2MzA1MzQ0Mjc= | 234 | Huggingface NLP, Uploading custom dataset | {
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Does anyone know how we can call our custom dataset using the nlp.load command? Let's say that I have a dataset based on the same format as that of squad-v1.1, how am I supposed to load it using huggingface nlp.
Thank you! | {
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```
dataset = nlp.load_dataset('c4', 'en', beam_runner='DirectRunner'
, data_dir='/mypath')
```
and i met failure as follows
```
Downloading and preparing dataset c4/en (download: Unknown size, generated: Unknown size, total: Unknown size) to /home/adam/.cache/huggingface/datasets/c4/en/2.3.0...
Traceback (most recent call last):
File "download_corpus.py", line 38, in <module>
, data_dir='/home/adam/data/corpus/en/c4')
File "/home/adam/anaconda3/envs/adam/lib/python3.7/site-packages/nlp/load.py", line 520, in load_dataset
save_infos=save_infos,
File "/home/adam/anaconda3/envs/adam/lib/python3.7/site-packages/nlp/builder.py", line 420, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/adam/anaconda3/envs/adam/lib/python3.7/site-packages/nlp/builder.py", line 816, in _download_and_prepare
dl_manager, verify_infos=False, pipeline=pipeline,
File "/home/adam/anaconda3/envs/adam/lib/python3.7/site-packages/nlp/builder.py", line 457, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/adam/anaconda3/envs/adam/lib/python3.7/site-packages/nlp/datasets/c4/f545de9f63300d8d02a6795e2eb34e140c47e62a803f572ac5599e170ee66ecc/c4.py", line 175, in _split_generators
dl_manager.download_checksums(_CHECKSUMS_URL)
AttributeError: 'DownloadManager' object has no attribute 'download_checksums
```
can i get any advice? | {
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} | With this PR users will be able to upload their own datasets and metrics.
As mentioned in #181, I had to use the new endpoints and revert the use of dataclasses (just in case we have changes in the API in the future).
We now distinguish commands for datasets and commands for metrics:
```bash
nlp-cli upload_dataset <path/to/dataset>
nlp-cli upload_metric <path/to/metric>
nlp-cli s3_datasets {rm, ls}
nlp-cli s3_metrics {rm, ls}
```
Does it sound good to you @julien-c @thomwolf ? | {
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@yjernite | {
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```
FileNotFoundError: [Errno 2] No such file or directory: '/home/sasha/.cache/huggingface/datasets/xnli/plain_text/1.0.0/dataset_info.json'
Traceback:
File "/home/sasha/.local/lib/python3.7/site-packages/streamlit/ScriptRunner.py", line 322, in _run_script
exec(code, module.__dict__)
File "/home/sasha/nlp_viewer/run.py", line 86, in <module>
dts, fail = get(str(option.id), str(conf_option.name) if conf_option else None)
File "/home/sasha/.local/lib/python3.7/site-packages/streamlit/caching.py", line 591, in wrapped_func
return get_or_create_cached_value()
File "/home/sasha/.local/lib/python3.7/site-packages/streamlit/caching.py", line 575, in get_or_create_cached_value
return_value = func(*args, **kwargs)
File "/home/sasha/nlp_viewer/run.py", line 72, in get
builder_instance = builder_cls(name=conf)
File "/home/sasha/.local/lib/python3.7/site-packages/nlp/builder.py", line 610, in __init__
super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)
File "/home/sasha/.local/lib/python3.7/site-packages/nlp/builder.py", line 152, in __init__
self.info = DatasetInfo.from_directory(self._cache_dir)
File "/home/sasha/.local/lib/python3.7/site-packages/nlp/info.py", line 157, in from_directory
with open(os.path.join(dataset_info_dir, DATASET_INFO_FILENAME), "r") as f:
```
Is it possible to see if the dataset_info.json is correctly placed? | {
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But at the first try, it tell me to install `apache_beam` and `mwparserfromhell`, which I thought wouldn't be used according to #204 , it was kind of confusing me at that time.
Maybe we should not force users to install these ? Or we just add them to`nlp`'s dependency ? | {
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Here is a self-contained notebook to reproduce both scores : https://colab.research.google.com/drive/14EyAXValB6UzKY9x4rs_T3pyL7alpw_F?usp=sharing
---
`nlp` : (Only mid F-scores)
>rouge1 0.33508031962733364
rouge2 0.14574333776191592
rougeL 0.2321187823256159
`files2rouge` :
>Running ROUGE...
===========================
1 ROUGE-1 Average_R: 0.48873 (95%-conf.int. 0.41192 - 0.56339)
1 ROUGE-1 Average_P: 0.29010 (95%-conf.int. 0.23605 - 0.34445)
1 ROUGE-1 Average_F: 0.34761 (95%-conf.int. 0.29479 - 0.39871)
===========================
1 ROUGE-2 Average_R: 0.20280 (95%-conf.int. 0.14969 - 0.26244)
1 ROUGE-2 Average_P: 0.12772 (95%-conf.int. 0.08603 - 0.17752)
1 ROUGE-2 Average_F: 0.14798 (95%-conf.int. 0.10517 - 0.19240)
===========================
1 ROUGE-L Average_R: 0.32960 (95%-conf.int. 0.26501 - 0.39676)
1 ROUGE-L Average_P: 0.19880 (95%-conf.int. 0.15257 - 0.25136)
1 ROUGE-L Average_F: 0.23619 (95%-conf.int. 0.19073 - 0.28663)
---
When using longer predictions/gold, the difference is bigger.
**How can I reproduce same score as `files2rouge` ?**
@lhoestq
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] | null | 6 | "2020-05-30T18:30:40Z" | "2023-08-26T17:38:48Z" | "2021-01-04T09:53:32Z" | NONE | null | null | null | Hi, I am interested in porting google research's new BLEURT learned metric to PyTorch (because I wish to do something experimental with language generation and backpropping through BLEURT). I noticed that you guys don't have it yet so I am partly just asking if you plan to add it (@thomwolf said you want to do so on Twitter).
I had a go of just like manually using the checkpoint that they publish which includes the weights. It seems like the architecture is exactly aligned with the out-of-the-box BertModel in transformers just with a single linear layer on top of the CLS embedding. I loaded all the weights to the PyTorch model but I am not able to get the same numbers as the BLEURT package's python api. Here is my colab notebook where I tried https://colab.research.google.com/drive/1Bfced531EvQP_CpFvxwxNl25Pj6ptylY?usp=sharing . If you have any pointers on what might be going wrong that would be much appreciated!
Thank you muchly! | {
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I think it would be interesting to add the FLUE dataset for francophones or anyone wishing to work on French.
In other requests, I read that you are already working on some datasets, and I was wondering if FLUE was planned.
If it is not the case, I can provide each of the cleaned FLUE datasets (in the form of a directly exploitable dataset rather than in the original xml formats which require additional processing, with the French part for cases where the dataset is based on a multilingual dataframe, etc.). | {
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https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb
breaks when running this cell:
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} | When I run the command `RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_real_dataset_arcd` while working on #220. I get the error ` unexpected keyword argument "'download_and_prepare_kwargs'"` at the level of `load_dataset`. Indeed, this [function](https://github.com/huggingface/nlp/blob/master/src/nlp/load.py#L441) no longer has the argument `download_and_prepare_kwargs` but rather `download_config`. So here I change the tests accordingly. | {
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} | This should fix your env because you had `mwparserfromhell ` as a first party for `isort` @patrickvonplaten | {
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