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https://api.github.com/repos/huggingface/datasets/issues/2243 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2243/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2243/comments | https://api.github.com/repos/huggingface/datasets/issues/2243/events | https://github.com/huggingface/datasets/issues/2243 | 862,909,389 | MDU6SXNzdWU4NjI5MDkzODk= | 2,243 | Map is slow and processes batches one after another | {
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"Hi @villmow, thanks for reporting.\r\n\r\nCould you please try with the Datasets version 1.6? We released it yesterday and it fixes some issues about the processing speed. You can see the fix implemented by @lhoestq here: #2122.\r\n\r\nOnce you update Datasets, please confirm if the problem persists.",
"Hi @albe... | 2021-04-20T14:58:20 | 2021-05-03T17:54:33 | 2021-05-03T17:54:32 | NONE | null | null | null | null | ## Describe the bug
I have a somewhat unclear bug to me, where I can't figure out what the problem is. The code works as expected on a small subset of my dataset (2000 samples) on my local machine, but when I execute the same code with a larger dataset (1.4 million samples) this problem occurs. Thats why I can't give exact steps to reproduce, I'm sorry.
I process a large dataset in a two step process. I first call map on a dataset I load from disk and create a new dataset from it. This works like expected and `map` uses all workers I started it with. Then I process the dataset created by the first step, again with `map`, which is really slow and starting only one or two process at a time. Number of processes is the same for both steps.
pseudo code:
```python
ds = datasets.load_from_disk("path")
new_dataset = ds.map(work, batched=True, ...) # fast uses all processes
final_dataset = new_dataset.map(work2, batched=True, ...) # slow starts one process after another
```
## Expected results
Second stage should be as fast as the first stage.
## Versions
Paste the output of the following code:
- Datasets: 1.5.0
- Python: 3.8.8 (default, Feb 24 2021, 21:46:12)
- Platform: Linux-5.4.0-60-generic-x86_64-with-glibc2.10
Do you guys have any idea? Thanks a lot! | {
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https://api.github.com/repos/huggingface/datasets/issues/2242 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2242/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2242/comments | https://api.github.com/repos/huggingface/datasets/issues/2242/events | https://github.com/huggingface/datasets/issues/2242 | 862,870,205 | MDU6SXNzdWU4NjI4NzAyMDU= | 2,242 | Link to datasets viwer on Quick Tour page returns "502 Bad Gateway" | {
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"This should be fixed now!\r\n\r\ncc @srush "
] | 2021-04-20T14:19:51 | 2021-04-20T15:02:45 | 2021-04-20T15:02:45 | NONE | null | null | null | null | Link to datasets viwer (https://huggingface.co/datasets/viewer/) on Quick Tour page (https://huggingface.co/docs/datasets/quicktour.html) returns "502 Bad Gateway"
The same error with https://huggingface.co/datasets/viewer/?dataset=glue&config=mrpc | {
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https://api.github.com/repos/huggingface/datasets/issues/2239 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2239/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2239/comments | https://api.github.com/repos/huggingface/datasets/issues/2239/events | https://github.com/huggingface/datasets/issues/2239 | 861,904,306 | MDU6SXNzdWU4NjE5MDQzMDY= | 2,239 | Error loading wikihow dataset | {
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"Hi @odellus, thanks for reporting.\r\n\r\nThe `wikihow` dataset has 2 versions:\r\n- `all`: Consisting of the concatenation of all paragraphs as the articles and the bold lines as the reference summaries.\r\n- `sep`: Consisting of each paragraph and its summary.\r\n\r\nTherefore, in order to load it, you have to s... | 2021-04-19T21:02:31 | 2021-04-20T16:33:11 | 2021-04-20T16:33:11 | CONTRIBUTOR | null | null | null | null | ## Describe the bug
When attempting to load wikihow into a dataset with
```python
from datasets import load_dataset
dataset = load_dataset('wikihow', data_dir='./wikihow')
```
I get the message:
```
AttributeError: 'BuilderConfig' object has no attribute 'filename'
```
at the end of a [full stack trace](https://gist.github.com/odellus/602c3b2de52f541d353b1022f320ffc2).
## Steps to reproduce the bug
I have followed the instructions for creating a wikihow dataset. The [wikihow dataset site](https://huggingface.co/datasets/wikihow) says to use
```python
from datasets import load_dataset
dataset = load_dataset('wikihow')
```
to load the dataset. I do so and I get the message
```
AssertionError: The dataset wikihow with config all requires manual data.
Please follow the manual download instructions: You need to manually download two wikihow files. An overview of which files to download can be seen at https://github.com/mahnazkoupaee/WikiHow-Dataset.
You need to download the following two files manually:
1) https://ucsb.app.box.com/s/ap23l8gafpezf4tq3wapr6u8241zz358 and save the file under <path/to/folder>/wikihowAll.csv
2) https://ucsb.app.box.com/s/7yq601ijl1lzvlfu4rjdbbxforzd2oag and save the file under <path/to/folder>/wikihowSep.csv
The <path/to/folder> can e.g. be "~/manual_wikihow_data".
Wikihow can then be loaded using the following command `datasets.load_dataset("wikihow", data_dir="<path/to/folder>")`.
.
Manual data can be loaded with `datasets.load_dataset(wikihow, data_dir='<path/to/manual/data>')
```
So I create a directory `./wikihow` and download `wikihowAll.csv` and `wikihowSep.csv` into the new directory.
Then I run
```python
from datasets import load_dataset
dataset = load_dataset('wikihow', data_dir='./wikihow')
```
that's when I get the [stack trace](https://gist.github.com/odellus/602c3b2de52f541d353b1022f320ffc2)
## Expected results
I expected it to load the downloaded files into a dataset.
## Actual results
```python
Using custom data configuration default-data_dir=.%2Fwikihow
Downloading and preparing dataset wikihow/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/azureuser/.cache/huggingface/datasets/wikihow/default-data_dir=.%2Fwikihow/0.0.0/58f42f8f0e4d459811a0f69aaab35870093830ccd58006769e7e1eb3e0e686c2... ---------------------------------------------------------------------------
AttributeError
Traceback (most recent call last)
<ipython-input-9-5e4d40142f30> in <module>
----> 1 dataset = load_dataset('wikihow',data_dir='./wikihow')
~/.local/lib/python3.6/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs)
745 try_from_hf_gcs=try_from_hf_gcs,
746 base_path=base_path,-->
747 use_auth_token=use_auth_token,
748 )
749
~/.local/lib/python3.6/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
577 if not downloaded_from_gcs:
578 self._download_and_prepare( -->
579 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
580 )
581 # Sync info
~/.local/lib/python3.6/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
632 split_dict = SplitDict(dataset_name=self.name)
633 split_generators_kwargs = self._make_split_generators_kwargs(prepare_split_kwargs) -->
634 split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
635
636 # Checksums verification
~/.cache/huggingface/modules/datasets_modules/datasets/wikihow/58f42f8f0e4d459811a0f69aaab35870093830ccd58006769e7e1eb3e0e686c2/wikihow.py in _split_generators(self, dl_manager)
132
133 path_to_manual_file = os.path.join(
--> 134 os.path.abspath(os.path.expanduser(dl_manager.manual_dir)), self.config.filename
135 )
136
AttributeError: 'BuilderConfig' object has no attribute 'filename'
```
## Versions
Paste the output of the following code:
```python
import datasets
import sys
import platform
print(f"""
- Datasets: {datasets.__version__}
- Python: {sys.version}
- Platform: {platform.platform()}
""")
```
```
- Datasets: 1.5.0
- Python: 3.6.9 (default, Jan 26 2021, 15:33:00) [GCC 8.4.0]
- Platform: Linux-5.4.0-1046-azure-x86_64-with-Ubuntu-18.04-bionic
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https://api.github.com/repos/huggingface/datasets/issues/2237 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2237/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2237/comments | https://api.github.com/repos/huggingface/datasets/issues/2237/events | https://github.com/huggingface/datasets/issues/2237 | 861,427,439 | MDU6SXNzdWU4NjE0Mjc0Mzk= | 2,237 | Update Dataset.dataset_size after transformed with map | {
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"@albertvillanova I would like to take this up. It would be great if you could point me as to how the dataset size is calculated in HF. Thanks!"
] | 2021-04-19T15:19:38 | 2021-04-20T14:22:05 | null | MEMBER | null | null | null | null | After loading a dataset, if we transform it by using `.map` its `dataset_size` attirbute is not updated. | null | {
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] | open | false | null | [] | [] | 2021-04-19T14:46:26 | 2021-04-19T14:46:26 | null | NONE | null | null | null | null | ## Request to add StrategyQA dataset
- **Name:** StrategyQA
- **Description:** open-domain QA [(project page)](https://allenai.org/data/strategyqa)
- **Paper:** [url](https://arxiv.org/pdf/2101.02235.pdf)
- **Data:** [here](https://allenai.org/data/strategyqa)
- **Motivation:** uniquely-formulated dataset that also includes a question-decomposition breakdown and associated Wikipedia annotations for each step. Good for multi-hop reasoning modeling.
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https://api.github.com/repos/huggingface/datasets/issues/2230 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2230/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2230/comments | https://api.github.com/repos/huggingface/datasets/issues/2230/events | https://github.com/huggingface/datasets/issues/2230 | 859,817,159 | MDU6SXNzdWU4NTk4MTcxNTk= | 2,230 | Keys yielded while generating dataset are not being checked | {
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"Hi ! Indeed there's no verification on the uniqueness nor the types of the keys.\r\nDo you already have some ideas of what you would like to implement and how ?",
"Hey @lhoestq, thank you so much for the opportunity.\r\nAlthough I haven't had much experience with the HF Datasets code, after a careful look at how... | 2021-04-16T13:29:47 | 2021-05-10T17:31:21 | 2021-05-10T17:31:21 | CONTRIBUTOR | null | null | null | null | The keys used in the dataset generation script to ensure the same order is generated on every user's end should be checked for their types (i.e either `str` or `int`) as well as whether they are unique or not.
Currently, the keys are not being checked for any of these, as evident from `xnli' dataset generation:
https://github.com/huggingface/datasets/blob/56346791aed417306d054d89bd693d6b7eab17f7/datasets/xnli/xnli.py#L196
Even after having a tuple as key, the dataset is generated without any warning.
Also, as tested in the case of `anli` dataset (I tweeked the dataset script to use `1` as a key for every example):
```
>>> import datasets
>>> nik = datasets.load_dataset('anli')
Downloading and preparing dataset anli/plain_text (download: 17.76 MiB, generated: 73.55 MiB, post-processed: Unknown size, total: 91.31 MiB) to C:\Users\nikhil\.cache\huggingface\datasets\anli\plain_text\0.1.0\43fa2c99c10bf8478f1fa0860f7b122c6b277c4c41306255b7641257cf4e3299...
0 examples [00:00, ? examples/s]1 {'uid': '0fd0abfb-659e-4453-b196-c3a64d2d8267', 'premise': 'The Parma trolleybus system (Italian: "Rete filoviaria di Parma" ) forms part of the public transport network of the city and "comune" of Parma, in the region of Emilia-Romagna, northern Italy. In operation since 1953, the system presently comprises four urban routes.', 'hypothesis': 'The trolleybus system has over 2 urban routes', 'label': 'entailment', 'reason': ''}
2021-04-16 12:38:14.483968: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
1 examples [00:01, 1.87s/ examples]1 {'uid': '7ed72ff4-40b7-4f8a-b1b9-6c612aa62c84', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 – 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage/science fiction adventure series "The Champions". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': "Sharron Macready was a popular character through the 1980's.", 'label': 'neutral', 'reason': ''}
1 {'uid': '5d2930a3-62ac-485d-94d7-4e36cbbcd7b5', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 – 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage/science fiction adventure series "The Champions". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': "Bastedo didn't keep any pets because of her views on animal rights.", 'label': 'neutral', 'reason': ''}
1 {'uid': '324db753-ddc9-4a85-a825-f09e2e5aebdd', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 – 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage/science fiction adventure series "The Champions". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Alexandra Bastedo was named by her mother.', 'label': 'neutral', 'reason': ''}
1 {'uid': '4874f429-da0e-406a-90c7-22240ff3ddf8', 'premise': 'Alexandra Lendon Bastedo (9 March 1946 – 12 January 2014) was a British actress, best known for her role as secret agent Sharron Macready in the 1968 British espionage/science fiction adventure series "The Champions". She has been cited as a sex symbol of the 1960s and 1970s. Bastedo was a vegetarian and animal welfare advocate.', 'hypothesis': 'Bastedo cared for all the animals that inhabit the earth.', 'label': 'neutral', 'reason': ''}
```
Here also, the dataset was generated successfuly even hough it had same keys without any warning.
The reason appears to stem from here:
https://github.com/huggingface/datasets/blob/56346791aed417306d054d89bd693d6b7eab17f7/src/datasets/builder.py#L988
Here, although it has access to every key, but it is not being checked and the example is written directly:
https://github.com/huggingface/datasets/blob/56346791aed417306d054d89bd693d6b7eab17f7/src/datasets/builder.py#L992
I would like to take this issue if you allow me. Thank You! | {
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"Hi ! Sure sounds good. Also if you find other datasets that use tuples instead of str/int, you can also fix them !\r\nthanks :)",
"@lhoestq I have sent a PR for fixing the issue. Would be great if you could have a look! Thanks!"
] | 2021-04-16T13:21:53 | 2021-04-19T08:56:42 | 2021-04-19T08:56:42 | CONTRIBUTOR | null | null | null | null | When using `ds = datasets.load_dataset('xnli', 'ar')`, the dataset generation script uses the following section of code in the egging, which yields a tuple key instead of the specified `str` or `int` key:
https://github.com/huggingface/datasets/blob/56346791aed417306d054d89bd693d6b7eab17f7/datasets/xnli/xnli.py#L196
Since, community datasets in Tensorflow Datasets also use HF datasets, this causes a Tuple key error while loading HF's `xnli` dataset.
I'm up for sending a fix for this, I think we can simply use `file_idx + "_" + row_idx` as a unique key instead of a tuple. | {
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https://api.github.com/repos/huggingface/datasets/issues/2226 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2226/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2226/comments | https://api.github.com/repos/huggingface/datasets/issues/2226/events | https://github.com/huggingface/datasets/issues/2226 | 859,720,302 | MDU6SXNzdWU4NTk3MjAzMDI= | 2,226 | Batched map fails when removing all columns | {
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"I found the problem. I called `set_format` on some columns before. This makes it crash. Here is a complete example to reproduce:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nsst = load_dataset(\"sst\")\r\nsst.set_format(\"torch\", columns=[\"label\"], output_all_columns=True)\r\nds = sst[\"train\"]\r\n... | 2021-04-16T11:17:01 | 2022-10-05T17:32:15 | 2022-10-05T17:32:15 | NONE | null | null | null | null | Hi @lhoestq ,
I'm hijacking this issue, because I'm currently trying to do the approach you recommend:
> Currently the optimal setup for single-column computations is probably to do something like
>
> ```python
> result = dataset.map(f, input_columns="my_col", remove_columns=dataset.column_names)
> ```
Here is my code: (see edit, in which I added a simplified version
```
This is the error:
```bash
pyarrow.lib.ArrowInvalid: Column 1 named tokens expected length 8964 but got length 1000
```
I wonder why this error occurs, when I delete every column? Can you give me a hint?
### Edit:
I preprocessed my dataset before (using map with the features argument) and saved it to disk. May this be part of the error? I can iterate over the
complete dataset and print every sample before calling map. There seems to be no other problem with the dataset.
I tried to simplify the code that crashes:
```python
# works
log.debug(dataset.column_names)
log.debug(dataset)
for i, sample in enumerate(dataset):
log.debug(i, sample)
# crashes
counted_dataset = dataset.map(
lambda x: {"a": list(range(20))},
input_columns=column,
remove_columns=dataset.column_names,
load_from_cache_file=False,
num_proc=num_workers,
batched=True,
)
```
```
pyarrow.lib.ArrowInvalid: Column 1 named tokens expected length 20 but got length 1000
```
Edit2:
May this be a problem with a schema I set when preprocessing the dataset before? I tried to add the `features` argument to the function and then I get a new error:
```python
# crashes
counted_dataset = dataset.map(
lambda x: {"a": list(range(20))},
input_columns=column,
remove_columns=dataset.column_names,
load_from_cache_file=False,
num_proc=num_workers,
batched=True,
features=datasets.Features(
{
"a": datasets.Sequence(datasets.Value("int32"))
}
)
)
```
```
File "env/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1704, in _map_single
writer.write_batch(batch)
File "env/lib/python3.8/site-packages/datasets/arrow_writer.py", line 312, in write_batch
col_type = schema.field(col).type if schema is not None else None
File "pyarrow/types.pxi", line 1341, in pyarrow.lib.Schema.field
KeyError: 'Column tokens does not exist in schema'
```
_Originally posted by @villmow in https://github.com/huggingface/datasets/issues/2193#issuecomment-820230874_ | {
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https://api.github.com/repos/huggingface/datasets/issues/2224 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2224/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2224/comments | https://api.github.com/repos/huggingface/datasets/issues/2224/events | https://github.com/huggingface/datasets/issues/2224 | 857,983,361 | MDU6SXNzdWU4NTc5ODMzNjE= | 2,224 | Raise error if Windows max path length is not disabled | {
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} | [] | open | false | null | [] | [] | 2021-04-14T14:57:20 | 2021-04-14T14:59:13 | null | MEMBER | null | null | null | null | On startup, raise an error if Windows max path length is not disabled; ask the user to disable it.
Linked to discussion in #2220. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/2218 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2218/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2218/comments | https://api.github.com/repos/huggingface/datasets/issues/2218/events | https://github.com/huggingface/datasets/issues/2218 | 857,238,435 | MDU6SXNzdWU4NTcyMzg0MzU= | 2,218 | Duplicates in the LAMA dataset | {
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"Hi,\r\n\r\ncurrently the datasets API doesn't have a dedicated function to remove duplicate rows, but since the LAMA dataset is not too big (it fits in RAM), we can leverage pandas to help us remove duplicates:\r\n```python\r\n>>> from datasets import load_dataset, Dataset\r\n>>> dataset = load_dataset('lama', spl... | 2021-04-13T18:59:49 | 2021-04-14T21:42:27 | null | NONE | null | null | null | null | I observed duplicates in the LAMA probing dataset, see a minimal code below.
```
>>> import datasets
>>> dataset = datasets.load_dataset('lama')
No config specified, defaulting to: lama/trex
Reusing dataset lama (/home/anam/.cache/huggingface/datasets/lama/trex/1.1.0/97deffae13eca0a18e77dfb3960bb31741e973586f5c1fe1ec0d6b5eece7bddc)
>>> train_dataset = dataset['train']
>>> train_dataset[0]
{'description': 'language or languages a person has learned from early childhood', 'label': 'native language', 'masked_sentence': 'Louis Jules Trochu ([lwi ʒyl tʁɔʃy]; 12 March 1815 – 7 October 1896) was a [MASK] military leader and politician.', 'obj_label': 'French', 'obj_surface': 'French', 'obj_uri': 'Q150', 'predicate_id': 'P103', 'sub_label': 'Louis Jules Trochu', 'sub_surface': 'Louis Jules Trochu', 'sub_uri': 'Q441235', 'template': 'The native language of [X] is [Y] .', 'template_negated': '[X] is not owned by [Y] .', 'type': 'N-1', 'uuid': '40b2ed1c-0961-482e-844e-32596b6117c8'}
>>> train_dataset[1]
{'description': 'language or languages a person has learned from early childhood', 'label': 'native language', 'masked_sentence': 'Louis Jules Trochu ([lwi ʒyl tʁɔʃy]; 12 March 1815 – 7 October 1896) was a [MASK] military leader and politician.', 'obj_label': 'French', 'obj_surface': 'French', 'obj_uri': 'Q150', 'predicate_id': 'P103', 'sub_label': 'Louis Jules Trochu', 'sub_surface': 'Louis Jules Trochu', 'sub_uri': 'Q441235', 'template': 'The native language of [X] is [Y] .', 'template_negated': '[X] is not owned by [Y] .', 'type': 'N-1', 'uuid': '40b2ed1c-0961-482e-844e-32596b6117c8'}
```
I checked the original data available at https://dl.fbaipublicfiles.com/LAMA/data.zip. This particular duplicated comes from:
```
{"uuid": "40b2ed1c-0961-482e-844e-32596b6117c8", "obj_uri": "Q150", "obj_label": "French", "sub_uri": "Q441235", "sub_label": "Louis Jules Trochu", "predicate_id": "P103", "evidences": [{"sub_surface": "Louis Jules Trochu", "obj_surface": "French", "masked_sentence": "Louis Jules Trochu ([lwi \u0292yl t\u0281\u0254\u0283y]; 12 March 1815 \u2013 7 October 1896) was a [MASK] military leader and politician."}, {"sub_surface": "Louis Jules Trochu", "obj_surface": "French", "masked_sentence": "Louis Jules Trochu ([lwi \u0292yl t\u0281\u0254\u0283y]; 12 March 1815 \u2013 7 October 1896) was a [MASK] military leader and politician."}]}
```
What is the best way to deal with these duplicates if I want to use `datasets` to probe with LAMA? | null | {
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https://api.github.com/repos/huggingface/datasets/issues/2214 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2214/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2214/comments | https://api.github.com/repos/huggingface/datasets/issues/2214/events | https://github.com/huggingface/datasets/issues/2214 | 856,333,657 | MDU6SXNzdWU4NTYzMzM2NTc= | 2,214 | load_metric error: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings' | {
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"Hi @nsaphra, thanks for reporting.\r\n\r\nThis issue was fixed in `datasets` version 1.3.0. Could you please update `datasets` and tell me if the problem persists?\r\n```shell\r\npip install -U datasets\r\n```",
"There might be a bug in the conda version of `datasets` 1.2.1 where the datasets/metric scripts are ... | 2021-04-12T20:26:01 | 2021-04-23T15:20:02 | 2021-04-23T15:20:02 | NONE | null | null | null | null | I'm having the same problem as [Notebooks issue 10](https://github.com/huggingface/notebooks/issues/10) on datasets 1.2.1, and it seems to be an issue with the datasets package.
