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} | 2 | 2021-06-14T10:02:47 | 2021-06-15T18:06:18 | 2021-06-14T14:24:37 | MEMBER | null | false | {
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} | From pyarrow version 4, it is supported to cast sliced lists.
This PR uses default pyarrow cast in Datasets to cast sliced list arrays if pyarrow version is >= 4.
In relation with PR #2461 and #2490.
cc: @lhoestq, @abhi1thakur, @SBrandeis | {
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However the fingerprint seems to change when someone moves the cache directory of the dataset.
This is because it uses the default fingerprint generation:
1. the dataset path is used to get the fingerprint
2. the modification times of the arrow file is also used to get the fingerprint
To fix that we could set the fingerprint of the dataset to be a hash of (<dataset_name>, <config_name>, <version>, <script_hash>), i.e. a hash of the the cache path relative to the cache directory. | {
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- How to make datasets the fastest
- How to make datasets take the less RAM
- How to make datasets take the less hard drive mem
cc: @thomwolf
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} | ref - https://github.com/huggingface/datasets/issues/2068 | {
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} | Hi, awesome folks behind the huggingface!
Here is my PR for the text classification dataset in Mongolian.
Please do let me know in case you have anything to clarify.
Thanks & Regards,
Enod | {
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} | Hi there!
I'm wanting to add the Rico datasets for software engineering type data to y'alls awesome library. However, as I have started coding, I've ran into a few hiccups so I thought it best to open the PR early to get a bit of discussion on how the Rico datasets should be added to the `datasets` lib.
1) There are 7 different datasets under Rico and so I was wondering, should I make a folder for each or should I put them as different configurations of a single dataset?
You can see the datasets available for Rico here: http://interactionmining.org/rico
2) As of right now, I have a semi working version of the first dataset which has pairs of screenshots and hierarchies from android applications. However, these screenshots are very large (1440, 2560, 3) and there are 66,000 of them so I am not able to perform the processing that the `datasets` lib does after downloading and extracting the dataset since I run out of memory very fast. Is there a way to have `datasets` lib not put everything into memory while it is processing the dataset?
2.1) If there is not a way, would it be better to just return the path to the screenshots instead of the actual image?
3) The hierarchies are JSON objects and looking through the documentation of `datasets`, I didn't see any feature that I could use for this type of data. So, for now I just have it being read in as a string, is this okay or should I be doing it differently?
4) One of the Rico datasets is a bunch of animations (GIFs), is there a `datasets` feature that I can put this type of data into or should I just return the path as a string?
I appreciate any and all help I can get for this PR, I think the Rico datasets will be an awesome addition to the library :nerd_face: ! | {
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https://api.github.com/repos/huggingface/datasets/issues/2485 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2485/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2485/comments | https://api.github.com/repos/huggingface/datasets/issues/2485/events | https://github.com/huggingface/datasets/issues/2485 | 919,099,218 | MDU6SXNzdWU5MTkwOTkyMTg= | 2,485 | Implement layered building | {
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] | open | false | null | [] | null | 0 | 2021-06-11T18:54:25 | 2021-06-11T18:54:25 | null | MEMBER | null | null | null | As discussed with @stas00 and @lhoestq (see also here https://github.com/huggingface/datasets/issues/2481#issuecomment-859712190):
> My suggestion for this would be to have this enabled by default.
>
> Plus I don't know if there should be a dedicated issue to that is another functionality. But I propose layered building rather than all at once. That is:
>
> 1. uncompress a handful of files via a generator enough to generate one arrow file
> 2. process arrow file 1
> 3. delete all the files that went in and aren't needed anymore.
>
> rinse and repeat.