```python
>>> from datasets import load_metric
>>> metric = load_metric("glue", "sst2")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/ext3/miniconda3/lib/python3.8/site-packages/datasets-1.2.1-py3.8.egg/datasets/load.py", line 502, in load_metric
File "/ext3/miniconda3/lib/python3.8/site-packages/datasets-1.2.1-py3.8.egg/datasets/load.py", line 66, in import_main_class
File "/ext3/miniconda3/lib/python3.8/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1014, in _gcd_import
File "<frozen importlib._bootstrap>", line 991, in _find_and_load
File "<frozen importlib._bootstrap>", line 975, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 671, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 783, in exec_module
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "/home/ns4008/.cache/huggingface/modules/datasets_modules/metrics/glue/e4606ab9804a36bcd5a9cebb2cb65bb14b6ac78ee9e6d5981fa679a495dd55de/glue.py", line 105, in <module>
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
AttributeError: module 'datasets.utils.file_utils' has no attribute 'add_start_docstrings'
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2212 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2212/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2212/comments | https://api.github.com/repos/huggingface/datasets/issues/2212/events | https://github.com/huggingface/datasets/issues/2212 | 855,999,133 | MDU6SXNzdWU4NTU5OTkxMzM= | 2,212 | Can't reach "https://storage.googleapis.com/illuin/fquad/train.json.zip" when trying to load fquad dataset | {
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"Hi ! Apparently the data are not available from this url anymore. We'll replace it with the new url when it's available",
"I saw this on their website when we request to download the dataset:\r\n\r\n\r\... | 2021-04-12T13:49:56 | 2023-10-03T16:09:19 | 2023-10-03T16:09:18 | NONE | null | null | null | null | I'm trying to load the [fquad dataset](https://huggingface.co/datasets/fquad) by running:
```Python
fquad = load_dataset("fquad")
```
which produces the following error:
```
Using custom data configuration default
Downloading and preparing dataset fquad/default (download: 3.14 MiB, generated: 6.62 MiB, post-processed: Unknown size, total: 9.76 MiB) to /root/.cache/huggingface/datasets/fquad/default/0.1.0/778dc2c85813d05ddd0c17087294d5f8f24820752340958070876b677af9f061...
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
<ipython-input-48-a2721797e23b> in <module>()
----> 1 fquad = load_dataset("fquad")
11 frames
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)
614 raise FileNotFoundError("Couldn't find file at {}".format(url))
615 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
--> 616 raise ConnectionError("Couldn't reach {}".format(url))
617
618 # Try a second time
ConnectionError: Couldn't reach https://storage.googleapis.com/illuin/fquad/train.json.zip
```
Does anyone know why that is and how to fix it? | {
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"Hi,\r\n\r\nI've already opened a PR with the fix. If you are in a hurry, just build the project from source and run:\r\n```bash\r\ndatasets-cli test datasets/lc_quad --save_infos --all_configs --ignore_verifications\r\n```\r\n\r\n",
"Ah sorry, I tried searching but couldn't find any related PR. \r\n\r\nThank you... | 2021-04-12T13:38:58 | 2021-04-14T13:42:25 | 2021-04-14T13:42:25 | NONE | null | null | null | null | I'm having issues loading the [lc_quad](https://huggingface.co/datasets/fquad) dataset by running:
```Python
lc_quad = load_dataset("lc_quad")
```
which is giving me the following error:
```
Using custom data configuration default
Downloading and preparing dataset lc_quad/default (download: 3.69 MiB, generated: 19.77 MiB, post-processed: Unknown size, total: 23.46 MiB) to /root/.cache/huggingface/datasets/lc_quad/default/2.0.0/5a98fe174603f5dec6df07edf1c2b4d2317210d2ad61f5a393839bca4d64e5a7...
---------------------------------------------------------------------------
NonMatchingChecksumError Traceback (most recent call last)
<ipython-input-42-404ace83f73c> in <module>()
----> 1 lc_quad = load_dataset("lc_quad")
3 frames
/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)
37 if len(bad_urls) > 0:
38 error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 39 raise NonMatchingChecksumError(error_msg + str(bad_urls))
40 logger.info("All the checksums matched successfully" + for_verification_name)
41
NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://github.com/AskNowQA/LC-QuAD2.0/archive/master.zip']
```
Does anyone know why this could be and how I fix it? | {
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https://api.github.com/repos/huggingface/datasets/issues/2210 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2210/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2210/comments | https://api.github.com/repos/huggingface/datasets/issues/2210/events | https://github.com/huggingface/datasets/issues/2210 | 855,709,400 | MDU6SXNzdWU4NTU3MDk0MDA= | 2,210 | dataloading slow when using HUGE dataset | {
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"Hi ! Yes this is an issue with `datasets<=1.5.0`\r\nThis issue has been fixed by #2122 , we'll do a new release soon :)\r\nFor now you can test it on the `master` branch.",
"Hi, thank you for your answer. I did not realize that my issue stems from the same problem. "
] | 2021-04-12T08:33:02 | 2021-04-13T02:03:05 | 2021-04-13T02:03:05 | NONE | null | null | null | null | Hi,
When I use datasets with 600GB data, the dataloading speed increases significantly.
I am experimenting with two datasets, and one is about 60GB and the other 600GB.
Simply speaking, my code uses `datasets.set_format("torch")` function and let pytorch-lightning handle ddp training.
When looking at the pytorch-lightning supported profile of two different runs, I see that fetching a batch(`get_train_batch`) consumes an unreasonable amount of time when data is large. What could be the cause?
* 60GB data
```
Action | Mean duration (s) |Num calls | Total time (s) | Percentage % |
------------------------------------------------------------------------------------------------------------------------------------
Total | - |_ | 200.33 | 100 % |
------------------------------------------------------------------------------------------------------------------------------------
run_training_epoch | 71.994 |1 | 71.994 | 35.937 |
run_training_batch | 0.64373 |100 | 64.373 | 32.133 |
optimizer_step_and_closure_0 | 0.64322 |100 | 64.322 | 32.108 |
training_step_and_backward | 0.61004 |100 | 61.004 | 30.452 |
model_backward | 0.37552 |100 | 37.552 | 18.745 |
model_forward | 0.22813 |100 | 22.813 | 11.387 |
training_step | 0.22759 |100 | 22.759 | 11.361 |
get_train_batch | 0.066385 |100 | 6.6385 | 3.3138 |
```
* 600GB data
```
Action | Mean duration (s) |Num calls | Total time (s) | Percentage % |
------------------------------------------------------------------------------------------------------------------------------------
Total | - |_ | 3285.6 | 100 % |
------------------------------------------------------------------------------------------------------------------------------------
run_training_epoch | 1397.9 |1 | 1397.9 | 42.546 |
run_training_batch | 7.2596 |100 | 725.96 | 22.095 |
optimizer_step_and_closure_0 | 7.2589 |100 | 725.89 | 22.093 |
training_step_and_backward | 7.223 |100 | 722.3 | 21.984 |
model_backward | 6.9662 |100 | 696.62 | 21.202 |
get_train_batch | 6.322 |100 | 632.2 | 19.241 |
model_forward | 0.24902 |100 | 24.902 | 0.75789 |
training_step | 0.2485 |100 | 24.85 | 0.75633 |
```
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https://api.github.com/repos/huggingface/datasets/issues/2207 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2207/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2207/comments | https://api.github.com/repos/huggingface/datasets/issues/2207/events | https://github.com/huggingface/datasets/issues/2207 | 855,267,383 | MDU6SXNzdWU4NTUyNjczODM= | 2,207 | making labels consistent across the datasets | {
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"Hi ! The ClassLabel feature type encodes the labels as integers.\r\nThe integer corresponds to the index of the label name in the `names` list of the ClassLabel.\r\nHere that means that the labels are 'entailment' (0), 'neutral' (1), 'contradiction' (2).\r\n\r\nYou can get the label names back by using `a.features... | 2021-04-11T10:03:56 | 2022-06-01T16:23:08 | 2022-06-01T16:21:10 | NONE | null | null | null | null | Hi
For accessing the labels one can type
```
>>> a.features['label']
ClassLabel(num_classes=3, names=['entailment', 'neutral', 'contradiction'], names_file=None, id=None)
```
The labels however are not consistent with the actual labels sometimes, for instance in case of XNLI, the actual labels are 0,1,2, but if one try to access as above they are entailment, neutral,contradiction,
it would be great to have the labels consistent.
thanks
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https://api.github.com/repos/huggingface/datasets/issues/2206 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2206/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2206/comments | https://api.github.com/repos/huggingface/datasets/issues/2206/events | https://github.com/huggingface/datasets/issues/2206 | 855,252,415 | MDU6SXNzdWU4NTUyNTI0MTU= | 2,206 | Got pyarrow error when loading a dataset while adding special tokens into the tokenizer | {
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"Hi,\r\n\r\nthe output of the tokenizers is treated specially in the lib to optimize the dataset size (see the code [here](https://github.com/huggingface/datasets/blob/master/src/datasets/arrow_writer.py#L138-L141)). It looks like that one of the values in a dictionary returned by the tokenizer is out of the assume... | 2021-04-11T08:40:09 | 2021-11-10T12:18:30 | 2021-11-10T12:04:28 | NONE | null | null | null | null | I added five more special tokens into the GPT2 tokenizer. But after that, when I try to pre-process the data using my previous code, I got an error shown below:
Traceback (most recent call last):
File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1687, in _map_single
writer.write(example)
File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_writer.py", line 296, in write
self.write_on_file()
File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_writer.py", line 270, in write_on_file
pa_array = pa.array(typed_sequence)
File "pyarrow/array.pxi", line 222, in pyarrow.lib.array
File "pyarrow/array.pxi", line 110, in pyarrow.lib._handle_arrow_array_protocol
File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/datasets/arrow_writer.py", line 108, in __arrow_array__
out = out.cast(pa.list_(self.optimized_int_type))
File "pyarrow/array.pxi", line 810, in pyarrow.lib.Array.cast
File "/home/xuyan/anaconda3/envs/convqa/lib/python3.7/site-packages/pyarrow/compute.py", line 281, in cast
return call_function("cast", [arr], options)
File "pyarrow/_compute.pyx", line 465, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 294, in pyarrow._compute.Function.call
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Integer value 50259 not in range: -128 to 127
Do you have any idea about it? | {
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"Hi ! This might be related to #2153 \r\n\r\nYou're right the ArrowWriter should be initialized with `features=self.info.features` ! Good catch\r\nI'm opening a PR to fix this and also to figure out how it was not caught in the tests\r\n\r\nEDIT: opened #2201",
"> Hi ! This might be related to #2153\r\n> \r\n> Yo... | 2021-04-09T11:47:13 | 2021-06-04T10:37:35 | 2021-06-04T10:37:35 | NONE | null | null | null | null | Hi, here is my issue:
I initialized a Csv datasetbuilder with specific features:
```
def get_dataset_features(data_args):
features = {}
if data_args.text_features:
features.update({text_feature: hf_features.Value("string") for text_feature in data_args.text_features.strip().split(",")})
if data_args.num_features:
features.update({text_feature: hf_features.Value("float32") for text_feature in data_args.num_features.strip().split(",")})
if data_args.label_classes:
features["label"] = hf_features.ClassLabel(names=data_args.label_classes.strip().split(","))
else:
features["label"] = hf_features.Value("float32")
return hf_features.Features(features)
datasets = load_dataset(extension,
data_files=data_files,
sep=data_args.delimiter,
header=data_args.header,
column_names=data_args.column_names.split(",") if data_args.column_names else None,
features=get_dataset_features(data_args=data_args))
```
The `features` is printout as below before `builder_instance.as_dataset` is called:
```
{'label': ClassLabel(num_classes=2, names=['unacceptable', 'acceptable'], names_file=None, id=None), 'notated': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'src_code': Value(dtype='string', id=None)}
````
But after the `builder_instance.as_dataset` is called for Csv dataset builder, the `features` is changed to:
```
{'label': Value(dtype='int64', id=None), 'notated': Value(dtype='string', id=None), 'sentence': Value(dtype='string', id=None), 'src_code': Value(dtype='string', id=None)}
```
After digged into the code, I releazed that in `ArrowBasedBuilder._prepare_split`, the DatasetBuilder's info's features will be overwrited by `ArrowWriter`'s `_features`.
But `ArrowWriter` is initailized without passing `features`.
So my concern is:
It's this overwrite must be done, or, should it be an option to pass features in `_prepare_split` function? | {
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https://api.github.com/repos/huggingface/datasets/issues/2196 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2196/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2196/comments | https://api.github.com/repos/huggingface/datasets/issues/2196/events | https://github.com/huggingface/datasets/issues/2196 | 854,126,114 | MDU6SXNzdWU4NTQxMjYxMTQ= | 2,196 | `load_dataset` caches two arrow files? | {
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"Hi ! Files that starts with `cache-*` are cached computation files, i.e. they are the cached results of map/filter/cast/etc. operations. For example if you used `map` on your dataset to transform it, then the resulting dataset is going to be stored and cached in a `cache-*` file. These files are used to avoid havi... | 2021-04-09T03:49:19 | 2021-04-12T05:25:29 | 2021-04-12T05:25:29 | NONE | null | null | null | null | Hi,
I am using datasets to load large json file of 587G.
I checked the cached folder and found that there are two arrow files created:
* `cache-ed205e500a7dc44c.arrow` - 355G
* `json-train.arrow` - 582G
Why is the first file created?
If I delete it, would I still be able to `load_from_disk`? | {
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https://api.github.com/repos/huggingface/datasets/issues/2195 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2195/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2195/comments | https://api.github.com/repos/huggingface/datasets/issues/2195/events | https://github.com/huggingface/datasets/issues/2195 | 854,070,194 | MDU6SXNzdWU4NTQwNzAxOTQ= | 2,195 | KeyError: '_indices_files' in `arrow_dataset.py` | {
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"Thanks for reporting @samsontmr.\r\n\r\nIt seems a backward compatibility issue...",
"Thanks @samsontmr this should be fixed on master now\r\n\r\nFeel free to reopen if you're still having issues"
] | 2021-04-09T01:37:12 | 2021-04-09T09:55:09 | 2021-04-09T09:54:39 | NONE | null | null | null | null | After pulling the latest master, I'm getting a crash when `load_from_disk` tries to load my local dataset.
Trace:
```
Traceback (most recent call last):
File "load_data.py", line 11, in <module>
dataset = load_from_disk(SRC)
File "/opt/conda/envs/py38/lib/python3.8/site-packages/datasets/load.py", line 784, in load_from_disk
return DatasetDict.load_from_disk(dataset_path, fs, keep_in_memory=keep_in_memory)
File "/opt/conda/envs/py38/lib/python3.8/site-packages/datasets/dataset_dict.py", line 692, in load_from_disk
dataset_dict[k] = Dataset.load_from_disk(dataset_dict_split_path, fs, keep_in_memory=keep_in_memory)
File "/opt/conda/envs/py38/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 634, in load_from_disk
if state["_indices_files"]:
KeyError: '_indices_files'
```
I believe this is the line causing the error since there may not be a `_indices_files` key in the older versions:
https://github.com/huggingface/datasets/blob/b70141e3c5149430951773aaa0155555c5fb3e76/src/datasets/arrow_dataset.py#L634
May I suggest using `state.get()` instead of directly indexing the dictionary?
@lhoestq | {
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https://api.github.com/repos/huggingface/datasets/issues/2194 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2194/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2194/comments | https://api.github.com/repos/huggingface/datasets/issues/2194/events | https://github.com/huggingface/datasets/issues/2194 | 853,909,452 | MDU6SXNzdWU4NTM5MDk0NTI= | 2,194 | py3.7: TypeError: can't pickle _LazyModule objects | {
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"\r\nThis wasn't a `datasets` problem, but `transformers`' and it was solved here https://github.com/huggingface/transformers/pull/11168\r\n"
] | 2021-04-08T21:02:48 | 2021-04-09T16:56:50 | 2021-04-09T01:52:57 | CONTRIBUTOR | null | null | null | null | While this works fine with py3.8, under py3.7, with a totally new conda env and transformers install:
```
git clone https://github.com/huggingface/transformers
cd transformers
pip install -e .[testing]
export BS=1; rm -rf /tmp/test-clm; PYTHONPATH=src USE_TF=0 CUDA_VISIBLE_DEVICES=0 python \
examples/language-modeling/run_clm.py --model_name_or_path distilgpt2 --dataset_name wikitext \
--dataset_config_name wikitext-2-raw-v1 --do_train --max_train_samples 1 \
--per_device_train_batch_size $BS --output_dir /tmp/test-clm --block_size 128 --logging_steps 1 \
--fp16
```
```
Traceback (most recent call last):
File "examples/language-modeling/run_clm.py", line 453, in <module>
main()
File "examples/language-modeling/run_clm.py", line 336, in main
load_from_cache_file=not data_args.overwrite_cache,
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/dataset_dict.py", line 303, in map
for k, dataset in self.items()
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/dataset_dict.py", line 303, in <dictcomp>
for k, dataset in self.items()
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1259, in map
update_data=update_data,
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 157, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/fingerprint.py", line 158, in wrapper
self._fingerprint, transform, kwargs_for_fingerprint
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/fingerprint.py", line 105, in update_fingerprint
hasher.update(transform_args[key])
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/fingerprint.py", line 57, in update
self.m.update(self.hash(value).encode("utf-8"))
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/fingerprint.py", line 53, in hash
return cls.hash_default(value)
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/fingerprint.py", line 46, in hash_default
return cls.hash_bytes(dumps(value))
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 389, in dumps
dump(obj, file)
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 361, in dump
Pickler(file, recurse=True).dump(obj)
File "/home/stas/anaconda3/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump
StockPickler.dump(self, obj)
File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 437, in dump
self.save(obj)
File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/home/stas/anaconda3/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 556, in save_function
obj=obj,
File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 638, in save_reduce
save(args)
File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 789, in save_tuple
save(element)
File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/home/stas/anaconda3/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 859, in save_dict
self._batch_setitems(obj.items())
File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 885, in _batch_setitems
save(v)
File "/home/stas/anaconda3/lib/python3.7/pickle.py", line 524, in save
rv = reduce(self.proto)
TypeError: can't pickle _LazyModule objects
```
```
$ python --version
Python 3.7.4
$ python -m torch.utils.collect_env
Collecting environment information...
PyTorch version: 1.8.0.dev20210110+cu110
Is debug build: False
CUDA used to build PyTorch: 11.0
ROCM used to build PyTorch: N/A
OS: Ubuntu 20.04.2 LTS (x86_64)
GCC version: (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0
Clang version: 10.0.0-4ubuntu1
CMake version: version 3.16.3
```
Thanks. | {
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] | closed | false | null | [] | [
"Hi ! Yes we are working on making `filter` significantly faster. You can look at related PRs here: #2060 #2178 \r\n\r\nI think you can expect to have the fast version of `filter` available next week.\r\n\r\nWe'll make it only select one column, and we'll also make the overall filtering operation way faster by avoi... | 2021-04-08T18:16:14 | 2021-04-26T16:13:59 | 2021-04-26T16:13:59 | CONTRIBUTOR | null | null | null | null | I'm currently using the `wikipedia` dataset— I'm tokenizing the articles with the `tokenizers` library using `map()` and also adding a new `num_tokens` column to the dataset as part of that map operation.
I want to be able to _filter_ the dataset based on this `num_tokens` column, but even when I specify `input_columns=['num_tokens']`, it seems that the entirety of each row is loaded into memory, which makes the operation take much longer than it should. Indeed, `filter` currently just calls `map`, and I found that in `_map_single` on lines 1690-1704 of `arrow_dataset.py`, the method is just grabbing slices of _all the rows_ of the dataset and then passing only the specified columns to the map function. It seems that, when the user passes a value for `input_columns`, the `map` function should create a temporary pyarrow table by selecting just those columns, and then get slices from that table. Or something like that— I'm not very familiar with the pyarrow API.
I know that in the meantime I can sort of get around this by simply only returning the rows that match my filter criterion from the tokenizing function I pass to `map()`, but I actually _also_ want to map on just the `num_tokens` column in order to compute batches with a roughly uniform number of tokens per batch. I would also ideally like to be able to change my minimum and maximum article lengths without having to re-tokenize the entire dataset.
PS: This is definitely not a "dataset request." I'm realizing that I don't actually know how to remove labels from my own issues on other people's repos, if that is even possible. | {
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https://api.github.com/repos/huggingface/datasets/issues/2190 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2190/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2190/comments | https://api.github.com/repos/huggingface/datasets/issues/2190/events | https://github.com/huggingface/datasets/issues/2190 | 853,181,564 | MDU6SXNzdWU4NTMxODE1NjQ= | 2,190 | News_commentary Dataset Translation Pairs are of Incorrect Language Specified Pairs | {
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"Hi @anassalamah,\r\n\r\nCould you please try with this:\r\n```python\r\ntrain_ds = load_dataset(\"news_commentary\", lang1=\"ar\", lang2=\"en\", split='train[:98%]')\r\nval_ds = load_dataset(\"news_commentary\", lang1=\"ar\", lang2=\"en\", split='train[98%:]')\r\n```",
"Hello @albertvillanova, \r\n\r\nThanks for... | 2021-04-08T07:53:43 | 2021-05-24T10:03:55 | 2021-05-24T10:03:55 | NONE | null | null | null | null | I used load_dataset to load the news_commentary dataset for "ar-en" translation pairs but found translations from Arabic to Hindi.
```
train_ds = load_dataset("news_commentary", "ar-en", split='train[:98%]')
val_ds = load_dataset("news_commentary", "ar-en", split='train[98%:]')
# filtering out examples that are not ar-en translations but ar-hi
val_ds = val_ds.filter(lambda example, indice: indice not in chain(range(1312,1327) ,range(1384,1399), range(1030,1042)), with_indices=True)
```
* I'm fairly new to using datasets so I might be doing something wrong | {
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https://api.github.com/repos/huggingface/datasets/issues/2189 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2189/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2189/comments | https://api.github.com/repos/huggingface/datasets/issues/2189/events | https://github.com/huggingface/datasets/issues/2189 | 853,052,891 | MDU6SXNzdWU4NTMwNTI4OTE= | 2,189 | save_to_disk doesn't work when we use concatenate_datasets function before creating the final dataset_object. | {
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"Hi ! We refactored save_to_disk in #2025 so this doesn't happen.\r\nFeel free to try it on master for now\r\nWe'll do a new release soon"
] | 2021-04-08T04:42:53 | 2022-06-01T16:32:15 | 2022-06-01T16:32:15 | NONE | null | null | null | null | As you can see, it saves the entire dataset.
@lhoestq
You can check by going through the following example,
```
from datasets import load_from_disk,concatenate_datasets
loaded_data=load_from_disk('/home/gsir059/HNSW-ori/my_knowledge_dataset')
n=20
kb_list=[loaded_data.shard(n, i, contiguous=True) for i in range(n)]
final_dataset=concatenate_datasets([kb_list[1],kb_list[2]])
final_dataset.save_to_disk('/home/gsir059/haha/k.arrow')
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2188 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2188/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2188/comments | https://api.github.com/repos/huggingface/datasets/issues/2188/events | https://github.com/huggingface/datasets/issues/2188 | 853,044,166 | MDU6SXNzdWU4NTMwNDQxNjY= | 2,188 | Duplicate data in Timit dataset | {
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"Hi ! Thanks for reporting\r\nIf I recall correctly this has been recently fixed #1995\r\nCan you try to upgrade your local version of `datasets` ?\r\n```\r\npip install --upgrade datasets\r\n```",
"Hi Ihoestq,\r\n\r\nThank you. It works after upgrading the datasets\r\n"
] | 2021-04-08T04:21:54 | 2021-04-08T12:13:19 | 2021-04-08T12:13:19 | NONE | null | null | null | null | I ran a simple code to list all texts in Timit dataset and the texts were all the same.
Is this dataset corrupted?
**Code:**
timit = load_dataset("timit_asr")
print(*timit['train']['text'], sep='\n')
**Result:**
Would such an act of refusal be useful?
Would such an act of refusal be useful?
Would such an act of refusal be useful?
Would such an act of refusal be useful?
...
...
Would such an act of refusal be useful? | {
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https://api.github.com/repos/huggingface/datasets/issues/2187 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2187/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2187/comments | https://api.github.com/repos/huggingface/datasets/issues/2187/events | https://github.com/huggingface/datasets/issues/2187 | 852,939,736 | MDU6SXNzdWU4NTI5Mzk3MzY= | 2,187 | Question (potential issue?) related to datasets caching | {
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"An educated guess: does this refer to the fact that depending on the custom column names in the dataset files (csv in this case), there is a dataset loader being created? and this dataset loader - using the \"custom data configuration\" is used among all jobs running using this particular csv files? (thinking out ... | 2021-04-08T00:16:28 | 2023-01-03T18:30:38 | null | NONE | null | null | null | null | I thought I had disabled datasets caching in my code, as follows:
```
from datasets import set_caching_enabled
...
def main():
# disable caching in datasets
set_caching_enabled(False)
```
However, in my log files I see messages like the following:
```
04/07/2021 18:34:42 - WARNING - datasets.builder - Using custom data configuration default-888a87931cbc5877
04/07/2021 18:34:42 - WARNING - datasets.builder - Reusing dataset csv (xxxx/cache-transformers/datasets/csv/default-888a87931cbc5877/0.0.0/965b6429be0fc05f975b608ce64e1fa941cc8fb4f30629b523d2390f3c0e1a93
```
Can you please let me know what this reusing dataset csv means? I wouldn't expect any reusing with the datasets caching disabled. Thank you! | null | {
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"Hi, one workaround would be to save the mapped(tokenized in your case) file using `save_to_disk`, and having each process load this file using `load_from_disk`. This is what I am doing, and in this case, I turn off the ability to automatically load from the cache.\r\n\r\nAlso, multiprocessing the map function seem... | 2021-04-07T18:22:14 | 2021-10-23T07:11:15 | 2021-04-09T15:38:31 | CONTRIBUTOR | null | null | null | null | Hi,
I have a question regarding distributed training and the `.map` call on a dataset.
I have a local dataset "my_custom_dataset" that I am loading with `datasets = load_from_disk(dataset_path=my_path)`.
`dataset` is then tokenized:
```python
datasets = load_from_disk(dataset_path=my_path)
[...]
def tokenize_function(examples):
return tokenizer(examples[text_column_name])
logger.info("Mapping dataset to tokenized dataset.")
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=True,
)
```
I am using 31 workers (`preprocessing_num_workers=31`) and thus it creates 31 `cache*.arrow` files in `my_path/train` (there is only a train split).
When I relaunch the script, the map is tokenization is skipped in favor of loading the 31 previously cached files, and that's perfect.
Everything so far was done by launching a **single process script**.
I now launch the same training script in **distributed mode** (`pytorch -m torch.distributed.launch --nproc_per_node 2`). However, once it reaches the map call, it re-does the tokenization... instead of loading the 31 cached files.
I tried adding the `cache_file_name` argument: `cache_file_name={"train": my_path/one_of_the_arrow_file}`, but I can't give the 31 cached files, so it probably isn't the right way to do it.
**My question: what is the best way to load cached files if they were pre-processed and dumped in multiple arrow files?** It seems automatically handled for single processes but fails on distributed training.
- I am following the same structure as the examples of transformers (more specifically [run_clm.py](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_clm.py) in my case)
- I am using 1.5.0 version of datasets if that matters. | {
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https://api.github.com/repos/huggingface/datasets/issues/2181 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2181/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2181/comments | https://api.github.com/repos/huggingface/datasets/issues/2181/events | https://github.com/huggingface/datasets/issues/2181 | 852,261,607 | MDU6SXNzdWU4NTIyNjE2MDc= | 2,181 | Error when loading a HUGE json file (pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries) | {
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"Hi ! Can you try to increase the block size ? For example\r\n```python\r\nblock_size_10MB = 10<<20\r\nload_dataset(\"json\", ..., block_size=block_size_10MB)\r\n```\r\nThe block size corresponds to how much bytes to process at a time from the input stream.\r\nThis will determine multi-threading granularity as well... | 2021-04-07T10:26:46 | 2021-04-12T07:15:55 | 2021-04-12T07:15:55 | NONE | null | null | null | null | Hi, thanks for the great library. I have used the brilliant library for a couple of small projects, and now using it for a fairly big project.
When loading a huge json file of 500GB, pyarrow complains as follows:
```
Traceback (most recent call last):
File "/home/user/.pyenv/versions/3.7.9/lib/python3.7/site-packages/datasets/builder.py", line 531, in incomplete_dir
yield tmp_dir
File "/home/user/.pyenv/versions/3.7.9/lib/python3.7/site-packages/datasets/builder.py", line 573, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/user/.pyenv/versions/3.7.9/lib/python3.7/site-packages/datasets/builder.py", line 650, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/home/user/.pyenv/versions/3.7.9/lib/python3.7/site-packages/datasets/builder.py", line 1027, in _prepare_split
for key, table in utils.tqdm(generator, unit=" tables", leave=False, disable=not_verbose):
File "/home/user/.pyenv/versions/3.7.9/lib/python3.7/site-packages/tqdm/std.py", line 1133, in __iter__
for obj in iterable:
File "/app/.cache/huggingface/modules/datasets_modules/datasets/json/9498524fd296a6cca99c66d6c5be507d1c0991f5a814e535b507f4a66096a641/json.py", line 83, in _generate_tables
parse_options=self.config.pa_parse_options,
File "pyarrow/_json.pyx", line 247, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 122, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: straddling object straddles two block boundaries (try to increase block size?)
```
When using only a small portion of the sample file, say first 100 lines, it works perfectly well..
I see that it is the error from pyarrow, but could you give me a hint or possible solutions?
#369 describes the same error and #372 claims to have fixed the issue, but I have no clue why I am still getting this one. Thanks in advance! | {
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https://api.github.com/repos/huggingface/datasets/issues/2179 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2179/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2179/comments | https://api.github.com/repos/huggingface/datasets/issues/2179/events | https://github.com/huggingface/datasets/issues/2179 | 852,237,957 | MDU6SXNzdWU4NTIyMzc5NTc= | 2,179 | Load small datasets in-memory instead of using memory map | {
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"following_url": "https://api.github.com/users/albertvillanova/following{/o... | [] | 2021-04-07T09:58:16 | 2021-04-20T10:04:04 | 2021-04-20T10:04:03 | MEMBER | null | null | null | null | Currently all datasets are loaded using memory mapping by default in `load_dataset`.
However this might not be necessary for small datasets. If a dataset is small enough, then it can be loaded in-memory and:
- its memory footprint would be small so it's ok
- in-memory computations/queries would be faster
- the caching on-disk would be disabled, making computations even faster (no I/O bound because of the disk)
- but running the same computation a second time would recompute everything since there would be no cached results on-disk. But this is probably fine since computations would be fast anyway + users should be able to provide a cache filename if needed.
Therefore, maybe the default behavior of `load_dataset` should be to load small datasets in-memory and big datasets using memory mapping. | {
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https://api.github.com/repos/huggingface/datasets/issues/2176 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2176/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2176/comments | https://api.github.com/repos/huggingface/datasets/issues/2176/events | https://github.com/huggingface/datasets/issues/2176 | 851,865,795 | MDU6SXNzdWU4NTE4NjU3OTU= | 2,176 | Converting a Value to a ClassLabel | {
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"Hi @nelson-liu!\r\nHere is what I do to convert a string to class label:\r\n\r\n```python\r\nfrom datasets import load_dataset, features\r\n\r\n\r\ndset = load_dataset(...)\r\ncol_name = \"the string column name\"\r\n\r\nclass_names = dset.unique(col_name)\r\nclass_feature = features.ClassLabel(names=sorted(class... | 2021-04-06T22:54:16 | 2022-06-01T16:31:49 | 2022-06-01T16:31:49 | NONE | null | null | null | null | Hi!
In the docs for `cast`, it's noted that `For non-trivial conversion, e.g. string <-> ClassLabel you should use map() to update the Dataset.`
Would it be possible to have an example that demonstrates such a string <-> ClassLabel conversion using `map`? Thanks! | {
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https://api.github.com/repos/huggingface/datasets/issues/2175 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2175/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2175/comments | https://api.github.com/repos/huggingface/datasets/issues/2175/events | https://github.com/huggingface/datasets/issues/2175 | 851,836,096 | MDU6SXNzdWU4NTE4MzYwOTY= | 2,175 | dataset.search_batch() function outputs all -1 indices sometime. | {
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"Actually, I found the answer [here](https://github.com/facebookresearch/faiss/wiki/FAQ#what-does-it-mean-when-a-search-returns--1-ids). \r\n\r\nSo we have to do some modifications to the code for instances where the index doesn't retrieve any IDs.",
"@lhoestq @patrickvonplaten \r\n\r\nI also found another short... | 2021-04-06T21:50:49 | 2021-04-16T12:21:16 | 2021-04-16T12:21:15 | NONE | null | null | null | null | I am working with RAG and playing around with different faiss indexes. At the moment I use **index = faiss.index_factory(768, "IVF65536_HNSW32,Flat")**.
During the retrieval phase exactly in [this line of retrieval_rag.py](https://github.com/huggingface/transformers/blob/master/src/transformers/models/rag/retrieval_rag.py#L231) an error issue when all retrieved indices are -1. Please refer to the screenshot of a PID worker.
Here, my retrieve batch size is 2 and n_docs is 5. I can solve this by working around np. stack, but I want to ask, why we get an output index of -1. Do you have any idea :) ?
Is this a problem of the index, where the faiss can't find any similar vector?
Is there documentation on the output index being -1?
@lhoestq
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https://api.github.com/repos/huggingface/datasets/issues/2170 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2170/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2170/comments | https://api.github.com/repos/huggingface/datasets/issues/2170/events | https://github.com/huggingface/datasets/issues/2170 | 850,913,228 | MDU6SXNzdWU4NTA5MTMyMjg= | 2,170 | Wikipedia historic dumps are deleted but hf/datasets hardcodes dump date | {
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"It seems that this can be fixed from user's end by including a `date` argument, like this:\r\n\r\n`dataset = datasets.load_dataset('wikipedia', '20200501.en', date='20210420')`\r\n\r\nYou can get available dates from [here](https://dumps.wikimedia.org/enwiki/).\r\n\r\nThis is not a proper fix however as all the fi... | 2021-04-06T03:13:18 | 2021-06-16T01:10:50 | null | NONE | null | null | null | null | Wikimedia does not keep all historical dumps. For example, as of today https://dumps.wikimedia.org/kowiki/ only provides
```
20201220/ 02-Feb-2021 01:36 -
20210101/ 21-Feb-2021 01:26 -
20210120/ 02-Mar-2021 01:25 -
20210201/ 21-Mar-2021 01:26 -
20210220/ 02-Apr-2021 01:26 -
20210301/ 03-Mar-2021 08:10 -
20210320/ 21-Mar-2021 18:13 -
20210401/ 03-Apr-2021 10:08 -
latest/ 03-Apr-2021 10:08 -
```
However, the wikipedia dataset provided in the library, only supports the following configs, none of which are applicable anymore when disregarding the cached datasets:
```
ValueError: BuilderConfig 20210401.ko not found. Available: ['20200501.aa', '20200501.ab', '20200501.ace', '20200501.ady', '20200501.af', '20200501.ak', '20200501.als', '20200501.am', '20200501.an', '20200501.ang', '20200501.ar', '20200501.arc', '20200501.arz', '20200501.as', '20200501.ast', '20200501.atj', '20200501.av', '20200501.ay', '20200501.az', '20200501.azb', '20200501.ba', '20200501.bar', '20200501.bat-smg', '20200501.bcl', '20200501.be', '20200501.be-x-old', '20200501.bg', '20200501.bh', '20200501.bi', '20200501.bjn', '20200501.bm', '20200501.bn', '20200501.bo', '20200501.bpy', '20200501.br', '20200501.bs', '20200501.bug', '20200501.bxr', '20200501.ca', '20200501.cbk-zam', '20200501.cdo', '20200501.ce', '20200501.ceb', '20200501.ch', '20200501.cho', '20200501.chr', '20200501.chy', '20200501.ckb', '20200501.co', '20200501.cr', '20200501.crh', '20200501.cs', '20200501.csb', '20200501.cu', '20200501.cv', '20200501.cy', '20200501.da', '20200501.de', '20200501.din', '20200501.diq', '20200501.dsb', '20200501.dty', '20200501.dv', '20200501.dz', '20200501.ee', '20200501.el', '20200501.eml', '20200501.en', '20200501.eo', '20200501.es', '20200501.et', '20200501.eu', '20200501.ext', '20200501.fa', '20200501.ff', '20200501.fi', '20200501.fiu-vro', '20200501.fj', '20200501.fo', '20200501.fr', '20200501.frp', '20200501.frr', '20200501.fur', '20200501.fy', '20200501.ga', '20200501.gag', '20200501.gan', '20200501.gd', '20200501.gl', '20200501.glk', '20200501.gn', '20200501.gom', '20200501.gor', '20200501.got', '20200501.gu', '20200501.gv', '20200501.ha', '20200501.hak', '20200501.haw', '20200501.he', '20200501.hi', '20200501.hif', '20200501.ho', '20200501.hr', '20200501.hsb', '20200501.ht', '20200501.hu', '20200501.hy', '20200501.ia', '20200501.id', '20200501.ie', '20200501.ig', '20200501.ii', '20200501.ik', '20200501.ilo', '20200501.inh', '20200501.io', '20200501.is', '20200501.it', '20200501.iu', '20200501.ja', '20200501.jam', '20200501.jbo', '20200501.jv', '20200501.ka', '20200501.kaa', '20200501.kab', '20200501.kbd', '20200501.kbp', '20200501.kg', '20200501.ki', '20200501.kj', '20200501.kk', '20200501.kl', '20200501.km', '20200501.kn', '20200501.ko', '20200501.koi', '20200501.krc', '20200501.ks', '20200501.ksh', '20200501.ku', '20200501.kv', '20200501.kw', '20200501.ky', '20200501.la', '20200501.lad', '20200501.lb', '20200501.lbe', '20200501.lez', '20200501.lfn', '20200501.lg', '20200501.li', '20200501.lij', '20200501.lmo', '20200501.ln', '20200501.lo', '20200501.lrc', '20200501.lt', '20200501.ltg', '20200501.lv', '20200501.mai', '20200501.map-bms', '20200501.mdf', '20200501.mg', '20200501.mh', '20200501.mhr', '20200501.mi', '20200501.min', '20200501.mk', '20200501.ml', '20200501.mn', '20200501.mr', '20200501.mrj', '20200501.ms', '20200501.mt', '20200501.mus', '20200501.mwl', '20200501.my', '20200501.myv', '20200501.mzn', '20200501.na', '20200501.nah', '20200501.nap', '20200501.nds', '20200501.nds-nl', '20200501.ne', '20200501.new', '20200501.ng', '20200501.nl', '20200501.nn', '20200501.no', '20200501.nov', '20200501.nrm', '20200501.nso', '20200501.nv', '20200501.ny', '20200501.oc', '20200501.olo', '20200501.om', '20200501.or', '20200501.os', '20200501.pa', '20200501.pag', '20200501.pam', '20200501.pap', '20200501.pcd', '20200501.pdc', '20200501.pfl', '20200501.pi', '20200501.pih', '20200501.pl', '20200501.pms', '20200501.pnb', '20200501.pnt', '20200501.ps', '20200501.pt', '20200501.qu', '20200501.rm', '20200501.rmy', '20200501.rn', '20200501.ro', '20200501.roa-rup', '20200501.roa-tara', '20200501.ru', '20200501.rue', '20200501.rw', '20200501.sa', '20200501.sah', '20200501.sat', '20200501.sc', '20200501.scn', '20200501.sco', '20200501.sd', '20200501.se', '20200501.sg', '20200501.sh', '20200501.si', '20200501.simple', '20200501.sk', '20200501.sl', '20200501.sm', '20200501.sn', '20200501.so', '20200501.sq', '20200501.sr', '20200501.srn', '20200501.ss', '20200501.st', '20200501.stq', '20200501.su', '20200501.sv', '20200501.sw', '20200501.szl', '20200501.ta', '20200501.tcy', '20200501.te', '20200501.tet', '20200501.tg', '20200501.th', '20200501.ti', '20200501.tk', '20200501.tl', '20200501.tn', '20200501.to', '20200501.tpi', '20200501.tr', '20200501.ts', '20200501.tt', '20200501.tum', '20200501.tw', '20200501.ty', '20200501.tyv', '20200501.udm', '20200501.ug', '20200501.uk', '20200501.ur', '20200501.uz', '20200501.ve', '20200501.vec', '20200501.vep', '20200501.vi', '20200501.vls', '20200501.vo', '20200501.wa', '20200501.war', '20200501.wo', '20200501.wuu', '20200501.xal', '20200501.xh', '20200501.xmf', '20200501.yi', '20200501.yo', '20200501.za', '20200501.zea', '20200501.zh', '20200501.zh-classical', '20200501.zh-min-nan', '20200501.zh-yue', '20200501.zu']
```
The cached datasets:
```
% aws s3 --no-sign-request --endpoint-url https://storage.googleapis.com ls s3://huggingface-nlp/cache/datasets/wikipedia/
PRE 20200501.de/
PRE 20200501.en/
PRE 20200501.fr/
PRE 20200501.frr/
PRE 20200501.it/
PRE 20200501.simple/
``` | null | {
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} | [] | closed | false | null | [] | [] | 2021-04-04T19:29:54 | 2021-04-19T09:08:55 | 2021-04-19T09:08:55 | COLLABORATOR | null | null | null | null | A minimal reproducible example:
```python
>>> from datasets import load_dataset, Dataset
>>> dset = load_dataset("sst", split="train")
>>> dset.save_to_disk("sst")
>>> type(dset.split)
<class 'datasets.splits.NamedSplit'>
>>> dset = Dataset.load_from_disk("sst")
>>> type(dset.split) # NamedSplit expected
<class 'str'>
```
It seems like this bug was introduced in #2025. | {
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"Hi @vyraun ! The test references for CommonGen are not publicly available: you can reach out to the original dataset authors if you would like to ask for them, but we will not be releasing them as part of GEM (March 31st was the release date for the test set inputs, references are incidentally released for some of... | 2021-04-04T02:02:45 | 2021-04-06T08:13:12 | 2021-04-06T08:13:12 | NONE | null | null | null | null | @yjernite Hi, are the test sets for the GEM datasets scheduled to be [added soon](https://gem-benchmark.com/shared_task)?
e.g.
```
from datasets import load_dataset
DATASET_NAME="common_gen"
data = load_dataset("gem", DATASET_NAME)
```
The test set doesn't have the target or references.
```
data['test'][0]
{'concept_set_id': 0, 'concepts': ['drill', 'field', 'run', 'team'], 'gem_id': 'common_gen-test-0', 'gem_parent_id': 'common_gen-test-0', 'references': [], 'target': ''}
```
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https://api.github.com/repos/huggingface/datasets/issues/2165 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2165/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2165/comments | https://api.github.com/repos/huggingface/datasets/issues/2165/events | https://github.com/huggingface/datasets/issues/2165 | 849,771,665 | MDU6SXNzdWU4NDk3NzE2NjU= | 2,165 | How to convert datasets.arrow_dataset.Dataset to torch.utils.data.Dataset | {
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"Hi,\r\n\r\na HF dataset can be converted to a Torch Dataset with a simple wrapper as follows:\r\n```python\r\nfrom torch.utils.data import Dataset\r\n \r\nclass HFDataset(Dataset):\r\n def __init__(self, dset):\r\n self.dset = dset\r\n\r\n def __getitem__(self, idx):\r\n return self.dset[idx]\r... | 2021-04-04T01:01:48 | 2021-08-24T15:55:35 | 2021-04-07T15:06:04 | NONE | null | null | null | null | Hi,
I'm trying to pretraine deep-speed model using HF arxiv dataset like:
```
train_ds = nlp.load_dataset('scientific_papers', 'arxiv')
train_ds.set_format(
type="torch",
columns=["input_ids", "attention_mask", "global_attention_mask", "labels"],
)
engine, _, _, _ = deepspeed.initialize(
args=args,
model=model,
model_parameters=[p for p in model.parameters() if p.requires_grad],
training_data=train_ds)
```
but deepspeed.initialize accepts torch.utils.data.Dataset only. How can I convert HF-style dataset to torch-style dataset?
| {
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"This looks like an issue with the cc100 dataset itself but not sure\r\nDid you try loading cc100 on your machine ?",
"Hi\nloading works fine, but the viewer only is broken\nthanks\n\nOn Wed, Apr 7, 2021 at 12:17 PM Quentin Lhoest ***@***.***>\nwrote:\n\n> This looks like an issue with the cc100 dataset itself bu... | 2021-04-02T10:11:13 | 2022-10-05T13:20:24 | 2022-10-05T13:20:24 | NONE | null | null | null | null | Hi
visualization through dataset viewer for cc100 is broken
https://huggingface.co/datasets/viewer/
thanks a lot
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https://api.github.com/repos/huggingface/datasets/issues/2161 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2161/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2161/comments | https://api.github.com/repos/huggingface/datasets/issues/2161/events | https://github.com/huggingface/datasets/issues/2161 | 849,127,041 | MDU6SXNzdWU4NDkxMjcwNDE= | 2,161 | any possibility to download part of large datasets only? | {
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"Not yet but it’s on the short/mid-term roadmap (requested by many indeed).",
"oh, great, really awesome feature to have, thank you very much for the great, fabulous work",
"We'll work on dataset streaming soon. This should allow you to only load the examples you need ;)",
"thanks a lot Quentin, this would be... | 2021-04-02T10:06:46 | 2022-10-05T13:26:51 | 2022-10-05T13:26:51 | NONE | null | null | null | null | Hi
Some of the datasets I need like cc100 are very large, and then I wonder if I can download first X samples of the shuffled/unshuffled data without going through first downloading the whole data then sampling? thanks | {
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https://api.github.com/repos/huggingface/datasets/issues/2160 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2160/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2160/comments | https://api.github.com/repos/huggingface/datasets/issues/2160/events | https://github.com/huggingface/datasets/issues/2160 | 849,052,921 | MDU6SXNzdWU4NDkwNTI5MjE= | 2,160 | data_args.preprocessing_num_workers almost freezes | {
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"Hi.\r\nI cannot always reproduce this issue, and on later runs I did not see it so far. Sometimes also I set 8 processes but I see less being showed, is this normal, here only 5 are shown for 8 being set, thanks\r\n\r\n```\r\n#3: 11%|███████████████▊ ... | 2021-04-02T07:56:13 | 2021-04-02T10:14:32 | 2021-04-02T10:14:31 | NONE | null | null | null | null | Hi @lhoestq
I am running this code from huggingface transformers https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_mlm.py
to speed up tokenization, since I am running on multiple datasets, I am using data_args.preprocessing_num_workers = 4 with opus100 corpus but this moves on till a point and then this freezes almost for sometime during tokenization steps and then this is back again, overall to me taking more time than normal case, I appreciate your advice on how I can use this option properly to speed up.
thanks | {
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"closing since I think this is cc100, just the name has been changed. thanks "
] | 2021-04-01T23:28:36 | 2021-04-02T10:05:19 | 2021-04-02T10:05:19 | NONE | null | null | null | null | ## Adding a Dataset
- **Name:** ccnet
- **Description:**
Common Crawl
- **Paper:**
https://arxiv.org/abs/1911.00359
- **Data:**
https://github.com/facebookresearch/cc_net
- **Motivation:**
this is one of the most comprehensive clean monolingual datasets across a variety of languages. Quite important for cross-lingual reseach
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
thanks | {
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"Thanks for reporting !\r\nThe viewer doesn't have all the dependencies of the datasets. We may add openpyxl to be able to show this dataset properly",
"This viewer tool is deprecated now and the new viewer at https://huggingface.co/datasets/fake_news_english works fine, so I'm closing this issue"
] | 2021-04-01T14:13:20 | 2022-10-05T13:22:02 | 2022-10-05T13:22:02 | NONE | null | null | null | null | When I visit the [Huggingface - viewer](https://huggingface.co/datasets/viewer/) web site, under the dataset "fake_news_english" I've got this error:
> ImportError: To be able to use this dataset, you need to install the following dependencies['openpyxl'] using 'pip install # noqa: requires this pandas optional dependency for reading xlsx files' for instance'
as well as the error Traceback.