>
> 1. This way much less disc space will be required - e.g. on JZ we won't be running into inode limitation, also it'd help with the collaborative hub training project
> 2. The user doesn't need to go and manually clean up all the huge files that were left after pre-processing
> 3. It would already include deleting temp files this issue is talking about
>
> I wonder if the new streaming API would be of help, except here the streaming would be into arrow files as the destination, rather than dataloaders. | {
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```python
from datasets import load_dataset_builder
dataset_name = "openwebtext"
builder = load_dataset_builder(dataset_name)
print(builder.cache_dir)
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2483 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2483/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2483/comments | https://api.github.com/repos/huggingface/datasets/issues/2483/events | https://github.com/huggingface/datasets/pull/2483 | 918,871,712 | MDExOlB1bGxSZXF1ZXN0NjY4MjU1Mjg1 | 2,483 | Use gc.collect only when needed to avoid slow downs | {
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} | In https://github.com/huggingface/datasets/commit/42320a110d9d072703814e1f630a0d90d626a1e6 we added a call to gc.collect to resolve some issues on windows (see https://github.com/huggingface/datasets/pull/2482)
However calling gc.collect too often causes significant slow downs (the CI run time doubled).
So I just moved the gc.collect call to the exact place where it's actually needed: when post-processing a dataset | {
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} | We used to have permission errors on windows whith the latest versions of tqdm (see [here](https://app.circleci.com/pipelines/github/huggingface/datasets/6365/workflows/24f7c960-3176-43a5-9652-7830a23a981e/jobs/39232))
They were due to open arrow files not properly closed by pyarrow.
Since https://github.com/huggingface/datasets/commit/42320a110d9d072703814e1f630a0d90d626a1e6 gc.collect is called each time we don't need an arrow file to make sure that the files are closed.
close https://github.com/huggingface/datasets/issues/2471
cc @lewtun | {
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} | 1 | 2021-06-11T12:21:52 | 2021-07-19T09:08:18 | 2021-07-19T09:08:18 | MEMBER | null | null | null | As discussed with @stas00 and @lhoestq, allowing the deletion of extracted files would save a great amount of disk space to typical user. | {
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TODO:
- [x] Set configurable extracted datasets path: #2487
- [x] Set configurable downloaded datasets path: #2488
- [ ] Set configurable "incomplete" datasets path? | {
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https://api.github.com/repos/huggingface/datasets/issues/2476 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2476/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2476/comments | https://api.github.com/repos/huggingface/datasets/issues/2476/events | https://github.com/huggingface/datasets/pull/2476 | 917,686,662 | MDExOlB1bGxSZXF1ZXN0NjY3MTg3OTk1 | 2,476 | Add TimeDial | {
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} | Dataset: https://github.com/google-research-datasets/TimeDial
To-Do: Update README.md and add YAML tags | {
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https://api.github.com/repos/huggingface/datasets/issues/2475 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2475/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2475/comments | https://api.github.com/repos/huggingface/datasets/issues/2475/events | https://github.com/huggingface/datasets/issues/2475 | 917,650,882 | MDU6SXNzdWU5MTc2NTA4ODI= | 2,475 | Issue in timit_asr database | {
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] | closed | false | null | [] | null | 2 | 2021-06-10T18:05:29 | 2021-06-13T08:13:50 | 2021-06-13T08:13:13 | NONE | null | null | null | ## Describe the bug
I am trying to load the timit_asr dataset however only the first record is shown (duplicated over all the rows).
I am using the next code line
dataset = load_dataset(“timit_asr”, split=“test”).shuffle().select(range(10))
The above code result with the same sentence duplicated ten times.
It also happens when I use the dataset viewer at Streamlit .
## Steps to reproduce the bug
from datasets import load_dataset
dataset = load_dataset(“timit_asr”, split=“test”).shuffle().select(range(10))
data = dataset.to_pandas()
# Sample code to reproduce the bug
```
## Expected results
table with different row information
## Actual results
Specify the actual results or traceback.