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"Hi ! Thanks for reporting. I opened a PR to fix this issue: #2201",
"Nice question which helped me a lot! I have wasted a lot of time to the `DatasetDict` creation from a csv file. Hope the document of this module add some simple examples.",
"Hi :) We're indeed working on tutorials that we will add to the docs... | 2021-03-31T08:30:09 | 2022-10-05T13:29:12 | 2022-10-05T13:29:12 | NONE | null | null | null | null | First of all, I'm sorry if it is a repeated issue or the changes are already in master, I searched and I didn't find anything.
I'm using datasets 1.5.0
As you can see, when I load the dataset, the ClassLabels are ignored, I have to cast the dataset in order to make it work.
Code to reproduce:
```python
import datasets
data_location = "/data/prueba_multiclase"
features = datasets.Features(
{"texto": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["false", "true"])}
)
dataset = datasets.load_dataset(
"csv", data_files=data_location, delimiter="\t", features=features
)
```
Dataset I used:
[prueba_multiclase.zip](https://github.com/huggingface/datasets/files/6235022/prueba_multiclase.zip) (it has to be unzipped)
Thank you! ❤️
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https://api.github.com/repos/huggingface/datasets/issues/2149 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2149/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2149/comments | https://api.github.com/repos/huggingface/datasets/issues/2149/events | https://github.com/huggingface/datasets/issues/2149 | 844,734,076 | MDU6SXNzdWU4NDQ3MzQwNzY= | 2,149 | Telugu subset missing for xtreme tatoeba dataset | {
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"Good catch ! Thanks for reporting\r\n\r\nI just opened #2180 to fix this",
"Fixed in #2180"
] | 2021-03-30T15:26:34 | 2022-10-05T13:28:30 | 2022-10-05T13:28:30 | CONTRIBUTOR | null | null | null | null | from nlp import load_dataset
train_dataset = load_dataset('xtreme', 'tatoeba.tel')['validation']
ValueError: BuilderConfig tatoeba.tel not found.
but language tel is actually included in xtreme:
https://github.com/google-research/xtreme/blob/master/utils_preprocess.py
def tatoeba_preprocess(args):
lang3_dict = {
'afr':'af', 'ara':'ar', 'bul':'bg', 'ben':'bn',
'deu':'de', 'ell':'el', 'spa':'es', 'est':'et',
'eus':'eu', 'pes':'fa', 'fin':'fi', 'fra':'fr',
'heb':'he', 'hin':'hi', 'hun':'hu', 'ind':'id',
'ita':'it', 'jpn':'ja', 'jav':'jv', 'kat':'ka',
'kaz':'kk', 'kor':'ko', 'mal':'ml', 'mar':'mr',
'nld':'nl', 'por':'pt', 'rus':'ru', 'swh':'sw',
'tam':'ta', **_'tel':'te'_**, 'tha':'th', 'tgl':'tl', <----here
'tur':'tr', 'urd':'ur', 'vie':'vi', 'cmn':'zh',
'eng':'en',
} | {
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https://api.github.com/repos/huggingface/datasets/issues/2148 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2148/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2148/comments | https://api.github.com/repos/huggingface/datasets/issues/2148/events | https://github.com/huggingface/datasets/issues/2148 | 844,700,910 | MDU6SXNzdWU4NDQ3MDA5MTA= | 2,148 | Add configurable options to `seqeval` metric | {
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"Hi @marrodion. \r\n\r\nThanks for pointing this out. It would be great to incorporate this metric-specific enhancement.\r\n\r\nAnother possibility would be to require the user to input the scheme as a string `mode=\"strict\", scheme=\"IOB2\"` and then dynamically import the corresponding module using Python `impor... | 2021-03-30T15:04:06 | 2021-04-15T13:49:46 | 2021-04-15T13:49:46 | CONTRIBUTOR | null | null | null | null | Right now `load_metric("seqeval")` only works in the default mode of evaluation (equivalent to conll evaluation).
However, seqeval library [supports](https://github.com/chakki-works/seqeval#support-features) different evaluation schemes (IOB1, IOB2, etc.), which can be plugged in just by supporting additional kwargs in `Seqeval._compute`
https://github.com/huggingface/datasets/blob/85cf7ff920c90ca2e12bedca12b36d2a043c3da2/metrics/seqeval/seqeval.py#L109
Things that would be relevant are, for example, supporting `mode="strict", scheme=IOB2` to count only full entity match as a true positive and omit partial matches.
The only problem I see is that the spirit of `metrics` seems to not require additional imports from user. `seqeval` only supports schemes as objects, without any string aliases.
It can be solved naively with mapping like `{"IOB2": seqeval.scheme.IOB2}`. Or just left as is and require user to explicitly import scheme from `seqeval` if he wants to configure it past the default implementation.
If that makes sense, I am happy to implement the change. | {
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https://api.github.com/repos/huggingface/datasets/issues/2146 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2146/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2146/comments | https://api.github.com/repos/huggingface/datasets/issues/2146/events | https://github.com/huggingface/datasets/issues/2146 | 844,673,244 | MDU6SXNzdWU4NDQ2NzMyNDQ= | 2,146 | Dataset file size on disk is very large with 3D Array | {
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"Hi ! In the arrow file we store all the integers as uint8.\r\nSo your arrow file should weigh around `height x width x n_channels x n_images` bytes.\r\n\r\nWhat feature type do your TFDS dataset have ?\r\n\r\nIf it uses a `tfds.features.Image` type, then what is stored is the encoded data (as png or jpg for exampl... | 2021-03-30T14:46:09 | 2021-04-16T13:07:02 | null | NONE | null | null | null | null | Hi,
I have created my own dataset using the provided dataset loading script. It is an image dataset where images are stored as 3D Array with dtype=uint8.
The actual size on disk is surprisingly large. It takes 520 MB. Here is some info from `dataset_info.json`.
`{
"description": "",
"citation": "",
"homepage": "",
"license": "",
"features": {
"image": {
"shape": [224, 224, 3],
"dtype": "uint8",
"id": null,
"_type": "Array3D",
}
},
"post_processed": null,
"supervised_keys": null,
"builder_name": "shot_type_image_dataset",
"config_name": "default",
"version": {
"version_str": "0.0.0",
"description": null,
"major": 0,
"minor": 0,
"patch": 0,
},
"splits": {
"train": {
"name": "train",
"num_bytes": 520803408,
"num_examples": 1479,
"dataset_name": "shot_type_image_dataset",
}
},
"download_checksums": {
"": {
"num_bytes": 16940447118,
"checksum": "5854035705efe08b0ed8f3cf3da7b4d29cba9055c2d2d702c79785350d72ee03",
}
},
"download_size": 16940447118,
"post_processing_size": null,
"dataset_size": 520803408,
"size_in_bytes": 17461250526,
}`
I have created the same dataset with tensorflow_dataset and it takes only 125MB on disk.
I am wondering, is it normal behavior ? I understand `Datasets` uses Arrow for serialization wheres tf uses TF Records.
This might be a problem for large dataset.
Thanks for your help.
| null | {
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https://api.github.com/repos/huggingface/datasets/issues/2144 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2144/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2144/comments | https://api.github.com/repos/huggingface/datasets/issues/2144/events | https://github.com/huggingface/datasets/issues/2144 | 844,352,067 | MDU6SXNzdWU4NDQzNTIwNjc= | 2,144 | Loading wikipedia 20200501.en throws pyarrow related error | {
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"That's how I loaded the dataset\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache')\r\n```",
"Hi ! It looks like the arrow file in the folder\r\n`/usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa... | 2021-03-30T10:38:31 | 2021-04-01T09:21:17 | null | NONE | null | null | null | null | **Problem description**
I am getting the following error when trying to load wikipedia/20200501.en dataset.
**Error log**
Downloading and preparing dataset wikipedia/20200501.en (download: 16.99 GiB, generated: 17.07 GiB, post-processed: Unknown size, total: 34.06 GiB) to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931...
Downloading: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14.6k/14.6k [00:00<00:00, 5.41MB/s]
Downloading: 59%|███████████████████████████████████████████████████████████████████████████████████████▊ | 10.7G/18.3G [11:30<08:08, 15.5MB/s]
Dataset wikipedia downloaded and prepared to /usr/local/workspace/NAS_NLP/cache/wikipedia/20200501.en/1.0.0/50aa706aa417bb77d910ad61211cc672c0ef3e0f224225a5e0a18277ade8b931. Subsequent calls will reuse this data.
Traceback (most recent call last):
File "load_wiki.py", line 2, in <module>
ds = load_dataset('wikipedia', '20200501.en', cache_dir='/usr/local/workspace/NAS_NLP/cache')
File "/usr/local/lib/python3.6/dist-packages/datasets/load.py", line 751, in load_dataset
ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)
File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 746, in as_dataset
map_tuple=True,
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in map_nested
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 204, in <listcomp>
_single_map_nested((function, obj, types, None, True)) for obj in tqdm(iterable, disable=disable_tqdm)
File "/usr/local/lib/python3.6/dist-packages/datasets/utils/py_utils.py", line 142, in _single_map_nested
return function(data_struct)
File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 763, in _build_single_dataset
in_memory=in_memory,
File "/usr/local/lib/python3.6/dist-packages/datasets/builder.py", line 835, in _as_dataset
in_memory=in_memory,
File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 215, in read
return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)
File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 236, in read_files
pa_table = self._read_files(files, in_memory=in_memory)
File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 171, in _read_files
pa_table: pa.Table = self._get_dataset_from_filename(f_dict, in_memory=in_memory)
File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 302, in _get_dataset_from_filename
pa_table = ArrowReader.read_table(filename, in_memory=in_memory)
File "/usr/local/lib/python3.6/dist-packages/datasets/arrow_reader.py", line 324, in read_table
pa_table = f.read_all()
File "pyarrow/ipc.pxi", line 544, in pyarrow.lib.RecordBatchReader.read_all
File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status
OSError: Expected to be able to read 9176784 bytes for message body, got 4918712
**Detailed version info**
datasets==1.5.0
- dataclasses [required: Any, installed: 0.8]
- dill [required: Any, installed: 0.3.3]
- fsspec [required: Any, installed: 0.8.7]
- importlib-metadata [required: Any, installed: 1.7.0]
- zipp [required: >=0.5, installed: 3.1.0]
- huggingface-hub [required: <0.1.0, installed: 0.0.7]
- filelock [required: Any, installed: 3.0.12]
- importlib-metadata [required: Any, installed: 1.7.0]
- zipp [required: >=0.5, installed: 3.1.0]
- requests [required: Any, installed: 2.24.0]
- certifi [required: >=2017.4.17, installed: 2020.6.20]
- chardet [required: >=3.0.2,<4, installed: 3.0.4]
- idna [required: >=2.5,<3, installed: 2.6]
- urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]
- tqdm [required: Any, installed: 4.49.0]
- importlib-metadata [required: Any, installed: 1.7.0]
- zipp [required: >=0.5, installed: 3.1.0]
- multiprocess [required: Any, installed: 0.70.11.1]
- dill [required: >=0.3.3, installed: 0.3.3]
- numpy [required: >=1.17, installed: 1.17.0]
- pandas [required: Any, installed: 1.1.5]
- numpy [required: >=1.15.4, installed: 1.17.0]
- python-dateutil [required: >=2.7.3, installed: 2.8.0]
- six [required: >=1.5, installed: 1.15.0]
- pytz [required: >=2017.2, installed: 2020.1]
- pyarrow [required: >=0.17.1, installed: 3.0.0]
- numpy [required: >=1.16.6, installed: 1.17.0]
- requests [required: >=2.19.0, installed: 2.24.0]
- certifi [required: >=2017.4.17, installed: 2020.6.20]
- chardet [required: >=3.0.2,<4, installed: 3.0.4]
- idna [required: >=2.5,<3, installed: 2.6]
- urllib3 [required: >=1.21.1,<1.26,!=1.25.1,!=1.25.0, installed: 1.25.10]
- tqdm [required: >=4.27,<4.50.0, installed: 4.49.0]
- xxhash [required: Any, installed: 2.0.0]
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https://api.github.com/repos/huggingface/datasets/issues/2139 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2139/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2139/comments | https://api.github.com/repos/huggingface/datasets/issues/2139/events | https://github.com/huggingface/datasets/issues/2139 | 843,662,613 | MDU6SXNzdWU4NDM2NjI2MTM= | 2,139 | TypeError when using save_to_disk in a dataset loaded with ReadInstruction split | {
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} | [] | closed | false | null | [] | [
"Hi !\r\nI think this has been fixed recently on `master`.\r\nCan you try again by installing `datasets` from `master` ?\r\n```\r\npip install git+https://github.com/huggingface/datasets.git\r\n```",
"Hi!\r\n\r\nUsing that version of the code solves the issue. Thanks!"
] | 2021-03-29T18:23:54 | 2021-03-30T09:12:53 | 2021-03-30T09:12:53 | NONE | null | null | null | null | Hi,
Loading a dataset with `load_dataset` using a split defined via `ReadInstruction` and then saving it to disk results in the following error: `TypeError: Object of type ReadInstruction is not JSON serializable`.
Here is the minimal reproducible example:
```python
from datasets import load_dataset
from datasets import ReadInstruction
data_1 = load_dataset(
"wikiann",
"en",
split="validation",
)
data_1.save_to_disk("temporary_path_1")
print("Save with regular split works.")
data_2 = load_dataset(
"wikiann",
"en",
split=ReadInstruction("validation", to=50, unit="%"),
)
data_2.save_to_disk("temporary_path_2")
```
and the corresponding output:
```
Reusing dataset wikiann (/xxxxx/.cache/huggingface/datasets/wikiann/en/1.1.0/0b11a6fb31eea02f38ca17610657bfba3206100685283014daceb8da291c3be9)
Save with regular split works.
Reusing dataset wikiann (/xxxxx/.cache/huggingface/datasets/wikiann/en/1.1.0/0b11a6fb31eea02f38ca17610657bfba3206100685283014daceb8da291c3be9)
Traceback (most recent call last):
File "bug.py", line 20, in <module>
data_2.save_to_disk("temporary_path_2")
File "/xxxxx/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 645, in save_to_disk
json.dump(state, state_file, indent=2, sort_keys=True)
File "/usr/lib/python3.7/json/__init__.py", line 179, in dump
for chunk in iterable:
File "/usr/lib/python3.7/json/encoder.py", line 431, in _iterencode
yield from _iterencode_dict(o, _current_indent_level)
File "/usr/lib/python3.7/json/encoder.py", line 405, in _iterencode_dict
yield from chunks
File "/usr/lib/python3.7/json/encoder.py", line 438, in _iterencode
o = _default(o)
File "/usr/lib/python3.7/json/encoder.py", line 179, in default
raise TypeError(f'Object of type {o.__class__.__name__} '
TypeError: Object of type ReadInstruction is not JSON serializable
```
Let me know if there is some misuse from my end.
Thanks in advance.
| {
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https://api.github.com/repos/huggingface/datasets/issues/2135 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2135/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2135/comments | https://api.github.com/repos/huggingface/datasets/issues/2135/events | https://github.com/huggingface/datasets/issues/2135 | 843,246,344 | MDU6SXNzdWU4NDMyNDYzNDQ= | 2,135 | en language data from MLQA dataset is missing | {
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"Hi ! Indeed only the languages of the `translate-train` data are included...\r\nI can't find a link to download the english train set on https://github.com/facebookresearch/MLQA though, do you know where we can download it ?",
"Hi @lhoestq \r\nthank you very much for coming back to me, now I see, you are right, ... | 2021-03-29T10:47:50 | 2021-03-30T10:20:23 | 2021-03-30T10:20:23 | CONTRIBUTOR | null | null | null | null | Hi
I need mlqa-translate-train.en dataset, but it is missing from the MLQA dataset. could you have a look please? @lhoestq thank you for your help to fix this issue. | {
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https://api.github.com/repos/huggingface/datasets/issues/2134 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2134/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2134/comments | https://api.github.com/repos/huggingface/datasets/issues/2134/events | https://github.com/huggingface/datasets/issues/2134 | 843,242,849 | MDU6SXNzdWU4NDMyNDI4NDk= | 2,134 | Saving large in-memory datasets with save_to_disk crashes because of pickling | {
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"gists... | [
"Hi !\r\nIndeed `save_to_disk` doesn't call pickle anymore. Though the `OverflowError` can still appear for in-memory datasets bigger than 4GB. This happens when doing this for example:\r\n```python\r\nimport pyarrow as pa\r\nimport pickle\r\n\r\narr = pa.array([0] * ((4 * 8 << 30) // 64))\r\ntable = pa.Table.from_... | 2021-03-29T10:43:15 | 2021-05-03T17:59:21 | 2021-05-03T17:59:21 | NONE | null | null | null | null | Using Datasets 1.5.0 on Python 3.7.
Recently I've been working on medium to large size datasets (pretokenized raw text sizes from few gigabytes to low tens of gigabytes), and have found out that several preprocessing steps are massively faster when done in memory, and I have the ability to requisition a lot of RAM, so I decided to do these steps completely out of the datasets library.
So my workflow is to do several .map() on datasets object, then for the operation which is faster in memory to extract the necessary columns from the dataset and then drop it whole, do the transformation in memory, and then create a fresh Dataset object using .from_dict() or other method.
When I then try to call save_to_disk(path) on the dataset, it crashes because of pickling, which appears to be because of using old pickle protocol which doesn't support large files (over 4 GiB).
```
Traceback (most recent call last):
File "./tokenize_and_chunkify_in_memory.py", line 80, in <module>
main()
File "./tokenize_and_chunkify_in_memory.py", line 75, in main
tokenize_and_chunkify(config)
File "./tokenize_and_chunkify_in_memory.py", line 60, in tokenize_and_chunkify
contexts_dataset.save_to_disk(chunked_path)
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 457, in save_to_disk
self = pickle.loads(pickle.dumps(self))
OverflowError: cannot serialize a bytes object larger than 4 GiB
```
From what I've seen this issue may be possibly fixed, as the line `self = pickle.loads(pickle.dumps(self))` does not appear to be present in the current state of the repository.
To save these datasets to disk, I've resorted to calling .map() over them with `function=None` and specifying the .arrow cache file, and then creating a new dataset using the .from_file() method, which I can then safely save to disk.
Additional issue when working with these large in-memory datasets is when using multiprocessing, is again to do with pickling. I've tried to speed up the mapping with function=None by specifying num_proc to the available cpu count, and I again get issues with transferring the dataset, with the following traceback. I am not sure if I should open a separate issue for that.
```
Traceback (most recent call last):
File "./tokenize_and_chunkify_in_memory.py", line 94, in <module>
main()
File "./tokenize_and_chunkify_in_memory.py", line 89, in main
tokenize_and_chunkify(config)
File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify
contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads)
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map
transformed_shards = [r.get() for r in results]
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp>
transformed_shards = [r.get() for r in results]
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get
raise self._value
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks
put(task)
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send
self._send_bytes(_ForkingPickler.dumps(obj))
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps
cls(buf, protocol, *args, **kwds).dump(obj)
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump
StockPickler.dump(self, obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump
self.save(obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict
self._batch_setitems(obj.items())
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems
save(v)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save
self.save_reduce(obj=obj, *rv)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce
save(state)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict
self._batch_setitems(obj.items())
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems
save(v)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save
self.save_reduce(obj=obj, *rv)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce
save(args)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list
self._batch_appends(obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends
save(x)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save
self.save_reduce(obj=obj, *rv)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce
save(args)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list
self._batch_appends(obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends
save(tmp[0])
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save
self.save_reduce(obj=obj, *rv)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce
save(args)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list
self._batch_appends(obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends
save(tmp[0])
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list
self._batch_appends(obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends
save(tmp[0])
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list
self._batch_appends(obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends
save(x)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save
self.save_reduce(obj=obj, *rv)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce
save(args)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes
self._write_large_bytes(BINBYTES + pack("<I", n), obj)
struct.error: 'I' format requires 0 <= number <= 4294967295Traceback (most recent call last):
File "./tokenize_and_chunkify_in_memory.py", line 94, in <module>
main()
File "./tokenize_and_chunkify_in_memory.py", line 89, in main
tokenize_and_chunkify(config)
File "./tokenize_and_chunkify_in_memory.py", line 67, in tokenize_and_chunkify
contexts_dataset.map(function=None, cache_file_name=str(output_dir_path / "tmp.arrow"), writer_batch_size=50000, num_proc=config.threads)
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in map
transformed_shards = [r.get() for r in results]
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1485, in <listcomp>
transformed_shards = [r.get() for r in results]
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 657, in get
raise self._value
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/pool.py", line 431, in _handle_tasks
put(task)
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/connection.py", line 209, in send
self._send_bytes(_ForkingPickler.dumps(obj))
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/multiprocess/reduction.py", line 54, in dumps
cls(buf, protocol, *args, **kwds).dump(obj)
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 454, in dump
StockPickler.dump(self, obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 437, in dump
self.save(obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict
self._batch_setitems(obj.items())
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems
save(v)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save
self.save_reduce(obj=obj, *rv)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 662, in save_reduce
save(state)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/home/cernypro/dev/envs/huggingface_gpu/lib/python3.7/site-packages/dill/_dill.py", line 941, in save_module_dict
StockPickler.save_dict(pickler, obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 859, in save_dict
self._batch_setitems(obj.items())
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 885, in _batch_setitems
save(v)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save
self.save_reduce(obj=obj, *rv)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce
save(args)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list
self._batch_appends(obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends
save(x)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save
self.save_reduce(obj=obj, *rv)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce
save(args)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list
self._batch_appends(obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends
save(tmp[0])
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save
self.save_reduce(obj=obj, *rv)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce
save(args)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list
self._batch_appends(obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends
save(tmp[0])
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list
self._batch_appends(obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 846, in _batch_appends
save(tmp[0])
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 789, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 819, in save_list
self._batch_appends(obj)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 843, in _batch_appends
save(x)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 549, in save
self.save_reduce(obj=obj, *rv)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 638, in save_reduce
save(args)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 774, in save_tuple
save(element)
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 504, in save
f(self, obj) # Call unbound method with explicit self
File "/mnt/appl/software/Python/3.7.4-GCCcore-8.3.0/lib/python3.7/pickle.py", line 732, in save_bytes
self._write_large_bytes(BINBYTES + pack("<I", n), obj)
struct.error: 'I' format requires 0 <= number <= 4294967295
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https://api.github.com/repos/huggingface/datasets/issues/2133 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2133/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2133/comments | https://api.github.com/repos/huggingface/datasets/issues/2133/events | https://github.com/huggingface/datasets/issues/2133 | 843,149,680 | MDU6SXNzdWU4NDMxNDk2ODA= | 2,133 | bug in mlqa dataset | {
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"If you print those questions, you get readable texts:\r\n```python\r\n>>> questions = [\r\n... \"\\u0645\\u062a\\u0649 \\u0628\\u062f\\u0627\\u062a \\u0627\\u0644\\u0645\\u062c\\u0644\\u0629 \\u0627\\u0644\\u0645\\u062f\\u0631\\u0633\\u064a\\u0629 \\u0641\\u064a \\u0646\\u0648\\u062a\\u0631\\u062f\\u0627\\u064... | 2021-03-29T09:03:09 | 2021-03-30T17:40:57 | 2021-03-30T17:40:57 | NONE | null | null | null | null | Hi
Looking into MLQA dataset for langauge "ar":
```
"question": [
"\u0645\u062a\u0649 \u0628\u062f\u0627\u062a \u0627\u0644\u0645\u062c\u0644\u0629 \u0627\u0644\u0645\u062f\u0631\u0633\u064a\u0629 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645 \u0628\u0627\u0644\u0646\u0634\u0631?",
"\u0643\u0645 \u0645\u0631\u0629 \u064a\u062a\u0645 \u0646\u0634\u0631\u0647\u0627 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?",
"\u0645\u0627 \u0647\u064a \u0627\u0644\u0648\u0631\u0642\u0629 \u0627\u0644\u064a\u0648\u0645\u064a\u0629 \u0644\u0644\u0637\u0644\u0627\u0628 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?",
"\u0643\u0645 \u0639\u062f\u062f \u0627\u0644\u0627\u0648\u0631\u0627\u0642 \u0627\u0644\u0627\u062e\u0628\u0627\u0631\u064a\u0629 \u0644\u0644\u0637\u0644\u0627\u0628 \u0627\u0644\u062a\u064a \u0648\u062c\u062f\u062a \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?",
"\u0641\u064a \u0627\u064a \u0633\u0646\u0629 \u0628\u062f\u0627\u062a \u0648\u0631\u0642\u0629 \u0627\u0644\u0637\u0627\u0644\u0628 \u0627\u0644\u062d\u0633 \u0627\u0644\u0633\u0644\u064a\u0645 \u0628\u0627\u0644\u0646\u0634\u0631 \u0641\u064a \u0646\u0648\u062a\u0631\u062f\u0627\u0645?"
]
```
the questions are in the wrong format, and not readable, could you please have a look? thanks @lhoestq
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"You can filter the languages this way:\r\n```python\r\ntydiqa_en = tydiqa_dataset.filter(lambda x: x[\"language\"] == \"english\")\r\n```\r\n\r\nOtherwise maybe we can have one configuration per language ?\r\nWhat do you think of this for example ?\r\n\r\n```python\r\nload_dataset(\"tydiqa\", \"primary_task.en\")\... | 2021-03-29T08:56:21 | 2021-04-04T09:57:15 | null | NONE | null | null | null | null | Hi @lhoestq
Currently TydiQA is mixed and user can only access the whole training set of all languages:
https://www.tensorflow.org/datasets/catalog/tydi_qa
for using this dataset, one need to train/evaluate in each separate language, and having them mixed, makes it hard to use this dataset. This is much convenient for user to have them split and I appreciate your help on this.
Meanwhile, till hopefully this is split per language, I greatly appreciate telling me how I can preprocess and get data per language. thanks a lot | null | {
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"Hi ! Thanks for reporting\r\nI was able to reproduce this issue. This was caused by missing split infos if a worker reloads the cache of the other worker.\r\n\r\nI just opened https://github.com/huggingface/datasets/pull/2137 to fix this issue",
"The PR got merged :)\r\nFeel free to try it out on the `master` br... | 2021-03-29T08:45:58 | 2021-04-10T11:08:55 | 2021-04-10T11:08:55 | NONE | null | null | null | null | version: 1.5.0
met a very strange error, I am training large scale language model, and need train on 2 machines(workers).
And sometimes I will get this error `TypeError: 'NoneType' object is not iterable`
This is traceback
```
71 | | Traceback (most recent call last):
-- | -- | --
72 | | File "run_gpt.py", line 316, in <module>
73 | | main()
74 | | File "run_gpt.py", line 222, in main
75 | | delimiter="\t", column_names=["input_ids", "attention_mask", "chinese_ref"])
76 | | File "/data/miniconda3/lib/python3.7/site-packages/datasets/load.py", line 747, in load_dataset
77 | | use_auth_token=use_auth_token,
78 | | File "/data/miniconda3/lib/python3.7/site-packages/datasets/builder.py", line 513, in download_and_prepare
79 | | self.download_post_processing_resources(dl_manager)
80 | | File "/data/miniconda3/lib/python3.7/site-packages/datasets/builder.py", line 673, in download_post_processing_resources
81 | | for split in self.info.splits:
82 | | TypeError: 'NoneType' object is not iterable
83 | | WARNING:datasets.builder:Reusing dataset csv (/usr/local/app/.cache/huggingface/datasets/csv/default-1c257ebd48e225e7/0.0.0/2960f95a26e85d40ca41a230ac88787f715ee3003edaacb8b1f0891e9f04dda2)
84 | | Traceback (most recent call last):
85 | | File "/data/miniconda3/lib/python3.7/runpy.py", line 193, in _run_module_as_main
86 | | "__main__", mod_spec)
87 | | File "/data/miniconda3/lib/python3.7/runpy.py", line 85, in _run_code
88 | | exec(code, run_globals)
89 | | File "/data/miniconda3/lib/python3.7/site-packages/torch/distributed/launch.py", line 340, in <module>
90 | | main()
91 | | File "/data/miniconda3/lib/python3.7/site-packages/torch/distributed/launch.py", line 326, in main
92 | | sigkill_handler(signal.SIGTERM, None) # not coming back
93 | | File "/data/miniconda3/lib/python3.7/site-packages/torch/distributed/launch.py", line 301, in sigkill_handler
94 | | raise subprocess.CalledProcessError(returncode=last_return_code, cmd=cmd)
```
On worker 1 it loads the dataset well, however on worker 2 will get this error.
And I will meet this error from time to time, sometimes it just goes well. | {
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"Here please find TFDS format of this dataset: https://www.tensorflow.org/datasets/catalog/wikiann\r\nwhere there is a span column, this is really necessary to be able to use the data, and I appreciate your help @lhoestq ",
"Hi !\r\nApparently you can get the spans from the NER tags using `tags_to_spans` defined ... | 2021-03-29T08:23:00 | 2021-08-27T14:44:18 | 2021-08-27T14:44:18 | NONE | null | null | null | null | Hi
Wikiann dataset needs to have "spans" columns, which is necessary to be able to use this dataset, but this column is missing from huggingface datasets, could you please have a look? thank you @lhoestq | {
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"Hi !\r\nWe're not using `TextDatasetForNextSentencePrediction` in `datasets`.\r\nAlthough you can probably use the `TextDatasetForNextSentencePrediction.create_examples_from_document` on a dataset to prepare it for next sentence prediction.",
"Thanks.\r\n\r\nDo you mean that `TextDatasetForNextSentencePrediction... | 2021-03-29T06:48:03 | 2021-04-01T04:58:40 | 2021-04-01T04:58:40 | NONE | null | null | null | null | Hello.
I'm trying to pretrain the BERT model with next sentence prediction. Is there any function that supports next sentence prediction
like ` TextDatasetForNextSentencePrediction` of `huggingface/transformers` ?
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https://api.github.com/repos/huggingface/datasets/issues/2128 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2128/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2128/comments | https://api.github.com/repos/huggingface/datasets/issues/2128/events | https://github.com/huggingface/datasets/issues/2128 | 843,023,910 | MDU6SXNzdWU4NDMwMjM5MTA= | 2,128 | Dialogue action slot name and value are reversed in MultiWoZ 2.2 | {
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"Hi\r\nGood catch ! Thanks for reporting\r\n\r\nIf you are interested in contributing, feel free to open a PR to fix this :) "
] | 2021-03-29T06:34:02 | 2021-03-31T12:48:01 | 2021-03-31T12:48:01 | CONTRIBUTOR | null | null | null | null | Hi @yjernite, thank you for adding MultiWoZ 2.2 in the huggingface datasets platform. It is beneficial!
I spot an error that the order of Dialogue action slot names and values are reversed.
https://github.com/huggingface/datasets/blob/649b2c469779bc4221e1b6969aa2496d63eb5953/datasets/multi_woz_v22/multi_woz_v22.py#L251-L262 | {
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https://api.github.com/repos/huggingface/datasets/issues/2125 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2125/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2125/comments | https://api.github.com/repos/huggingface/datasets/issues/2125/events | https://github.com/huggingface/datasets/issues/2125 | 842,690,570 | MDU6SXNzdWU4NDI2OTA1NzA= | 2,125 | Is dataset timit_asr broken? | {
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"Hi,\r\n\r\nthanks for the report, but this is a duplicate of #2052. ",
"@mariosasko \r\nThank you for your quick response! Following #2052, I've fixed the problem."
] | 2021-03-28T08:30:18 | 2021-03-28T12:29:25 | 2021-03-28T12:29:25 | NONE | null | null | null | null | Using `timit_asr` dataset, I saw all records are the same.
``` python
from datasets import load_dataset, load_metric
timit = load_dataset("timit_asr")
from datasets import ClassLabel
import random
import pandas as pd
from IPython.display import display, HTML
def show_random_elements(dataset, num_examples=10):
assert num_examples <= len(dataset), "Can't pick more elements than there are in the dataset."
picks = []
for _ in range(num_examples):
pick = random.randint(0, len(dataset)-1)
while pick in picks:
pick = random.randint(0, len(dataset)-1)
picks.append(pick)
df = pd.DataFrame(dataset[picks])
display(HTML(df.to_html()))
show_random_elements(timit['train'].remove_columns(["file", "phonetic_detail", "word_detail", "dialect_region", "id",
"sentence_type", "speaker_id"]), num_examples=20)
```
`output`
<img width="312" alt="Screen Shot 2021-03-28 at 17 29 04" src="https://user-images.githubusercontent.com/42398050/112746646-21acee80-8feb-11eb-84f3-dbb5d4269724.png">
I double-checked it [here](https://huggingface.co/datasets/viewer/), and met the same problem.
<img width="1374" alt="Screen Shot 2021-03-28 at 17 32 07" src="https://user-images.githubusercontent.com/42398050/112746698-9bdd7300-8feb-11eb-97ed-5babead385f4.png">
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https://api.github.com/repos/huggingface/datasets/issues/2124 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2124/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2124/comments | https://api.github.com/repos/huggingface/datasets/issues/2124/events | https://github.com/huggingface/datasets/issues/2124 | 842,627,729 | MDU6SXNzdWU4NDI2Mjc3Mjk= | 2,124 | Adding ScaNN library to do MIPS? | {
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"I haven't played with it (yet) but it sounds really cool !\r\n"
] | 2021-03-28T00:07:00 | 2021-03-29T13:23:43 | null | NONE | null | null | null | null | @lhoestq Hi I am thinking of adding this new google library to do the MIPS similar to **add_faiss_idex**. As the paper suggests, it is really fast when it comes to retrieving the nearest neighbors.
https://github.com/google-research/google-research/tree/master/scann
| null | {
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https://api.github.com/repos/huggingface/datasets/issues/2123 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2123/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2123/comments | https://api.github.com/repos/huggingface/datasets/issues/2123/events | https://github.com/huggingface/datasets/issues/2123 | 842,577,285 | MDU6SXNzdWU4NDI1NzcyODU= | 2,123 | Problem downloading GEM wiki_auto_asset_turk dataset | {
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"Hi,\r\n\r\nsadly I can't replicate the problem on my Windows machine. Try to update the library to the newest version with:\r\n```bash\r\npip install git+https://github.com/huggingface/datasets\r\n``` ",
"Thanks for the answer! I updated the library but unfortunately it didn't solve the problem.",
"Is there an... | 2021-03-27T18:41:28 | 2021-05-12T16:15:18 | 2021-05-12T16:15:17 | NONE | null | null | null | null | @yjernite
### Summary
I am currently working on the GEM datasets and do not manage to download the wiki_auto_asset_turk data, whereas all other datasets download well with the same code.
### Steps to reproduce
Code snippet:
from datasets import load_dataset
#dataset = load_dataset('gem', 'web_nlg_en')
dataset = load_dataset('gem', 'wiki_auto_asset_turk')
```
**Expected behavior:**
I expect the dataset to start downloading (download bar appears and progresses toward 100%)
**Actual behavior:**
Instead of seeing the download bar appearing, nothing happens; the following appears in the console as expected, but nothing more:
Downloading: 36.6kB [00:00, 37.2MB/s]
Downloading: 41.7kB [00:00, ?B/s]
Downloading and preparing dataset gem/wiki_auto_asset_turk (download: 121.37 MiB, generated: 145.69 MiB, post-processed: Unknown size, total: 267.07 MiB) to C:\Users\sfmil\.cache\huggingface\datasets\gem\wiki_auto_asset_turk\1.0.0\f252756d7f1b8f019aac71a1623b2950acfe10d25d956668ac4eae4e93c58b8d...
### Is this a regression?
No, it was the first time I was trying to download this dataset (same for the other ones).
### Debug info
- Python version: Python 3.8.2
- OS version: Windows 10 Family | {
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https://api.github.com/repos/huggingface/datasets/issues/2120 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2120/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2120/comments | https://api.github.com/repos/huggingface/datasets/issues/2120/events | https://github.com/huggingface/datasets/issues/2120 | 841,954,521 | MDU6SXNzdWU4NDE5NTQ1MjE= | 2,120 | dataset viewer does not work anymore | {
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"Thanks for reporting :) We're looking into it",
"Back up. "
] | 2021-03-26T13:22:13 | 2021-03-26T15:52:22 | 2021-03-26T15:52:22 | NONE | null | null | null | null | Hi
I normally use this link to see all datasets and how I can load them
https://huggingface.co/datasets/viewer/
Now I am getting
502 Bad Gateway
nginx/1.18.0 (Ubuntu)
could you bring this webpage back ? this was very helpful @lhoestq
thanks for your help | {
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https://api.github.com/repos/huggingface/datasets/issues/2117 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2117/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2117/comments | https://api.github.com/repos/huggingface/datasets/issues/2117/events | https://github.com/huggingface/datasets/issues/2117 | 841,535,283 | MDU6SXNzdWU4NDE1MzUyODM= | 2,117 | load_metric from local "glue.py" meet error 'NoneType' object is not callable | {
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"@Frankie123421 what was the resolution to this?",
"> @Frankie123421 what was the resolution to this?\r\n\r\nuse glue_metric.py instead of glue.py in load_metric",
"thank you!"
] | 2021-03-26T02:35:22 | 2021-08-25T21:44:05 | 2021-03-26T02:40:26 | NONE | null | null | null | null | actual_task = "mnli" if task == "mnli-mm" else task
dataset = load_dataset(path='/home/glue.py', name=actual_task)
metric = load_metric(path='/home/glue.py', name=actual_task)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-8-7ab77a465d81> in <module>
1 actual_task = "mnli" if task == "mnli-mm" else task
2 dataset = load_dataset(path='/home/jcli/glue.py', name=actual_task)
----> 3 metric = load_metric(path='/home/jcli/glue.py', name=actual_task)
~/anaconda3/envs/pytorch/lib/python3.6/site-packages/datasets/load.py in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, script_version, **metric_init_kwargs)
508 keep_in_memory=keep_in_memory,
509 experiment_id=experiment_id,
--> 510 **metric_init_kwargs,
511 )
512
TypeError: 'NoneType' object is not callable
Please help | {
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https://api.github.com/repos/huggingface/datasets/issues/2116 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2116/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2116/comments | https://api.github.com/repos/huggingface/datasets/issues/2116/events | https://github.com/huggingface/datasets/issues/2116 | 841,481,292 | MDU6SXNzdWU4NDE0ODEyOTI= | 2,116 | Creating custom dataset results in error while calling the map() function | {
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"Hi,\r\n\r\nthe `_data` attribute is missing due to `MyDataset.__init__` not calling the parent `__init__`. However, I don't think it's a good idea to subclass the `datasets.Dataset` class (e.g. it's kind of dangerous to override `datasets.Dataset.__getitem__`). Instead, it's better to follow the \"association over... | 2021-03-26T00:37:46 | 2021-03-31T14:30:32 | 2021-03-31T14:30:32 | NONE | null | null | null | null | calling `map()` of `datasets` library results into an error while defining a Custom dataset.
Reproducible example:
```
import datasets
class MyDataset(datasets.Dataset):
def __init__(self, sentences):
"Initialization"
self.samples = sentences
def __len__(self):
"Denotes the total number of samples"
return len(self.samples)
def __getitem__(self, index):
"Generates one sample of data"
# Select sample
# Load data and get label
samples = self.samples[index]
return samples
def preprocess_function_train(examples):
inputs = examples
labels = [example+tokenizer.eos_token for example in examples ]
inputs = tokenizer(inputs, max_length=30, padding=True, truncation=True)
labels = tokenizer(labels, max_length=30, padding=True, truncation=True)
model_inputs = inputs
model_inputs["labels"] = labels["input_ids"]
print("about to return")
return model_inputs
##train["sentence"] is dataframe column
train_dataset = MyDataset(train['sentence'].values.tolist())
train_dataset = train_dataset.map(
preprocess_function,
batched = True,
batch_size=32
)
```
Stack trace of error:
```
Traceback (most recent call last):
File "dir/train_generate.py", line 362, in <module>
main()
File "dir/train_generate.py", line 245, in main
train_dataset = train_dataset.map(
File "anaconda_dir/anaconda3/envs/env1/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 1244, in map
return self._map_single(
File "anaconda_dir/anaconda3/envs/env1/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 149, in wrapper
unformatted_columns = set(self.column_names) - set(self._format_columns or [])
File "anaconda_dir/anaconda3/envs/env1/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 526, in column_names
return self._data.column_names
AttributeError: 'MyDataset' object has no attribute '_data'
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2115 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2115/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2115/comments | https://api.github.com/repos/huggingface/datasets/issues/2115/events | https://github.com/huggingface/datasets/issues/2115 | 841,283,974 | MDU6SXNzdWU4NDEyODM5NzQ= | 2,115 | The datasets.map() implementation modifies the datatype of os.environ object | {
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} | [] | closed | false | null | [] | [] | 2021-03-25T20:29:19 | 2021-03-26T15:13:52 | 2021-03-26T15:13:52 | NONE | null | null | null | null | In our testing, we noticed that the datasets.map() implementation is modifying the datatype of python os.environ object from '_Environ' to 'dict'.
This causes following function calls to fail as follows:
`
x = os.environ.get("TEST_ENV_VARIABLE_AFTER_dataset_map", default=None)
TypeError: get() takes no keyword arguments
`
It looks like the following line in datasets.map implementation introduced this functionality.
https://github.com/huggingface/datasets/blob/0cb1ac06acb0df44a1cf4128d03a01865faa2504/src/datasets/arrow_dataset.py#L1421
Here is the test script to reproduce this error.
```
from datasets import load_dataset
from transformers import AutoTokenizer
import os
def test_train():
model_checkpoint = "distilgpt2"
datasets = load_dataset('wikitext', 'wikitext-2-raw-v1')
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)
tokenizer.pad_token = tokenizer.eos_token
def tokenize_function(examples):
y = tokenizer(examples['text'], truncation=True, max_length=64)
return y
x = os.environ.get("TEST_ENV_VARIABLE_BEFORE_dataset_map", default=None)
print(f"Testing environment variable: TEST_ENV_VARIABLE_BEFORE_dataset_map {x}")
print(f"Data type of os.environ before datasets.map = {os.environ.__class__.__name__}")
datasets.map(tokenize_function, batched=True, num_proc=2, remove_columns=["text"])
print(f"Data type of os.environ after datasets.map = {os.environ.__class__.__name__}")
x = os.environ.get("TEST_ENV_VARIABLE_AFTER_dataset_map", default=None)
print(f"Testing environment variable: TEST_ENV_VARIABLE_AFTER_dataset_map {x}")
if __name__ == "__main__":
test_train()
```
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https://api.github.com/repos/huggingface/datasets/issues/2108 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2108/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2108/comments | https://api.github.com/repos/huggingface/datasets/issues/2108/events | https://github.com/huggingface/datasets/issues/2108 | 840,181,055 | MDU6SXNzdWU4NDAxODEwNTU= | 2,108 | Is there a way to use a GPU only when training an Index in the process of add_faisis_index? | {
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] | open | false | null | [] | [] | 2021-03-24T21:32:16 | 2021-03-25T06:31:43 | null | NONE | null | null | null | null | Motivation - Some FAISS indexes like IVF consist of the training step that clusters the dataset into a given number of indexes. It would be nice if we can use a GPU to do the training step and covert the index back to CPU as mention in [this faiss example](https://gist.github.com/mdouze/46d6bbbaabca0b9778fca37ed2bcccf6). | null | {
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https://api.github.com/repos/huggingface/datasets/issues/2106 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2106/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2106/comments | https://api.github.com/repos/huggingface/datasets/issues/2106/events | https://github.com/huggingface/datasets/issues/2106 | 839,084,264 | MDU6SXNzdWU4MzkwODQyNjQ= | 2,106 | WMT19 Dataset for Kazakh-English is not formatted correctly | {
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"id": 2067388877,
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] | open | false | null | [] | [
"Hi ! Thanks for reporting\r\n\r\nBy looking at the raw `news-commentary-v14.en-kk.tsv` file, it looks like there are at least 17 lines with this issue.\r\nMoreover these issues are not always the same:\r\n- L97 is only `kk` text and must be appended at the end of the `kk` text of the **next** line\r\n- L2897 is on... | 2021-03-23T20:14:47 | 2021-03-25T21:36:20 | null | NONE | null | null | null | null | In addition to the bug of languages being switched from Issue @415, there are incorrect translations in the dataset because the English-Kazakh translations have a one off formatting error.