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.4.1 (also occur in the latest version)
- Platform: Linux-4.15.0-143-generic-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
- PyTorch version (GPU?): 1.8.1+cu102 (False)
- Tensorflow version (GPU?): 1.15.3 (False)
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No
- `datasets` version:
- Platform:
- Python version:
- PyArrow version:
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] | null | 3 | 2021-06-10T17:39:36 | 2021-06-14T08:41:09 | null | NONE | null | null | null | **Is your feature request related to a problem? Please describe.**
When using Google Colab big datasets can be an issue, as they won't fit on the VM's disk. Therefore mounting google drive could be a possible solution. Unfortunatly when loading my own dataset by using the _load_from_disk_ function, the data gets cached to the VM's disk:
`
from datasets import load_from_disk
myPreprocessedData = load_from_disk("/content/gdrive/MyDrive/ASR_data/myPreprocessedData")
`
I know that chaching on google drive could slow down learning. But at least it would run.
**Describe the solution you'd like**
Add cache_Dir parameter to the load_from_disk function.
**Describe alternatives you've considered**
It looks like you could write a custom loading script for the load_dataset function. But this seems to be much too complex for my use case. Is there perhaps a template here that uses the load_from_disk function?
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To-Do: Update README.md and add YAML tags | {
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} | 4 | 2021-06-10T15:15:46 | 2021-06-14T16:49:42 | 2021-06-14T16:49:42 | MEMBER | null | null | null | After the last release of Datasets (1.8.0), the automatic generation of the Zenodo DOI failed: it appears in yellow as "Received", instead of in green as "Published".
I have contacted Zenodo support to fix this issue.
TODO:
- [x] Check with Zenodo to fix the issue
- [x] Check BibTeX entry is right | {
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} | 0 | 2021-06-10T08:31:49 | 2021-06-11T15:11:50 | 2021-06-11T15:11:50 | MEMBER | null | null | null | See: https://app.circleci.com/pipelines/github/huggingface/datasets/235/workflows/cfb6a39f-68eb-4802-8b17-2cd5e8ea7369/jobs/1111
```
PermissionError: [WinError 32] The process cannot access the file because it is being used by another process
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] | closed | false | null | [] | null | 6 | 2021-06-09T22:40:22 | 2021-07-01T09:34:54 | 2021-07-01T09:11:13 | NONE | null | null | null | ## Describe the bug
Crash if when using `num_proc` > 1 (I used 16) for `map()` on a `datasets.Dataset`.
I believe I've had cases where `num_proc` > 1 works before, but now it seems either inconsistent, or depends on my data. I'm not sure whether the issue is on my end, because it's difficult for me to debug! Any tips greatly appreciated, I'm happy to provide more info if it would helps us diagnose.
## Steps to reproduce the bug
```python
# this function will be applied with map()
def tokenize_function(examples):
return tokenizer(
examples["text"],
padding=PaddingStrategy.DO_NOT_PAD,
truncation=True,
)
# data_files is a Dict[str, str] mapping name -> path
datasets = load_dataset("text", data_files={...})
# this is where the error happens if num_proc = 16,
# but is fine if num_proc = 1
tokenized_datasets = datasets.map(
tokenize_function,
batched=True,
num_proc=num_workers,
)
```
## Expected results
The `map()` function succeeds with `num_proc` > 1.
## Actual results
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.6.2
- Platform: Linux-5.4.0-73-generic-x86_64-with-glibc2.31
- Python version: 3.9.5
- PyTorch version (GPU?): 1.8.1+cu111 (True)
- Tensorflow version (GPU?): not installed (NA)
- Using GPU in script?: Yes, but I think N/A for this issue
- Using distributed or parallel set-up in script?: Multi-GPU on one machine, but I think also N/A for this issue
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The change is done for issues:
#2061
#2444
Close #2061 , close #2444. | {
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@lhoestq , can you please review | {
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} | The labels index were mismatching the actual ones used in the dataset. Specifically `0` is used for `SUPPORTS` and `1` is used for `REFUTES`
After this change, the `README.md` now reflects the content of `dataset_infos.json`.