The News Commentary v14 parallel data set for kk-en from http://www.statmt.org/wmt19/translation-task.html has a bug here:
> Line 94. The Swiss National Bank, for its part, has been battling with the deflationary effects of the franc’s dramatic appreciation over the past few years. Швейцарияның Ұлттық банкі өз тарапынан, соңғы бірнеше жыл ішінде франк құнының қатты өсуінің дефляциялық әсерімен күресіп келеді.
>
> Line 95. Дефляциялық күштер 2008 жылы терең және ұзаққа созылған жаһандық дағдарысқа байланысты орын алған ірі экономикалық және қаржылық орын алмасулардың арқасында босатылды. Жеке қарыз қаражаты үлесінің қысқаруы орталық банктің рефляцияға жұмсалған күш-жігеріне тұрақты соққан қарсы желдей болды.
>
> Line 96. The deflationary forces were unleashed by the major economic and financial dislocations associated with the deep and protracted global crisis that erupted in 2008. Private deleveraging became a steady headwind to central bank efforts to reflate. 2009 жылы, алдыңғы қатарлы экономикалардың шамамен үштен бірі бағаның төмендеуін көрсетті, бұл соғыстан кейінгі жоғары деңгей болды.
As you can see, line 95 has only the Kazakh translation which should be part of line 96. This causes all of the following English-Kazakh translation pairs to be one off rendering ALL of those translations incorrect. This issue was not fixed when the dataset was imported to Huggingface. By running this code
```
import datasets
from datasets import load_dataset
dataset = load_dataset('wmt19', 'kk-en')
for key in dataset['train']['translation']:
if 'The deflationary forces were unleashed by the major economic and financial dislocations associated with the deep and protracted global crisis that erupted in 2008.' in key['kk']:
print(key['en'])
print(key['kk'])
break
```
we get:
> 2009 жылы, алдыңғы қатарлы экономикалардың шамамен үштен бірі бағаның төмендеуін көрсетті, бұл соғыстан кейінгі жоғары деңгей болды.
> The deflationary forces were unleashed by the major economic and financial dislocations associated with the deep and protracted global crisis that erupted in 2008. Private deleveraging became a steady headwind to central bank efforts to reflate.
which shows that the issue still persists in the Huggingface dataset. The Kazakh sentence matches up to the next English sentence in the dataset instead of the current one.
Please let me know if there's you have any ideas to fix this one-off error from the dataset or if this can be fixed by Huggingface. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/2105 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2105/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2105/comments | https://api.github.com/repos/huggingface/datasets/issues/2105/events | https://github.com/huggingface/datasets/issues/2105 | 839,059,226 | MDU6SXNzdWU4MzkwNTkyMjY= | 2,105 | Request to remove S2ORC dataset | {
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"Hello @kyleclo! Currently, we are getting the data from your bucket, so if you remove it the HF script won't work anymore :) \r\n\r\nUntil you solve things on your end, @lhoestq suggested we just return a warning message when people try to load that dataset from HF. What would you like it to say?",
"Hi @kyleclo,... | 2021-03-23T19:43:06 | 2021-08-04T19:18:02 | null | NONE | null | null | null | null | Hi! I was wondering if it's possible to remove [S2ORC](https://huggingface.co/datasets/s2orc) from hosting on Huggingface's platform? Unfortunately, there are some legal considerations about how we make this data available. Happy to add back to Huggingface's platform once we work out those hurdles! Thanks! | null | {
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https://api.github.com/repos/huggingface/datasets/issues/2104 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2104/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2104/comments | https://api.github.com/repos/huggingface/datasets/issues/2104/events | https://github.com/huggingface/datasets/issues/2104 | 839,027,834 | MDU6SXNzdWU4MzkwMjc4MzQ= | 2,104 | Trouble loading wiki_movies | {
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"Hi ! `wiki_movies` was added in `datasets==1.2.0`. However it looks like you have `datasets==1.1.2`.\r\n\r\nTo use `wiki_movies`, please update `datasets` with\r\n```\r\npip install --upgrade datasets\r\n```",
"Thanks a lot! That solved it and I was able to upload a model trained on it as well :)"
] | 2021-03-23T18:59:54 | 2022-03-30T08:22:58 | 2022-03-30T08:22:58 | NONE | null | null | null | null | Hello,
I am trying to load_dataset("wiki_movies") and it gives me this error -
`FileNotFoundError: Couldn't find file locally at wiki_movies/wiki_movies.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.1.2/datasets/wiki_movies/wiki_movies.py or https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets/wiki_movies/wiki_movies.py`
Trying to do `python run_mlm.py \
--model_name_or_path roberta-base \
--dataset_name wiki_movies \` also gives the same error.
Is this something on my end? From what I can tell, this dataset was re-added by @lhoestq a few months ago.
Thank you! | {
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https://api.github.com/repos/huggingface/datasets/issues/2103 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2103/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2103/comments | https://api.github.com/repos/huggingface/datasets/issues/2103/events | https://github.com/huggingface/datasets/issues/2103 | 838,946,916 | MDU6SXNzdWU4Mzg5NDY5MTY= | 2,103 | citation, homepage, and license fields of `dataset_info.json` are duplicated many times | {
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"Thanks for reporting :)\r\nMaybe we can concatenate fields only if they are different.\r\n\r\nCurrently this is done here:\r\n\r\nhttps://github.com/huggingface/nlp/blob/349ac4398a3bcae6356f14c5754483383a60e8a4/src/datasets/info.py#L180-L196\r\n\r\nThis can be a good first contribution to the library.\r\nPlease co... | 2021-03-23T17:18:09 | 2021-04-06T14:39:59 | 2021-04-06T14:39:59 | NONE | null | null | null | null | This happens after a `map` operation when `num_proc` is set to `>1`. I tested this by cleaning up the json before running the `map` op on the dataset so it's unlikely it's coming from an earlier concatenation.
Example result:
```
"citation": "@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n url = {https://dumps.wikimedia.org}\n}\n\n@ONLINE {wikidump,\n author = {Wikimedia Foundation},\n title = {Wikimedia Downloads},\n
```
@lhoestq and I believe this is happening due to the fields being concatenated `num_proc` times. | {
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https://api.github.com/repos/huggingface/datasets/issues/2099 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2099/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2099/comments | https://api.github.com/repos/huggingface/datasets/issues/2099/events | https://github.com/huggingface/datasets/issues/2099 | 838,523,819 | MDU6SXNzdWU4Mzg1MjM4MTk= | 2,099 | load_from_disk takes a long time to load local dataset | {
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"Hi !\r\nCan you share more information about the features of your dataset ? You can get them by printing `my_dataset.features`\r\nCan you also share the code of your `map` function ?",
"It is actually just the tokenized `wikipedia` dataset with `input_ids`, `attention_mask`, etc, with one extra column which is a... | 2021-03-23T09:28:37 | 2021-03-23T17:12:16 | 2021-03-23T17:12:16 | NONE | null | null | null | null | I have an extremely large tokenized dataset (24M examples) that loads in a few minutes. However, after adding a column similar to `input_ids` (basically a list of integers) and saving the dataset to disk, the load time goes to >1 hour. I've even tried using `np.uint8` after seeing #1985 but it doesn't seem to be helping (the total size seems to be smaller though).
Does anyone know what could be the issue? Or does the casting of that column to `int8` need to happen in the function that writes the arrow table instead of in the `map` where I create the list of integers?
Tagging @lhoestq since you seem to be working on these issues and PRs :) | {
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"Hi ! This is 1.1 as specified by the download urls here:\r\n\r\nhttps://github.com/huggingface/nlp/blob/349ac4398a3bcae6356f14c5754483383a60e8a4/datasets/squad/squad.py#L50-L55",
"Got it. Thank you~"
] | 2021-03-23T07:47:54 | 2021-03-26T09:48:54 | 2021-03-26T09:48:54 | NONE | null | null | null | null | Hi~
I want train on squad dataset. What's the version of the squad? Is it 1.1 or 1.0? I'm new in QA, I don't find some descriptions about it. | {
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https://api.github.com/repos/huggingface/datasets/issues/2096 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2096/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2096/comments | https://api.github.com/repos/huggingface/datasets/issues/2096/events | https://github.com/huggingface/datasets/issues/2096 | 838,038,379 | MDU6SXNzdWU4MzgwMzgzNzk= | 2,096 | CoNLL 2003 dataset not including German | {
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"Hello. I've been looking for information about German Conll2003 and found your question. Official site (https://www.clips.uantwerpen.be/conll2003/ner/) mentions that organizers provide only annotation. German texts (ECI Multilingual Text Corpus) are not freely available and can be ordered from the Linguistic Data ... | 2021-03-22T19:23:56 | 2023-07-25T16:49:07 | 2023-07-25T16:49:07 | NONE | null | null | null | null | Hello, thanks for all the work on developing and maintaining this amazing platform, which I am enjoying working with!
I was wondering if there is a reason why the German CoNLL 2003 dataset is not included in the [repository](https://github.com/huggingface/datasets/tree/master/datasets/conll2003), since a copy of it could be found in some places on the internet such as GitHub? I could help adding the German data to the hub, unless there are some copyright issues that I am unaware of...
This is considering that many work use the union of CoNLL 2002 and 2003 datasets for comparing cross-lingual NER transfer performance in `en`, `de`, `es`, and `nl`. E.g., [XLM-R](https://www.aclweb.org/anthology/2020.acl-main.747.pdf).
## Adding a Dataset
- **Name:** CoNLL 2003 German
- **Paper:** https://www.aclweb.org/anthology/W03-0419/
- **Data:** https://github.com/huggingface/datasets/tree/master/datasets/conll2003
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https://api.github.com/repos/huggingface/datasets/issues/2092 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2092/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2092/comments | https://api.github.com/repos/huggingface/datasets/issues/2092/events | https://github.com/huggingface/datasets/issues/2092 | 836,984,043 | MDU6SXNzdWU4MzY5ODQwNDM= | 2,092 | How to disable making arrow tables in load_dataset ? | {
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"Hi ! We plan to add streaming features in the future.\r\n\r\nThis should allow to load a dataset instantaneously without generating the arrow table. The trade-off is that accessing examples from a streaming dataset must be done in an iterative way, and with an additional (but hopefully minor) overhead.\r\nWhat do ... | 2021-03-21T04:50:07 | 2022-06-01T16:49:52 | 2022-06-01T16:49:52 | NONE | null | null | null | null | Is there a way to disable the construction of arrow tables, or to make them on the fly as the dataset is being used ? | {
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https://api.github.com/repos/huggingface/datasets/issues/2089 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2089/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2089/comments | https://api.github.com/repos/huggingface/datasets/issues/2089/events | https://github.com/huggingface/datasets/issues/2089 | 836,788,019 | MDU6SXNzdWU4MzY3ODgwMTk= | 2,089 | Add documentaton for dataset README.md files | {
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"Hi ! We are using the [datasets-tagging app](https://github.com/huggingface/datasets-tagging) to select the tags to add.\r\n\r\nWe are also adding the full list of tags in #2107 \r\nThis covers multilinguality, language_creators, licenses, size_categories and task_categories.\r\n\r\nIn general if you want to add a... | 2021-03-20T11:44:38 | 2023-07-25T16:45:38 | 2023-07-25T16:45:37 | CONTRIBUTOR | null | null | null | null | Hi,
the dataset README files have special headers.
Somehow a documenation of the allowed values and tags is missing.
Could you add that?
Just to give some concrete questions that should be answered imo:
- which values can be passted to multilinguality?
- what should be passed to language_creators?
- which values should licenses have? What do I say when it is a custom license? Should I add a link?
- how should I choose size_categories ? What are valid ranges?
- what are valid task_categories?
Thanks
Philip | {
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https://api.github.com/repos/huggingface/datasets/issues/2084 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2084/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2084/comments | https://api.github.com/repos/huggingface/datasets/issues/2084/events | https://github.com/huggingface/datasets/issues/2084 | 835,750,671 | MDU6SXNzdWU4MzU3NTA2NzE= | 2,084 | CUAD - Contract Understanding Atticus Dataset | {
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"+1 on this request"
] | 2021-03-19T09:27:43 | 2021-04-16T08:50:44 | 2021-04-16T08:50:44 | CONTRIBUTOR | null | null | null | null | ## Adding a Dataset
- **Name:** CUAD - Contract Understanding Atticus Dataset
- **Description:** As one of the only large, specialized NLP benchmarks annotated by experts, CUAD can serve as a challenging research benchmark for the broader NLP community.
- **Paper:** https://arxiv.org/abs/2103.06268
- **Data:** https://github.com/TheAtticusProject/cuad/
- **Motivation:** good domain specific datasets are valuable
Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).
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"Hi,\r\n\r\nthis bug is related to `Dataset.{remove_columns, rename_column, flatten}` not propagating the change to the schema metadata when the info features are updated, so this line is the culprit:\r\n```python\r\ncommon_voice_train = common_voice_train.remove_columns(['client_id', 'up_votes', 'down_votes', 'age... | 2021-03-19T08:29:48 | 2021-04-09T09:25:33 | 2021-04-09T09:25:33 | CONTRIBUTOR | null | null | null | null | Hey,
I played around with the `concatenate_datasets(...)` function: https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=concatenate_datasets#datasets.concatenate_datasets
and noticed that when the order in which the datasets are concatenated changes an error is thrown where it should not IMO.
Here is a google colab to reproduce the error: https://colab.research.google.com/drive/17VTFU4KQ735-waWZJjeOHS6yDTfV5ekK?usp=sharing | {
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"Hi !\r\n\r\nThis is actually supported ! but not yet in `from_pandas`.\r\nYou can use `from_dict` for now instead:\r\n```python\r\nfrom datasets import Dataset, Array2D, Features, Value\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\ndataset = {\r\n 'bbox': [\r\n np.array([[1,2,3,4],[1,2,3,4],[1,2,3,... | 2021-03-18T16:29:14 | 2021-03-25T12:46:53 | 2021-03-25T12:46:53 | NONE | null | null | null | null | Hi,
I'm trying to put together a `datasets.Dataset` to be used with LayoutLM which is available in `transformers`. This model requires as input the bounding boxes of each of the token of a sequence. This is when I realized that `Dataset` does not support multi-dimensional arrays as a value for a column in a row.
The following code results in conversion error in pyarrow (`pyarrow.lib.ArrowInvalid: ('Can only convert 1-dimensional array values', 'Conversion failed for column bbox with type object')`)
```
from datasets import Dataset
import pandas as pd
import numpy as np
dataset = pd.DataFrame({
'bbox': [
np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),
np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),
np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]]),
np.array([[1,2,3,4],[1,2,3,4],[1,2,3,4]])
],
'input_ids': [1, 2, 3, 4]
})
dataset = Dataset.from_pandas(dataset)
```
Since I wanted to use pytorch for the downstream training task, I also tried a few ways to directly put in a column of 2-D pytorch tensor in a formatted dataset, but I can only have a list of 1-D tensors, or a list of arrays, or a list of lists.
```
import torch
from datasets import Dataset
import pandas as pd
dataset = pd.DataFrame({
'bbox': [
[[1,2,3,4],[1,2,3,4],[1,2,3,4]],
[[1,2,3,4],[1,2,3,4],[1,2,3,4]],
[[1,2,3,4],[1,2,3,4],[1,2,3,4]],
[[1,2,3,4],[1,2,3,4],[1,2,3,4]]
],
'input_ids': [1, 2, 3, 4]
})
dataset = Dataset.from_pandas(dataset)
def test(examples):
return {'bbbox': torch.Tensor(examples['bbox'])}
dataset = dataset.map(test)
print(dataset[0]['bbox'])
print(dataset[0]['bbbox'])
dataset.set_format(type='torch', columns=['input_ids', 'bbox'], output_all_columns=True)
print(dataset[0]['bbox'])
print(dataset[0]['bbbox'])
def test2(examples):
return {'bbbox': torch.stack(examples['bbox'])}
dataset = dataset.map(test2)
print(dataset[0]['bbox'])
print(dataset[0]['bbbox'])
```
Is is possible to support n-D arrays/tensors in datasets?
It seems that it can also be useful for this [feature request](https://github.com/huggingface/datasets/issues/263). | {
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https://api.github.com/repos/huggingface/datasets/issues/2078 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2078/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2078/comments | https://api.github.com/repos/huggingface/datasets/issues/2078/events | https://github.com/huggingface/datasets/issues/2078 | 834,694,819 | MDU6SXNzdWU4MzQ2OTQ4MTk= | 2,078 | MemoryError when computing WER metric | {
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"Hi ! Thanks for reporting.\r\nWe're indeed using `jiwer` to compute the WER.\r\n\r\nMaybe instead of calling `jiwer.wer` once for all the preditions/references we can compute the WER iteratively to avoid memory issues ? I'm not too familial with `jiwer` but this must be possible.\r\n\r\nCurrently the code to compu... | 2021-03-18T11:30:05 | 2021-05-01T08:31:49 | 2021-04-06T07:20:43 | NONE | null | null | null | null | Hi, I'm trying to follow the ASR example to try Wav2Vec. This is the code that I use for WER calculation:
```
wer = load_metric("wer")
print(wer.compute(predictions=result["predicted"], references=result["target"]))
```
However, I receive the following exception:
`Traceback (most recent call last):
File "/home/diego/IpGlobal/wav2vec/test_wav2vec.py", line 51, in <module>
print(wer.compute(predictions=result["predicted"], references=result["target"]))
File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/datasets/metric.py", line 403, in compute
output = self._compute(predictions=predictions, references=references, **kwargs)
File "/home/diego/.cache/huggingface/modules/datasets_modules/metrics/wer/73b2d32b723b7fb8f204d785c00980ae4d937f12a65466f8fdf78706e2951281/wer.py", line 94, in _compute
return wer(references, predictions)
File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 81, in wer
truth, hypothesis, truth_transform, hypothesis_transform, **kwargs
File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 192, in compute_measures
H, S, D, I = _get_operation_counts(truth, hypothesis)
File "/home/diego/miniconda3/envs/wav2vec3.6/lib/python3.6/site-packages/jiwer/measures.py", line 273, in _get_operation_counts
editops = Levenshtein.editops(source_string, destination_string)
MemoryError`
My system has more than 10GB of available RAM. Looking at the code, I think that it could be related to the way jiwer does the calculation, as it is pasting all the sentences in a single string before calling Levenshtein editops function.
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https://api.github.com/repos/huggingface/datasets/issues/2076 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2076/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2076/comments | https://api.github.com/repos/huggingface/datasets/issues/2076/events | https://github.com/huggingface/datasets/issues/2076 | 834,445,296 | MDU6SXNzdWU4MzQ0NDUyOTY= | 2,076 | Issue: Dataset download error | {
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"Hi @XuhuiZhou, thanks for reporting this issue. \r\n\r\nIndeed, the old links are no longer valid (404 Not Found error), and the script must be updated with the new links to Google Drive.",
"It would be nice to update the urls indeed !\r\n\r\nTo do this, you just need to replace the urls in `iwslt2017.py` and th... | 2021-03-18T06:36:06 | 2021-03-22T11:52:31 | null | NONE | null | null | null | null | The download link in `iwslt2017.py` file does not seem to work anymore.
For example, `FileNotFoundError: Couldn't find file at https://wit3.fbk.eu/archive/2017-01-trnted/texts/zh/en/zh-en.tgz`
Would be nice if we could modify it script and use the new downloadable link? | null | {
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https://api.github.com/repos/huggingface/datasets/issues/2075 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2075/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2075/comments | https://api.github.com/repos/huggingface/datasets/issues/2075/events | https://github.com/huggingface/datasets/issues/2075 | 834,301,246 | MDU6SXNzdWU4MzQzMDEyNDY= | 2,075 | ConnectionError: Couldn't reach common_voice.py | {
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"Hi @LifaSun, thanks for reporting this issue.\r\n\r\nSometimes, GitHub has some connectivity problems. Could you confirm that the problem persists?",
"@albertvillanova Thanks! It works well now. "
] | 2021-03-18T01:19:06 | 2021-03-20T10:29:41 | 2021-03-20T10:29:41 | NONE | null | null | null | null | When I run:
from datasets import load_dataset, load_metric
common_voice_train = load_dataset("common_voice", "zh-CN", split="train+validation")
common_voice_test = load_dataset("common_voice", "zh-CN", split="test")
Got:
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/master/datasets/common_voice/common_voice.py
Version:
1.4.1
Thanks! @lhoestq @LysandreJik @thomwolf | {
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https://api.github.com/repos/huggingface/datasets/issues/2071 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2071/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2071/comments | https://api.github.com/repos/huggingface/datasets/issues/2071/events | https://github.com/huggingface/datasets/issues/2071 | 833,950,824 | MDU6SXNzdWU4MzM5NTA4MjQ= | 2,071 | Multiprocessing is slower than single process | {
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"dupe of #1992"
] | 2021-03-17T16:08:58 | 2021-03-18T09:10:23 | 2021-03-18T09:10:23 | CONTRIBUTOR | null | null | null | null | ```python
# benchmark_filter.py
import logging
import sys
import time
from datasets import load_dataset, set_caching_enabled
if __name__ == "__main__":
set_caching_enabled(False)
logging.basicConfig(level=logging.DEBUG)
bc = load_dataset("bookcorpus")
now = time.time()
try:
bc["train"].filter(lambda x: len(x["text"]) < 64, num_proc=int(sys.argv[1]))
except Exception as e:
print(f"cancelled: {e}")
elapsed = time.time() - now
print(elapsed)
```
Running `python benchmark_filter.py 1` (20min+) is faster than `python benchmark_filter.py 2` (2hrs+) | {
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https://api.github.com/repos/huggingface/datasets/issues/2070 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2070/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2070/comments | https://api.github.com/repos/huggingface/datasets/issues/2070/events | https://github.com/huggingface/datasets/issues/2070 | 833,799,035 | MDU6SXNzdWU4MzM3OTkwMzU= | 2,070 | ArrowInvalid issue for squad v2 dataset | {
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"Hi ! This error happens when you use `map` in batched mode and then your function doesn't return the same number of values per column.\r\n\r\nIndeed since you're using `map` in batched mode, `prepare_validation_features` must take a batch as input (i.e. a dictionary of multiple rows of the dataset), and return a b... | 2021-03-17T13:51:49 | 2021-08-04T17:57:16 | 2021-08-04T17:57:16 | NONE | null | null | null | null | Hello, I am using the huggingface official question answering example notebook (https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb).
In the prepare_validation_features function, I made some modifications to tokenize a new set of quesions with the original contexts and save them in three different list called candidate_input_dis, candidate_attetion_mask and candidate_token_type_ids. When I try to run the next cell for dataset.map, I got the following error:
`ArrowInvalid: Column 1 named candidate_attention_mask expected length 1180 but got length 1178`
My code is as follows:
```
def generate_candidate_questions(examples):
val_questions = examples["question"]
candididate_questions = random.sample(datasets["train"]["question"], len(val_questions))
candididate_questions = [x[:max_length] for x in candididate_questions]
return candididate_questions
def prepare_validation_features(examples, use_mixing=False):
pad_on_right = tokenizer.padding_side == "right"
tokenized_examples = tokenizer(
examples["question" if pad_on_right else "context"],
examples["context" if pad_on_right else "question"],
truncation="only_second" if pad_on_right else "only_first",
max_length=max_length,
stride=doc_stride,
return_overflowing_tokens=True,
return_offsets_mapping=True,
padding="max_length",
)
if use_mixing:
candidate_questions = generate_candidate_questions(examples)
tokenized_candidates = tokenizer(
candidate_questions if pad_on_right else examples["context"],
examples["context"] if pad_on_right else candidate_questions,
truncation="only_second" if pad_on_right else "only_first",
max_length=max_length,
stride=doc_stride,
return_overflowing_tokens=True,
return_offsets_mapping=True,
padding="max_length",
)
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
tokenized_examples["example_id"] = []
if use_mixing:
tokenized_examples["candidate_input_ids"] = tokenized_candidates["input_ids"]
tokenized_examples["candidate_attention_mask"] = tokenized_candidates["attention_mask"]
tokenized_examples["candidate_token_type_ids"] = tokenized_candidates["token_type_ids"]
for i in range(len(tokenized_examples["input_ids"])):
sequence_ids = tokenized_examples.sequence_ids(i)
context_index = 1 if pad_on_right else 0
sample_index = sample_mapping[i]
tokenized_examples["example_id"].append(examples["id"][sample_index])
tokenized_examples["offset_mapping"][i] = [
(o if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples["offset_mapping"][i])
]
return tokenized_examples
validation_features = datasets["validation"].map(
lambda xs: prepare_validation_features(xs, True),
batched=True,
remove_columns=datasets["validation"].column_names
)
```
I guess this might happen because of the batched=True. I see similar issues in this repo related to arrow table length mismatch error, but in their cases, the numbers vary a lot. In my case, this error always happens when the expected length and unexpected length are very close. Thanks for the help! | {
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https://api.github.com/repos/huggingface/datasets/issues/2068 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2068/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2068/comments | https://api.github.com/repos/huggingface/datasets/issues/2068/events | https://github.com/huggingface/datasets/issues/2068 | 833,602,832 | MDU6SXNzdWU4MzM2MDI4MzI= | 2,068 | PyTorch not available error on SageMaker GPU docker though it is installed | {
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"cc @philschmid ",
"Hey @sivakhno,\r\n\r\nhow does your `requirements.txt` look like to install the `datasets` library and which version of it are you running? Can you try to install `datasets>=1.4.0`",
"Hi @philschmid - thanks for suggestion. I am using `datasets==1.4.1`. \r\nI have also tried using `torch=1.6... | 2021-03-17T10:04:27 | 2021-06-14T04:47:30 | 2021-06-14T04:47:30 | NONE | null | null | null | null | I get en error when running data loading using SageMaker SDK
```
File "main.py", line 34, in <module>
run_training()
File "main.py", line 25, in run_training
dm.setup('fit')
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/core/datamodule.py", line 92, in wrapped_fn
return fn(*args, **kwargs)
File "/opt/ml/code/data_module.py", line 103, in setup
self.dataset[split].set_format(type="torch", columns=self.columns)
File "/opt/conda/lib/python3.6/site-packages/datasets/fingerprint.py", line 337, in wrapper
out = func(self, *args, **kwargs)
File "/opt/conda/lib/python3.6/site-packages/datasets/arrow_dataset.py", line 995, in set_format
_ = get_formatter(type, **format_kwargs)
File "/opt/conda/lib/python3.6/site-packages/datasets/formatting/__init__.py", line 114, in get_formatter
raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
ValueError: PyTorch needs to be installed to be able to return PyTorch tensors.
```
when trying to execute dataset loading using this notebook https://github.com/PyTorchLightning/pytorch-lightning/blob/master/notebooks/04-transformers-text-classification.ipynb, specifically lines
```
self.columns = [c for c in self.dataset[split].column_names if c in self.loader_columns]
self.dataset[split].set_format(type="torch", columns=self.columns)
```
The SageMaker docker image used is 763104351884.dkr.ecr.eu-central-1.amazonaws.com/pytorch-training:1.4.0-gpu-py3 .
By running container interactively I have checked that torch loading completes successfully by executing `https://github.com/huggingface/datasets/blob/master/src/datasets/config.py#L39`.
Also as a first line in the data loading module I have
```
import os
os.environ["USE_TF"] = "0"
os.environ["USE_TORCH"] = "1"
````
But unfortunately the error stills persists. Any suggestions would be appreciated as I am stack.
Many Thanks!
| {
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https://api.github.com/repos/huggingface/datasets/issues/2067 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2067/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2067/comments | https://api.github.com/repos/huggingface/datasets/issues/2067/events | https://github.com/huggingface/datasets/issues/2067 | 833,559,940 | MDU6SXNzdWU4MzM1NTk5NDA= | 2,067 | Multiprocessing windows error | {
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"Hi ! Thanks for reporting.\r\nThis looks like a bug, could you try to provide a minimal code example that reproduces the issue ? This would be very helpful !\r\n\r\nOtherwise I can try to run the wav2vec2 code above on my side but probably not this week..",
"```\r\nfrom datasets import load_dataset\r\n\r\ndatase... | 2021-03-17T09:12:28 | 2021-08-04T17:59:08 | 2021-08-04T17:59:08 | CONTRIBUTOR | null | null | null | null | As described here https://huggingface.co/blog/fine-tune-xlsr-wav2vec2
When using the num_proc argument on windows the whole Python environment crashes and hanging in loop.
For example at the map_to_array part.
An error occures because the cache file already exists and windows throws and error. After this the log crashes into an loop | {
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https://api.github.com/repos/huggingface/datasets/issues/2065 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2065/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2065/comments | https://api.github.com/repos/huggingface/datasets/issues/2065/events | https://github.com/huggingface/datasets/issues/2065 | 833,291,432 | MDU6SXNzdWU4MzMyOTE0MzI= | 2,065 | Only user permission of saved cache files, not group | {
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"default": true... | closed | false | null | [] | [
"Hi ! Thanks for reporting.\r\n\r\nCurrently there's no way to specify this.\r\n\r\nWhen loading/processing a dataset, the arrow file is written using a temporary file. Then once writing is finished, it's moved to the cache directory (using `shutil.move` [here](https://github.com/huggingface/datasets/blob/f6b8251eb... | 2021-03-17T00:20:22 | 2023-03-31T12:17:06 | 2021-05-10T06:45:29 | NONE | null | null | null | null | Hello,
It seems when a cached file is saved from calling `dataset.map` for preprocessing, it gets the user permissions and none of the user's group permissions. As we share data files across members of our team, this is causing a bit of an issue as we have to continually reset the permission of the files. Do you know any ways around this or a way to correctly set the permissions? | {
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https://api.github.com/repos/huggingface/datasets/issues/2061 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2061/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2061/comments | https://api.github.com/repos/huggingface/datasets/issues/2061/events | https://github.com/huggingface/datasets/issues/2061 | 832,596,228 | MDU6SXNzdWU4MzI1OTYyMjg= | 2,061 | Cannot load udpos subsets from xtreme dataset using load_dataset() | {
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"@lhoestq Adding \"_\" to the class labels in the dataset script will fix the issue.\r\n\r\nThe bigger issue IMO is that the data files are in conll format, but the examples are tokens, not sentences.",
"Hi ! Thanks for reporting @adzcodez \r\n\r\n\r\n> @lhoestq Adding \"_\" to the class labels in the dataset scr... | 2021-03-16T09:32:13 | 2021-06-18T11:54:11 | 2021-06-18T11:54:10 | NONE | null | null | null | null | Hello,
I am trying to load the udpos English subset from xtreme dataset, but this faces an error during loading. I am using datasets v1.4.1, pip install. I have tried with other udpos languages which also fail, though loading a different subset altogether (such as XNLI) has no issue. I have also tried on Colab and faced the same error.
Reprex is:
`from datasets import load_dataset `
`dataset = load_dataset('xtreme', 'udpos.English')`
The error is:
`KeyError: '_'`
The full traceback is:
KeyError Traceback (most recent call last)
<ipython-input-5-7181359ea09d> in <module>
1 from datasets import load_dataset
----> 2 dataset = load_dataset('xtreme', 'udpos.English')
~\Anaconda3\envs\mlenv\lib\site-packages\datasets\load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs)
738
739 # Download and prepare data
--> 740 builder_instance.download_and_prepare(
741 download_config=download_config,
742 download_mode=download_mode,
~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, **download_and_prepare_kwargs)
576 logger.warning("HF google storage unreachable. Downloading and preparing it from source")
577 if not downloaded_from_gcs:
--> 578 self._download_and_prepare(
579 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
580 )
~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
654 try:
655 # Prepare split will record examples associated to the split
--> 656 self._prepare_split(split_generator, **prepare_split_kwargs)
657 except OSError as e:
658 raise OSError(
~\Anaconda3\envs\mlenv\lib\site-packages\datasets\builder.py in _prepare_split(self, split_generator)
977 generator, unit=" examples", total=split_info.num_examples, leave=False, disable=not_verbose
978 ):
--> 979 example = self.info.features.encode_example(record)
980 writer.write(example)
981 finally:
~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_example(self, example)
946 def encode_example(self, example):
947 example = cast_to_python_objects(example)
--> 948 return encode_nested_example(self, example)
949
950 def encode_batch(self, batch):
~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_nested_example(schema, obj)
840 # Nested structures: we allow dict, list/tuples, sequences
841 if isinstance(schema, dict):
--> 842 return {
843 k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)
844 }
~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in <dictcomp>(.0)
841 if isinstance(schema, dict):
842 return {
--> 843 k: encode_nested_example(sub_schema, sub_obj) for k, (sub_schema, sub_obj) in utils.zip_dict(schema, obj)
844 }
845 elif isinstance(schema, (list, tuple)):
~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_nested_example(schema, obj)
868 # ClassLabel will convert from string to int, TranslationVariableLanguages does some checks
869 elif isinstance(schema, (ClassLabel, TranslationVariableLanguages, Value, _ArrayXD)):
--> 870 return schema.encode_example(obj)
871 # Other object should be directly convertible to a native Arrow type (like Translation and Translation)
872 return obj
~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in encode_example(self, example_data)
647 # If a string is given, convert to associated integer
648 if isinstance(example_data, str):
--> 649 example_data = self.str2int(example_data)
650
651 # Allowing -1 to mean no label.
~\Anaconda3\envs\mlenv\lib\site-packages\datasets\features.py in str2int(self, values)
605 if value not in self._str2int:
606 value = value.strip()
--> 607 output.append(self._str2int[str(value)])
608 else:
609 # No names provided, try to integerize
KeyError: '_'
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https://api.github.com/repos/huggingface/datasets/issues/2059 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2059/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2059/comments | https://api.github.com/repos/huggingface/datasets/issues/2059/events | https://github.com/huggingface/datasets/issues/2059 | 832,579,156 | MDU6SXNzdWU4MzI1NzkxNTY= | 2,059 | Error while following docs to load the `ted_talks_iwslt` dataset | {
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"@skyprince999 as you authored the PR for this dataset, any comments?",
"This has been fixed in #2064 by @mariosasko (thanks again !)\r\n\r\nThe fix is available on the master branch and we'll do a new release very soon :)"
] | 2021-03-16T09:12:19 | 2021-03-16T18:00:31 | 2021-03-16T18:00:07 | NONE | null | null | null | null | I am currently trying to load the `ted_talks_iwslt` dataset into google colab.
The [docs](https://huggingface.co/datasets/ted_talks_iwslt) mention the following way of doing so.
```python
dataset = load_dataset("ted_talks_iwslt", language_pair=("it", "pl"), year="2014")
```
Executing it results in the error attached below.
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-6-7dcc67154ef9> in <module>()
----> 1 dataset = load_dataset("ted_talks_iwslt", language_pair=("it", "pl"), year="2014")
4 frames
/usr/local/lib/python3.7/dist-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, script_version, use_auth_token, **config_kwargs)
730 hash=hash,
731 features=features,
--> 732 **config_kwargs,
733 )
734
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in __init__(self, writer_batch_size, *args, **kwargs)
927
928 def __init__(self, *args, writer_batch_size=None, **kwargs):
--> 929 super(GeneratorBasedBuilder, self).__init__(*args, **kwargs)
930 # Batch size used by the ArrowWriter
931 # It defines the number of samples that are kept in memory before writing them
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in __init__(self, cache_dir, name, hash, features, **config_kwargs)
241 name,
242 custom_features=features,
--> 243 **config_kwargs,
244 )
245
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _create_builder_config(self, name, custom_features, **config_kwargs)
337 if "version" not in config_kwargs and hasattr(self, "VERSION") and self.VERSION:
338 config_kwargs["version"] = self.VERSION
--> 339 builder_config = self.BUILDER_CONFIG_CLASS(**config_kwargs)
340
341 # otherwise use the config_kwargs to overwrite the attributes
/root/.cache/huggingface/modules/datasets_modules/datasets/ted_talks_iwslt/024d06b1376b361e59245c5878ab8acf9a7576d765f2d0077f61751158e60914/ted_talks_iwslt.py in __init__(self, language_pair, year, **kwargs)
219 description=description,
220 version=datasets.Version("1.1.0", ""),
--> 221 **kwargs,
222 )
223
TypeError: __init__() got multiple values for keyword argument 'version'
```
How to resolve this?
PS: Thanks a lot @huggingface team for creating this great library! | {
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https://api.github.com/repos/huggingface/datasets/issues/2058 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2058/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2058/comments | https://api.github.com/repos/huggingface/datasets/issues/2058/events | https://github.com/huggingface/datasets/issues/2058 | 832,159,844 | MDU6SXNzdWU4MzIxNTk4NDQ= | 2,058 | Is it possible to convert a `tfds` to HuggingFace `dataset`? | {
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"Hi! You can either save the TF dataset to one of the formats supported by datasets (`parquet`, `csv`, `json`, ...) or pass a generator function to `Dataset.from_generator` that yields its examples."
] | 2021-03-15T20:18:47 | 2023-07-25T16:47:40 | 2023-07-25T16:47:40 | CONTRIBUTOR | null | null | null | null | I was having some weird bugs with `C4`dataset version of HuggingFace, so I decided to try to download `C4`from `tfds`. I would like to know if it is possible to convert a tfds dataset to HuggingFace dataset format :)
I can also open a new issue reporting the bug I'm receiving with `datasets.load_dataset('c4','en')` in the future if you think that it would be useful.
Thanks!
| {
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https://api.github.com/repos/huggingface/datasets/issues/2056 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2056/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2056/comments | https://api.github.com/repos/huggingface/datasets/issues/2056/events | https://github.com/huggingface/datasets/issues/2056 | 831,718,397 | MDU6SXNzdWU4MzE3MTgzOTc= | 2,056 | issue with opus100/en-fr dataset | {
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"@lhoestq I also deleted the cache and redownload the file and still the same issue, I appreciate any help on this. thanks ",
"Here please find the minimal code to reproduce the issue @lhoestq note this only happens with MT5TokenizerFast\r\n\r\n```\r\nfrom datasets import load_dataset\r\nfrom transformers impor... | 2021-03-15T11:32:42 | 2021-03-16T15:49:00 | 2021-03-16T15:48:59 | NONE | null | null | null | null | Hi
I am running run_mlm.py code of huggingface repo with opus100/fr-en pair, I am getting this error, note that this error occurs for only this pairs and not the other pairs. Any idea why this is occurring? and how I can solve this?
Thanks a lot @lhoestq for your help in advance.
`
thread '<unnamed>' panicked at 'index out of bounds: the len is 617 but the index is 617', /__w/tokenizers/tokenizers/tokenizers/src/tokenizer/normalizer.rs:382:21
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace
63%|██████████████████████████████████████████████████████████▊ | 626/1000 [00:27<00:16, 22.69ba/s]
Traceback (most recent call last):
File "run_mlm.py", line 550, in <module>
main()
File "run_mlm.py", line 412, in main
in zip(data_args.dataset_name, data_args.dataset_config_name)]
File "run_mlm.py", line 411, in <listcomp>
logger) for dataset_name, dataset_config_name\
File "/user/dara/dev/codes/seq2seq/data/tokenize_datasets.py", line 96, in get_tokenized_dataset
load_from_cache_file=not data_args.overwrite_cache,
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/dataset_dict.py", line 448, in map
for k, dataset in self.items()
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/dataset_dict.py", line 448, in <dictcomp>
for k, dataset in self.items()
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1309, in map
update_data=update_data,
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 204, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/fingerprint.py", line 337, in wrapper
out = func(self, *args, **kwargs)
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1574, in _map_single
batch, indices, check_same_num_examples=len(self.list_indexes()) > 0, offset=offset
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/arrow_dataset.py", line 1490, in apply_function_on_filtered_inputs
function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)
File "/user/dara/dev/codes/seq2seq/data/tokenize_datasets.py", line 89, in tokenize_function
return tokenizer(examples[text_column_name], return_special_tokens_mask=True)
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2347, in __call__
**kwargs,
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/transformers/tokenization_utils_base.py", line 2532, in batch_encode_plus
**kwargs,
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/transformers/tokenization_utils_fast.py", line 384, in _batch_encode_plus
is_pretokenized=is_split_into_words,
pyo3_runtime.PanicException: index out of bounds: the len is 617 but the index is 617
` | {
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https://api.github.com/repos/huggingface/datasets/issues/2055 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2055/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2055/comments | https://api.github.com/repos/huggingface/datasets/issues/2055/events | https://github.com/huggingface/datasets/issues/2055 | 831,684,312 | MDU6SXNzdWU4MzE2ODQzMTI= | 2,055 | is there a way to override a dataset object saved with save_to_disk? | {
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"Hi\r\nYou can rename the arrow file and update the name in `state.json`",
"I tried this way, but when there is a mapping process to the dataset, it again uses a random cache name. atm, I am trying to use the following method by setting an exact cache file,\r\n\r\n```\r\n dataset_with_embedding =csv_da... | 2021-03-15T10:50:53 | 2021-03-22T04:06:17 | 2021-03-22T04:06:17 | NONE | null | null | null | null | At the moment when I use save_to_disk, it uses the arbitrary name for the arrow file. Is there a way to override such an object? | {
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https://api.github.com/repos/huggingface/datasets/issues/2054 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2054/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2054/comments | https://api.github.com/repos/huggingface/datasets/issues/2054/events | https://github.com/huggingface/datasets/issues/2054 | 831,597,665 | MDU6SXNzdWU4MzE1OTc2NjU= | 2,054 | Could not find file for ZEST dataset | {
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"id": 2067388877,
"name": "dataset bug",
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"The zest dataset url was changed (allenai/zest#3) and #2057 should resolve this.",
"This has been fixed in #2057 by @matt-peters (thanks again !)\r\n\r\nThe fix is available on the master branch and we'll do a new release very soon :)",
"Thanks @lhoestq and @matt-peters ",
"I am closing this issue since its ... | 2021-03-15T09:11:58 | 2021-05-03T09:30:24 | 2021-05-03T09:30:24 | CONTRIBUTOR | null | null | null | null | I am trying to use zest dataset from Allen AI using below code in colab,
```
!pip install -q datasets
from datasets import load_dataset
dataset = load_dataset("zest")
```
I am getting the following error,
```
Using custom data configuration default
Downloading and preparing dataset zest/default (download: 5.53 MiB, generated: 19.96 MiB, post-processed: Unknown size, total: 25.48 MiB) to /root/.cache/huggingface/datasets/zest/default/0.0.0/1f7a230fbfc964d979bbca0f0130fbab3259fce547ee758ad8aa4f9c9bec6cca...
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
<ipython-input-6-18dbbc1a4b8a> in <module>()
1 from datasets import load_dataset
2
----> 3 dataset = load_dataset("zest")
9 frames
/usr/local/lib/python3.7/dist-packages/datasets/utils/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, local_files_only, use_etag, max_retries, use_auth_token)
612 )
613 elif response is not None and response.status_code == 404:
--> 614 raise FileNotFoundError("Couldn't find file at {}".format(url))
615 _raise_if_offline_mode_is_enabled(f"Tried to reach {url}")
616 raise ConnectionError("Couldn't reach {}".format(url))
FileNotFoundError: Couldn't find file at https://ai2-datasets.s3-us-west-2.amazonaws.com/zest/zest.zip
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2052 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2052/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2052/comments | https://api.github.com/repos/huggingface/datasets/issues/2052/events | https://github.com/huggingface/datasets/issues/2052 | 831,135,704 | MDU6SXNzdWU4MzExMzU3MDQ= | 2,052 | Timit_asr dataset repeats examples | {
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"Hi,\r\n\r\nthis was fixed by #1995, so you can wait for the next release or install the package directly from the master branch with the following command: \r\n```bash\r\npip install git+https://github.com/huggingface/datasets\r\n```",
"Ty!"
] | 2021-03-14T11:43:43 | 2021-03-15T10:37:16 | 2021-03-15T10:37:16 | NONE | null | null | null | null | Summary
When loading timit_asr dataset on datasets 1.4+, every row in the dataset is the same
Steps to reproduce
As an example, on this code there is the text from the training part:
Code snippet:
```
from datasets import load_dataset, load_metric
timit = load_dataset("timit_asr")
timit['train']['text']
#['Would such an act of refusal be useful?',
# 'Would such an act of refusal be useful?',
# 'Would such an act of refusal be useful?',
# 'Would such an act of refusal be useful?',
# 'Would such an act of refusal be useful?',
# 'Would such an act of refusal be useful?',
```
The same behavior happens for other columns
Expected behavior:
Different info on the actual timit_asr dataset
Actual behavior:
When loading timit_asr dataset on datasets 1.4+, every row in the dataset is the same. I've checked datasets 1.3 and the rows are different
Debug info
Streamlit version: (get it with $ streamlit version)
Python version: Python 3.6.12
Using Conda? PipEnv? PyEnv? Pex? Using pip
OS version: Centos-release-7-9.2009.1.el7.centos.x86_64
Additional information
You can check the same behavior on https://huggingface.co/datasets/viewer/?dataset=timit_asr | {
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https://api.github.com/repos/huggingface/datasets/issues/2050 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2050/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2050/comments | https://api.github.com/repos/huggingface/datasets/issues/2050/events | https://github.com/huggingface/datasets/issues/2050 | 831,006,551 | MDU6SXNzdWU4MzEwMDY1NTE= | 2,050 | Build custom dataset to fine-tune Wav2Vec2 | {
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"@lhoestq - We could simply use the \"general\" json dataset for this no? ",
"Sure you can use the json loader\r\n```python\r\ndata_files = {\"train\": \"path/to/your/train_data.json\", \"test\": \"path/to/your/test_data.json\"}\r\ntrain_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\r\n... | 2021-03-13T22:01:10 | 2021-03-15T09:27:28 | 2021-03-15T09:27:28 | NONE | null | null | null | null | Thank you for your recent tutorial on how to finetune Wav2Vec2 on a custom dataset. The example you gave here (https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) was on the CommonVoice dataset. However, what if I want to load my own dataset? I have a manifest (transcript and their audio files) in a JSON file.