Signed-off-by: Matteo Manica <[email protected]> | {
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Instead of updating the path, this PR fixes a commit hash as suggested by @lhoestq. | {
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} | 0 | 2021-06-08T19:22:04 | 2021-09-02T05:33:32 | null | MEMBER | null | null | null | As discussed in #2424 and #2437 (please see there for detailed conversation):
- It would be desirable to improve UX with respect the confusion between DatasetDict and Dataset.
- The difference between Dataset and DatasetDict is an additional abstraction complexity that confuses "typical" end users.
- A user expects a "Dataset" (whatever it contains multiple or a single split) and maybe it could be interesting to try to simplify the user-facing API as much as possible to hide this complexity from the end user.
Here is a proposal for discussion and refined (and potential abandon if it's not good enough):
- let's consider that a DatasetDict is also a Dataset with the various split concatenated one after the other
- let's disallow the use of integers in split names (probably not a very big breaking change)
- when you index with integers you access the examples progressively in split after the other is finished (in a deterministic order)
- when you index with strings/split name you have the same behavior as now (full backward compat)
- let's then also have all the methods of a Dataset on the DatasetDict
The end goal would be to merge both Dataset and DatasetDict object in a single object that would be (pretty much totally) backward compatible with both.
There are a few things that we could discuss if we want to merge Dataset and DatasetDict:
1. what happens if you index by a string ? Does it return the column or the split ? We could disallow conflicts between column names and split names to avoid ambiguities. It can be surprising to be able to get a column or a split using the same indexing feature
```
from datasets import load_dataset
dataset = load_dataset(...)
dataset["train"]
dataset["input_ids"]
```
2. what happens when you iterate over the object ? I guess it should iterate over the examples as a Dataset object, but a DatasetDict used to iterate over the splits as they are the dictionary keys. This is a breaking change that we can discuss.
Moreover regarding your points:
- integers are not allowed as split names already
- it's definitely doable to have all the methods. Maybe some of them like train_test_split that is currently only available for Dataset can be tweaked to work for a split dataset
cc: @thomwolf @lhoestq | {
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https://api.github.com/repos/huggingface/datasets/issues/2461 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2461/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2461/comments | https://api.github.com/repos/huggingface/datasets/issues/2461/events | https://github.com/huggingface/datasets/pull/2461 | 915,286,150 | MDExOlB1bGxSZXF1ZXN0NjY1MTE3MTY4 | 2,461 | Support sliced list arrays in cast | {
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} | There is this issue in pyarrow:
```python
import pyarrow as pa
arr = pa.array([[i * 10] for i in range(4)])
arr.cast(pa.list_(pa.int32())) # works
arr = arr.slice(1)
arr.cast(pa.list_(pa.int32())) # fails
# ArrowNotImplementedError("Casting sliced lists (non-zero offset) not yet implemented")
```
However in `Dataset.cast` we slice tables to cast their types (it's memory intensive), so we have the same issue.
Because of this it is currently not possible to cast a Dataset with a Sequence feature type (unless the table is small enough to not be sliced).
In this PR I fixed this by resetting the offset of `pyarrow.ListArray` arrays to zero in the table before casting.
I used `pyarrow.compute.subtract` function to update the offsets of the ListArray.
cc @abhi1thakur @SBrandeis | {
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https://api.github.com/repos/huggingface/datasets/issues/2460 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2460/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2460/comments | https://api.github.com/repos/huggingface/datasets/issues/2460/events | https://github.com/huggingface/datasets/pull/2460 | 915,268,536 | MDExOlB1bGxSZXF1ZXN0NjY1MTAyMjA4 | 2,460 | Revert default in-memory for small datasets | {
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} | 1 | 2021-06-08T17:14:23 | 2021-06-08T18:04:14 | 2021-06-08T17:55:43 | MEMBER | null | false | {
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https://api.github.com/repos/huggingface/datasets/issues/2459 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2459/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2459/comments | https://api.github.com/repos/huggingface/datasets/issues/2459/events | https://github.com/huggingface/datasets/issues/2459 | 915,222,015 | MDU6SXNzdWU5MTUyMjIwMTU= | 2,459 | `Proto_qa` hosting seems to be broken | {
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] | closed | false | null | [] | null | 1 | 2021-06-08T16:16:32 | 2021-06-10T08:31:09 | 2021-06-10T08:31:09 | MEMBER | null | null | null | ## Describe the bug
The hosting (on Github) of the `proto_qa` dataset seems broken. I haven't investigated more yet, just flagging it for now.