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https://api.github.com/repos/huggingface/datasets/issues/2048 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2048/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2048/comments | https://api.github.com/repos/huggingface/datasets/issues/2048/events | https://github.com/huggingface/datasets/issues/2048 | 830,953,431 | MDU6SXNzdWU4MzA5NTM0MzE= | 2,048 | github is not always available - probably need a back up | {
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} | [] | closed | false | null | [] | [] | 2021-03-13T18:03:32 | 2022-04-01T15:27:10 | 2022-04-01T15:27:10 | CONTRIBUTOR | null | null | null | null | Yesterday morning github wasn't working:
```
:/tmp$ wget https://raw.githubusercontent.com/huggingface/datasets/1.4.1/metrics/sacrebleu/sacrebleu.py--2021-03-12 18:35:59-- https://raw.githubusercontent.com/huggingface/datasets/1.4.1/metrics/sacrebleu/sacrebleu.py
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.111.133, 185.199.109.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
HTTP request sent, awaiting response... 500 Internal Server Error
2021-03-12 18:36:11 ERROR 500: Internal Server Error.
```
Suggestion: have a failover system and replicate the data on another system and reach there if gh isn't reachable? perhaps gh can be a master and the replicate a slave - so there is only one true source. | {
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https://api.github.com/repos/huggingface/datasets/issues/2046 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2046/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2046/comments | https://api.github.com/repos/huggingface/datasets/issues/2046/events | https://github.com/huggingface/datasets/issues/2046 | 830,423,033 | MDU6SXNzdWU4MzA0MjMwMzM= | 2,046 | add_faisis_index gets very slow when doing it interatively | {
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"I think faiss automatically sets the number of threads to use to build the index.\r\nCan you check how many CPU cores are being used when you build the index in `use_own_knowleldge_dataset` as compared to this script ? Are there other programs running (maybe for rank>0) ?",
"Hi,\r\n I am running the add_faiss_in... | 2021-03-12T20:27:18 | 2021-03-24T22:29:11 | 2021-03-24T22:29:11 | NONE | null | null | null | null | As the below code suggests, I want to run add_faisis_index in every nth interaction from the training loop. I have 7.2 million documents. Usually, it takes 2.5 hours (if I run an as a separate process similar to the script given in rag/use_own_knowleldge_dataset.py). Now, this takes usually 5hrs. Is this normal? Any way to make this process faster?
@lhoestq
```
def training_step(self, batch, batch_idx) -> Dict:
if (not batch_idx==0) and (batch_idx%5==0):
print("******************************************************")
ctx_encoder=self.trainer.model.module.module.model.rag.ctx_encoder
model_copy =type(ctx_encoder)(self.config_dpr) # get a new instance #this will be load in the CPU
model_copy.load_state_dict(ctx_encoder.state_dict()) # copy weights and stuff
list_of_gpus = ['cuda:2','cuda:3']
c_dir='/custom/cache/dir'
kb_dataset = load_dataset("csv", data_files=[self.custom_config.csv_path], split="train", delimiter="\t", column_names=["title", "text"],cache_dir=c_dir)
print(kb_dataset)
n=len(list_of_gpus) #nunber of dedicated GPUs
kb_list=[kb_dataset.shard(n, i, contiguous=True) for i in range(n)]
#kb_dataset.save_to_disk('/hpc/gsir059/MY-Test/RAY/transformers/examples/research_projects/rag/haha-dir')
print(self.trainer.global_rank)
dataset_shards = self.re_encode_kb(model_copy.to(device=list_of_gpus[self.trainer.global_rank]),kb_list[self.trainer.global_rank])
output = [None for _ in list_of_gpus]
#self.trainer.accelerator_connector.accelerator.barrier("embedding_process")
dist.all_gather_object(output, dataset_shards)
#This creation and re-initlaization of the new index
if (self.trainer.global_rank==0): #saving will be done in the main process
combined_dataset = concatenate_datasets(output)
passages_path =self.config.passages_path
logger.info("saving the dataset with ")
#combined_dataset.save_to_disk('/hpc/gsir059/MY-Test/RAY/transformers/examples/research_projects/rag/MY-Passage')
combined_dataset.save_to_disk(passages_path)
logger.info("Add faiss index to the dataset that consist of embeddings")
embedding_dataset=combined_dataset
index = faiss.IndexHNSWFlat(768, 128, faiss.METRIC_INNER_PRODUCT)
embedding_dataset.add_faiss_index("embeddings", custom_index=index)
embedding_dataset.get_index("embeddings").save(self.config.index_path)
| {
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https://api.github.com/repos/huggingface/datasets/issues/2040 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2040/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2040/comments | https://api.github.com/repos/huggingface/datasets/issues/2040/events | https://github.com/huggingface/datasets/issues/2040 | 830,169,387 | MDU6SXNzdWU4MzAxNjkzODc= | 2,040 | ValueError: datasets' indices [1] come from memory and datasets' indices [0] come from disk | {
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"Hi ! To help me understand the situation, can you print the values of `load_from_disk(PATH_DATA_CLS_A)['train']._indices_data_files` and `load_from_disk(PATH_DATA_CLS_B)['train']._indices_data_files` ?\r\nThey should both have a path to an arrow file\r\n\r\nAlso note that from #2025 concatenating datasets will no... | 2021-03-12T14:27:00 | 2021-08-04T18:00:43 | 2021-08-04T18:00:43 | NONE | null | null | null | null | Hi there,
I am trying to concat two datasets that I've previously saved to disk via `save_to_disk()` like so (note that both are saved as `DataDict`, `PATH_DATA_CLS_*` are `Path`-objects):
```python
concatenate_datasets([load_from_disk(PATH_DATA_CLS_A)['train'], load_from_disk(PATH_DATA_CLS_B)['train']])
```
Yielding the following error:
```python
ValueError: Datasets' indices should ALL come from memory, or should ALL come from disk.
However datasets' indices [1] come from memory and datasets' indices [0] come from disk.
```
Been trying to solve this for quite some time now. Both `DataDict` have been created by reading in a `csv` via `load_dataset` and subsequently processed using the various `datasets` methods (i.e. filter, map, remove col, rename col). Can't figure out tho...
`load_from_disk(PATH_DATA_CLS_A)['train']` yields:
```python
Dataset({
features: ['labels', 'text'],
num_rows: 785
})
```
`load_from_disk(PATH_DATA_CLS_B)['train']` yields:
```python
Dataset({
features: ['labels', 'text'],
num_rows: 3341
})
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2038 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2038/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2038/comments | https://api.github.com/repos/huggingface/datasets/issues/2038/events | https://github.com/huggingface/datasets/issues/2038 | 830,036,875 | MDU6SXNzdWU4MzAwMzY4NzU= | 2,038 | outdated dataset_infos.json might fail verifications | {
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"Hi ! Thanks for reporting.\r\n\r\nTo update the dataset_infos.json you can run:\r\n```\r\ndatasets-cli test ./datasets/doc2dial --all_configs --save_infos --ignore_verifications\r\n```",
"Fixed by #2041, thanks again @songfeng !"
] | 2021-03-12T11:41:54 | 2021-03-16T16:27:40 | 2021-03-16T16:27:40 | CONTRIBUTOR | null | null | null | null | The [doc2dial/dataset_infos.json](https://github.com/huggingface/datasets/blob/master/datasets/doc2dial/dataset_infos.json) is outdated. It would fail data_loader when verifying download checksum etc..
Could you please update this file or point me how to update this file?
Thank you. | {
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https://api.github.com/repos/huggingface/datasets/issues/2036 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2036/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2036/comments | https://api.github.com/repos/huggingface/datasets/issues/2036/events | https://github.com/huggingface/datasets/issues/2036 | 829,909,258 | MDU6SXNzdWU4Mjk5MDkyNTg= | 2,036 | Cannot load wikitext | {
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"Solved!"
] | 2021-03-12T09:09:39 | 2021-03-15T08:45:02 | 2021-03-15T08:44:44 | NONE | null | null | null | null | when I execute these codes
```
>>> from datasets import load_dataset
>>> test_dataset = load_dataset("wikitext")
```
I got an error,any help?
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/xxx/anaconda3/envs/transformer/lib/python3.7/site-packages/datasets/load.py", line 589, in load_dataset
path, script_version=script_version, download_config=download_config, download_mode=download_mode, dataset=True
File "/home/xxx/anaconda3/envs/transformer/lib/python3.7/site-packages/datasets/load.py", line 267, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/home/xxx/anaconda3/envs/transformer/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 308, in cached_path
use_etag=download_config.use_etag,
File "/home/xxx/anaconda3/envs/transformer/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 487, in get_from_cache
raise ConnectionError("Couldn't reach {}".format(url))
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/1.1.3/datasets/wikitext/wikitext.py
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2035 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2035/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2035/comments | https://api.github.com/repos/huggingface/datasets/issues/2035/events | https://github.com/huggingface/datasets/issues/2035 | 829,475,544 | MDU6SXNzdWU4Mjk0NzU1NDQ= | 2,035 | wiki40b/wikipedia for almost all languages cannot be downloaded | {
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"Dear @lhoestq for wikipedia dataset I also get the same error, I greatly appreciate if you could have a look into this dataset as well. Below please find the command to reproduce the error:\r\n\r\n```\r\ndataset = load_dataset(\"wikipedia\", \"20200501.bg\")\r\nprint(dataset)\r\n```\r\n\r\nYour library is my only ... | 2021-03-11T19:54:54 | 2024-03-15T16:09:49 | 2024-03-15T16:09:48 | NONE | null | null | null | null | Hi
I am trying to download the data as below:
```
from datasets import load_dataset
dataset = load_dataset("wiki40b", "cs")
print(dataset)
```
I am getting this error. @lhoestq I will be grateful if you could assist me with this error. For almost all languages except english I am getting this error.
I really need majority of languages in this dataset to be able to train my models for a deadline and your great scalable super well-written library is my only hope to train the models at scale while being low on resources.
thank you very much.
```
(fast) dara@vgne046:/user/dara/dev/codes/seq2seq$ python test_data.py
Downloading and preparing dataset wiki40b/cs (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to temp/dara/cache_home_2/datasets/wiki40b/cs/1.1.0/063778187363ffb294896eaa010fc254b42b73e31117c71573a953b0b0bf010f...
Traceback (most recent call last):
File "test_data.py", line 3, in <module>
dataset = load_dataset("wiki40b", "cs")
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/load.py", line 746, in load_dataset
use_auth_token=use_auth_token,
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/builder.py", line 579, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/datasets/builder.py", line 1105, in _download_and_prepare
import apache_beam as beam
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/apache_beam-2.28.0-py3.7-linux-x86_64.egg/apache_beam/__init__.py", line 96, in <module>
from apache_beam import io
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/apache_beam-2.28.0-py3.7-linux-x86_64.egg/apache_beam/io/__init__.py", line 23, in <module>
from apache_beam.io.avroio import *
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/apache_beam-2.28.0-py3.7-linux-x86_64.egg/apache_beam/io/avroio.py", line 55, in <module>
import avro
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 668, in _load_unlocked
File "<frozen importlib._bootstrap>", line 638, in _load_backward_compatible
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/avro_python3-1.9.2.1-py3.7.egg/avro/__init__.py", line 34, in <module>
File "/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/avro_python3-1.9.2.1-py3.7.egg/avro/__init__.py", line 30, in LoadResource
NotADirectoryError: [Errno 20] Not a directory: '/user/dara/libs/anaconda3/envs/fast/lib/python3.7/site-packages/avro_python3-1.9.2.1-py3.7.egg/avro/VERSION.txt'
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2032 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2032/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2032/comments | https://api.github.com/repos/huggingface/datasets/issues/2032/events | https://github.com/huggingface/datasets/issues/2032 | 829,250,912 | MDU6SXNzdWU4MjkyNTA5MTI= | 2,032 | Use Arrow filtering instead of writing a new arrow file for Dataset.filter | {
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"Actually table.filter returns a new table in memory, which can fill users RAM.\r\n\r\nTherefore it's not a good solution if we want to keep supporting bigger than RAM datastes"
] | 2021-03-11T15:18:50 | 2024-01-19T13:26:32 | 2024-01-19T13:26:32 | MEMBER | null | null | null | null | Currently the filter method reads the dataset batch by batch to write a new, filtered, arrow file on disk. Therefore all the reading + writing can take some time.
Using a mask directly on the arrow table doesn't do any read or write operation therefore it's significantly quicker.
I think there are two cases:
- if the dataset doesn't have an indices mapping, then one can simply use the arrow filtering on the main arrow table `dataset._data.filter(...)`
- if the dataset an indices mapping, then the mask should be applied on the indices mapping table `dataset._indices.filter(...)`
The indices mapping is used to map between the idx at `dataset[idx]` in `__getitem__` and the idx in the actual arrow table.
The new filter method should therefore be faster, and allow users to pass either a filtering function (that returns a boolean given an example), or directly a mask.
Feel free to discuss this idea in this thread :)
One additional note: the refactor at #2025 would make all the pickle-related stuff work directly with the arrow filtering, so that we only need to change the Dataset.filter method without having to deal with pickle.
cc @theo-m @gchhablani
related issues: #1796 #1949 | {
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https://api.github.com/repos/huggingface/datasets/issues/2031 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2031/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2031/comments | https://api.github.com/repos/huggingface/datasets/issues/2031/events | https://github.com/huggingface/datasets/issues/2031 | 829,122,778 | MDU6SXNzdWU4MjkxMjI3Nzg= | 2,031 | wikipedia.py generator that extracts XML doesn't release memory | {
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"Hi @miyamonz \r\nThanks for investigating this issue, good job !\r\nIt would be awesome to integrate your fix in the library, could you open a pull request ?",
"OK! I'll send it later."
] | 2021-03-11T12:51:24 | 2021-03-22T08:33:52 | 2021-03-22T08:33:52 | CONTRIBUTOR | null | null | null | null | I tried downloading Japanese wikipedia, but it always failed because of out of memory maybe.
I found that the generator function that extracts XML data in wikipedia.py doesn't release memory in the loop.
https://github.com/huggingface/datasets/blob/13a5b7db992ad5cf77895e4c0f76595314390418/datasets/wikipedia/wikipedia.py#L464-L502
`root.clear()` intend to clear memory, but it doesn't.
https://github.com/huggingface/datasets/blob/13a5b7db992ad5cf77895e4c0f76595314390418/datasets/wikipedia/wikipedia.py#L490
https://github.com/huggingface/datasets/blob/13a5b7db992ad5cf77895e4c0f76595314390418/datasets/wikipedia/wikipedia.py#L494
I replaced them with `elem.clear()`, then it seems to work correctly.
here is the notebook to reproduce it.
https://gist.github.com/miyamonz/dc06117302b6e85fa51cbf46dde6bb51#file-xtract_content-ipynb | {
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https://api.github.com/repos/huggingface/datasets/issues/2029 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2029/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2029/comments | https://api.github.com/repos/huggingface/datasets/issues/2029/events | https://github.com/huggingface/datasets/issues/2029 | 829,097,290 | MDU6SXNzdWU4MjkwOTcyOTA= | 2,029 | Loading a faiss index KeyError | {
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"In your code `dataset2` doesn't contain the \"embeddings\" column, since it is created from the pandas DataFrame with columns \"text\" and \"label\".\r\n\r\nTherefore when you call `dataset2[embeddings_name]`, you get a `KeyError`.\r\n\r\nIf you want the \"embeddings\" column back, you can create `dataset2` with\r... | 2021-03-11T12:16:13 | 2021-03-12T00:21:09 | 2021-03-12T00:21:09 | NONE | null | null | null | null | I've recently been testing out RAG and DPR embeddings, and I've run into an issue that is not apparent in the documentation.
The basic steps are:
1. Create a dataset (dataset1)
2. Create an embeddings column using DPR
3. Add a faiss index to the dataset
4. Save faiss index to a file
5. Create a new dataset (dataset2) with the same text and label information as dataset1
6. Try to load the faiss index from file to dataset2
7. Get `KeyError: "Column embeddings not in the dataset"`
I've made a colab notebook that should show exactly what I did. Please switch to GPU runtime; I didn't check on CPU.
https://colab.research.google.com/drive/1X0S9ZuZ8k0ybcoei4w7so6dS_WrABmIx?usp=sharing
Ubuntu Version
VERSION="18.04.5 LTS (Bionic Beaver)"
datasets==1.4.1
faiss==1.5.3
faiss-gpu==1.7.0
torch==1.8.0+cu101
transformers==4.3.3
NVIDIA-SMI 460.56
Driver Version: 460.32.03
CUDA Version: 11.2
Tesla K80
I was basically following the steps here: https://huggingface.co/docs/datasets/faiss_and_ea.html#adding-a-faiss-index
I included the exact code from the documentation at the end of the notebook to show that they don't work either.
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https://api.github.com/repos/huggingface/datasets/issues/2026 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2026/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2026/comments | https://api.github.com/repos/huggingface/datasets/issues/2026/events | https://github.com/huggingface/datasets/issues/2026 | 828,194,467 | MDU6SXNzdWU4MjgxOTQ0Njc= | 2,026 | KeyError on using map after renaming a column | {
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"Hi,\r\n\r\nActually, the error occurs due to these two lines:\r\n```python\r\nraw_dataset.set_format('torch',columns=['img','label'])\r\nraw_dataset = raw_dataset.rename_column('img','image')\r\n```\r\n`Dataset.rename_column` doesn't update the `_format_columns` attribute, previously defined by `Dataset.set_format... | 2021-03-10T18:54:17 | 2021-03-11T14:39:34 | 2021-03-11T14:38:40 | CONTRIBUTOR | null | null | null | null | Hi,
I'm trying to use `cifar10` dataset. I want to rename the `img` feature to `image` in order to make it consistent with `mnist`, which I'm also planning to use. By doing this, I was trying to avoid modifying `prepare_train_features` function.
Here is what I try:
```python
transform = Compose([ToPILImage(),ToTensor(),Normalize([0.0,0.0,0.0],[1.0,1.0,1.0])])
def prepare_features(examples):
images = []
labels = []
print(examples)
for example_idx, example in enumerate(examples["image"]):
if transform is not None:
images.append(transform(examples["image"][example_idx].permute(2,0,1)))
else:
images.append(examples["image"][example_idx].permute(2,0,1))
labels.append(examples["label"][example_idx])
output = {"label":labels, "image":images}
return output
raw_dataset = load_dataset('cifar10')
raw_dataset.set_format('torch',columns=['img','label'])
raw_dataset = raw_dataset.rename_column('img','image')
features = datasets.Features({
"image": datasets.Array3D(shape=(3,32,32),dtype="float32"),
"label": datasets.features.ClassLabel(names=[
"airplane",
"automobile",
"bird",
"cat",
"deer",
"dog",
"frog",
"horse",
"ship",
"truck",
]),
})
train_dataset = raw_dataset.map(prepare_features, features = features,batched=True, batch_size=10000)
```
The error:
```python
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-54-bf29672c53ee> in <module>()
14 ]),
15 })
---> 16 train_dataset = raw_dataset.map(prepare_features, features = features,batched=True, batch_size=10000)
2 frames
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in map(self, function, with_indices, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint)
1287 test_inputs = self[:2] if batched else self[0]
1288 test_indices = [0, 1] if batched else 0
-> 1289 update_data = does_function_return_dict(test_inputs, test_indices)
1290 logger.info("Testing finished, running the mapping function on the dataset")
1291
/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py in does_function_return_dict(inputs, indices)
1258 fn_args = [inputs] if input_columns is None else [inputs[col] for col in input_columns]
1259 processed_inputs = (
-> 1260 function(*fn_args, indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)
1261 )
1262 does_return_dict = isinstance(processed_inputs, Mapping)
<ipython-input-52-b4dccbafb70d> in prepare_features(examples)
3 labels = []
4 print(examples)
----> 5 for example_idx, example in enumerate(examples["image"]):
6 if transform is not None:
7 images.append(transform(examples["image"][example_idx].permute(2,0,1)))
KeyError: 'image'
```
The print statement inside returns this:
```python
{'label': tensor([6, 9])}
```
Apparently, both `img` and `image` do not exist after renaming.
Note that this code works fine with `img` everywhere.
Notebook: https://colab.research.google.com/drive/1SzESAlz3BnVYrgQeJ838vbMp1OsukiA2?usp=sharing
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https://api.github.com/repos/huggingface/datasets/issues/2022 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2022/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2022/comments | https://api.github.com/repos/huggingface/datasets/issues/2022/events | https://github.com/huggingface/datasets/issues/2022 | 827,435,033 | MDU6SXNzdWU4Mjc0MzUwMzM= | 2,022 | ValueError when rename_column on splitted dataset | {
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"Hi,\r\n\r\nThis is a bug so thanks for reporting it. `Dataset.__setstate__` is the problem, which is called when `Dataset.rename_column` tries to copy the dataset with `copy.deepcopy(self)`. This only happens if the `split` arg in `load_dataset` was defined as `ReadInstruction`.\r\n\r\nTo overcome this issue, use... | 2021-03-10T09:40:38 | 2025-02-05T13:36:07 | 2021-03-16T14:05:05 | NONE | null | null | null | null | Hi there,
I am loading `.tsv` file via `load_dataset` and subsequently split the rows into training and test set via the `ReadInstruction` API like so:
```python
split = {
'train': ReadInstruction('train', to=90, unit='%'),
'test': ReadInstruction('train', from_=-10, unit='%')
}
dataset = load_dataset(
path='csv', # use 'text' loading script to load from local txt-files
delimiter='\t', # xxx
data_files=text_files, # list of paths to local text files
split=split, # xxx
)
dataset
```
Part of output:
```python
DatasetDict({
train: Dataset({
features: ['sentence', 'sentiment'],
num_rows: 900
})
test: Dataset({
features: ['sentence', 'sentiment'],
num_rows: 100
})
})
```
Afterwards I'd like to rename the 'sentence' column to 'text' in order to be compatible with my modelin pipeline. If I run the following code I experience a `ValueError` however:
```python
dataset['train'].rename_column('sentence', 'text')
```
```python
/usr/local/lib/python3.7/dist-packages/datasets/splits.py in __init__(self, name)
353 for split_name in split_names_from_instruction:
354 if not re.match(_split_re, split_name):
--> 355 raise ValueError(f"Split name should match '{_split_re}'' but got '{split_name}'.")
356
357 def __str__(self):
ValueError: Split name should match '^\w+(\.\w+)*$'' but got 'ReadInstruction('.
```
In particular, these behavior does not arise if I use the deprecated `rename_column_` method. Any idea what causes the error? Would assume something in the way I defined the split.
Thanks in advance! :) | {
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