@zaidalyafeai if you want to dive into it, I think it's just a matter of changing the links in `proto_qa.py`
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("proto_qa")
```
## Actual results
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/load.py", line 751, in load_dataset
use_auth_token=use_auth_token,
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 575, in download_and_prepare
dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/builder.py", line 630, in _download_and_prepare
split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
File "/home/hf/.cache/huggingface/modules/datasets_modules/datasets/proto_qa/445346efaad5c5f200ecda4aa7f0fb50ff1b55edde3003be424a2112c3e8102e/proto_qa.py", line 131, in _split_generators
train_fpath = dl_manager.download(_URLs[self.config.name]["train"])
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 199, in download
num_proc=download_config.num_proc,
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/py_utils.py", line 195, in map_nested
return function(data_struct)
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/download_manager.py", line 218, in _download
return cached_path(url_or_filename, download_config=download_config)
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 291, in cached_path
use_auth_token=download_config.use_auth_token,
File "/home/hf/dev/promptsource/.venv/lib/python3.7/site-packages/datasets/utils/file_utils.py", line 621, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/iesl/protoqa-data/master/data/train/protoqa_train.jsonl
``` | {
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} | 1 | 2021-06-08T15:51:41 | 2021-06-08T18:57:11 | 2021-06-08T17:55:43 | MEMBER | null | null | null | Users are reporting issues and confusion about setting default in-memory to True for small datasets.
We see 2 clear use cases of Datasets:
- the "canonical" way, where you can work with very large datasets, as they are memory-mapped and cached (after every transformation)
- some edge cases (speed benchmarks, interactive/exploratory analysis,...), where default in-memory can explicitly be enabled, and no caching will be done
After discussing with @lhoestq we have agreed to:
- revert this feature (implemented in #2182)
- explain in the docs how to optimize speed/performance by setting default in-memory
cc: @stas00 https://github.com/huggingface/datasets/pull/2409#issuecomment-856210552 | {
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https://api.github.com/repos/huggingface/datasets/issues/2457 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2457/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2457/comments | https://api.github.com/repos/huggingface/datasets/issues/2457/events | https://github.com/huggingface/datasets/pull/2457 | 915,079,441 | MDExOlB1bGxSZXF1ZXN0NjY0OTQwMzQ0 | 2,457 | Add align_labels_with_mapping function | {
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} | This PR adds a helper function to align the `label2id` mapping between a `datasets.Dataset` and a classifier (e.g. a transformer with a `PretrainedConfig.label2id` dict), with the alignment performed on the dataset itself.
This will help us with the Hub evaluation, where we won't know in advance whether a model that is fine-tuned on say MNLI has the same mappings as the MNLI dataset we load from `datasets`.
An example where this is needed is if we naively try to evaluate `microsoft/deberta-base-mnli` on `mnli` because the model config has the following mappings:
```python
"id2label": {
"0": "CONTRADICTION",
"1": "NEUTRAL",
"2": "ENTAILMENT"
},
"label2id": {
"CONTRADICTION": 0,
"ENTAILMENT": 2,
"NEUTRAL": 1
}
```
while the `mnli` dataset has the `contradiction` and `neutral` labels swapped:
```python
id2label = {0: 'entailment', 1: 'neutral', 2: 'contradiction'}
label2id = {'contradiction': 2, 'entailment': 0, 'neutral': 1}
```
As a result, we get a much lower accuracy during evaluation:
```python
from datasets import load_dataset
from transformers.trainer_utils import EvalPrediction
from transformers import AutoModelForSequenceClassification, Trainer
# load dataset for evaluation
mnli = load_dataset("glue", "mnli", split="test")
# load model
model_ckpt = "microsoft/deberta-base-mnli"
model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
# preprocess, create trainer ...
mnli_enc = ...
trainer = Trainer(model, args=args, tokenizer=tokenizer)
# generate preds
preds = trainer.predict(mnli_enc)
# preds.label_ids misalinged with model.config => returns wrong accuracy (too low)!
compute_metrics(EvalPrediction(preds.predictions, preds.label_ids))
```
The fix is to use the helper function before running the evaluation to make sure the label IDs are aligned:
```python
mnli_enc_aligned = mnli_enc.align_labels_with_mapping(label2id=config.label2id, label_column="label")
# preds now aligned and everyone is happy :)
preds = trainer.predict(mnli_enc_aligned)
```
cc @thomwolf @lhoestq | {
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https://api.github.com/repos/huggingface/datasets/issues/2456 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2456/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2456/comments | https://api.github.com/repos/huggingface/datasets/issues/2456/events | https://github.com/huggingface/datasets/pull/2456 | 914,709,293 | MDExOlB1bGxSZXF1ZXN0NjY0NjAwOTk1 | 2,456 | Fix cross-reference typos in documentation | {
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} | Fix some minor typos in docs that avoid the creation of cross-reference links. | {
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https://api.github.com/repos/huggingface/datasets/issues/2455 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2455/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2455/comments | https://api.github.com/repos/huggingface/datasets/issues/2455/events | https://github.com/huggingface/datasets/pull/2455 | 914,177,468 | MDExOlB1bGxSZXF1ZXN0NjY0MTEzNjg2 | 2,455 | Update version in xor_tydi_qa.py | {
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} | Fix #2449
@lhoestq Should I revert to the old `dummy/1.0.0` or delete it and keep only `dummy/1.1.0`? | {
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https://api.github.com/repos/huggingface/datasets/issues/2454 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2454/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2454/comments | https://api.github.com/repos/huggingface/datasets/issues/2454/events | https://github.com/huggingface/datasets/pull/2454 | 913,883,631 | MDExOlB1bGxSZXF1ZXN0NjYzODUyODU1 | 2,454 | Rename config and environment variable for in memory max size | {
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} | As discussed in #2409, both config and environment variable have been renamed.
cc: @stas00, huggingface/transformers#12056 | {
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} | When loading a Dataset from a JSON file whose column names are not sorted alphabetically, we should get the same column name order, whether we pass features (in the same order as in the file) or not.
I found this issue while working on #2366. | {
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https://api.github.com/repos/huggingface/datasets/issues/2452 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2452/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2452/comments | https://api.github.com/repos/huggingface/datasets/issues/2452/events | https://github.com/huggingface/datasets/issues/2452 | 913,603,877 | MDU6SXNzdWU5MTM2MDM4Nzc= | 2,452 | MRPC test set differences between torch and tensorflow datasets | {
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] | closed | false | null | [] | null | 1 | 2021-06-07T14:20:26 | 2021-06-07T14:34:32 | 2021-06-07T14:34:32 | NONE | null | null | null | ## Describe the bug
When using `load_dataset("glue", "mrpc")` to load the MRPC dataset, the test set includes the labels. When using `tensorflow_datasets.load('glue/{}'.format('mrpc'))` to load the dataset the test set does not contain the labels. There should be consistency between torch and tensorflow ways of importing the GLUE datasets.
## Steps to reproduce the bug
Minimal working code
```python
from datasets import load_dataset
import tensorflow as tf
import tensorflow_datasets
# torch
dataset = load_dataset("glue", "mrpc")
# tf
data = tensorflow_datasets.load('glue/{}'.format('mrpc'))
data = list(data['test'].as_numpy_iterator())
for i in range(40,50):
tf_sentence1 = data[i]['sentence1'].decode("utf-8")
tf_sentence2 = data[i]['sentence2'].decode("utf-8")
tf_label = data[i]['label']
index = data[i]['idx']
print('Index {}'.format(index))
torch_sentence1 = dataset['test']['sentence1'][index]
torch_sentence2 = dataset['test']['sentence2'][index]
torch_label = dataset['test']['label'][index]
print('Tensorflow: \n\tSentence1 {}\n\tSentence2 {}\n\tLabel {}'.format(tf_sentence1, tf_sentence2, tf_label))
print('Torch: \n\tSentence1 {}\n\tSentence2 {}\n\tLabel {}'.format(torch_sentence1, torch_sentence2, torch_label))
```
Sample output
```
Index 954
Tensorflow:
Sentence1 Sabri Yakou , an Iraqi native who is a legal U.S. resident , appeared before a federal magistrate yesterday on charges of violating U.S. arms-control laws .
Sentence2 The elder Yakou , an Iraqi native who is a legal U.S. resident , appeared before a federal magistrate Wednesday on charges of violating U.S. arms control laws .
Label -1
Torch:
Sentence1 Sabri Yakou , an Iraqi native who is a legal U.S. resident , appeared before a federal magistrate yesterday on charges of violating U.S. arms-control laws .
Sentence2 The elder Yakou , an Iraqi native who is a legal U.S. resident , appeared before a federal magistrate Wednesday on charges of violating U.S. arms control laws .
Label 1
Index 711
Tensorflow:
Sentence1 Others keep records sealed for as little as five years or as much as 30 .
Sentence2 Some states make them available immediately ; others keep them sealed for as much as 30 years .
Label -1
Torch:
Sentence1 Others keep records sealed for as little as five years or as much as 30 .
Sentence2 Some states make them available immediately ; others keep them sealed for as much as 30 years .
Label 0
```
## Expected results
I would expect the datasets to be independent of whether I am working with torch or tensorflow.
## Actual results
Test set labels are provided in the `datasets.load_datasets()` for MRPC. However MRPC is the only task where the test set labels are not -1.
## Environment info
- `datasets` version: 1.7.0
- Platform: Linux-5.4.109+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.10
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2451 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2451/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2451/comments | https://api.github.com/repos/huggingface/datasets/issues/2451/events | https://github.com/huggingface/datasets/pull/2451 | 913,263,340 | MDExOlB1bGxSZXF1ZXN0NjYzMzIwNDY1 | 2,451 | Mention that there are no answers in adversarial_qa test set | {
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} | As mention in issue https://github.com/huggingface/datasets/issues/2447, there are no answers in the test set | {
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https://api.github.com/repos/huggingface/datasets/issues/2450 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2450/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2450/comments | https://api.github.com/repos/huggingface/datasets/issues/2450/events | https://github.com/huggingface/datasets/issues/2450 | 912,890,291 | MDU6SXNzdWU5MTI4OTAyOTE= | 2,450 | BLUE file not found | {
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} | [] | closed | false | null | [] | null | 2 | 2021-06-06T17:01:54 | 2021-06-07T10:46:15 | 2021-06-07T10:46:15 | NONE | null | null | null | Hi, I'm having the following issue when I try to load the `blue` metric.
```shell
import datasets
metric = datasets.load_metric('blue')
Traceback (most recent call last):
File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/load.py", line 320, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 291, in cached_path
use_auth_token=download_config.use_auth_token,
File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 621, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/1.7.0/metrics/blue/blue.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/load.py", line 332, in prepare_module
local_path = cached_path(file_path, download_config=download_config)
File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 291, in cached_path
use_auth_token=download_config.use_auth_token,
File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/utils/file_utils.py", line 621, in get_from_cache
raise FileNotFoundError("Couldn't find file at {}".format(url))
FileNotFoundError: Couldn't find file at https://raw.githubusercontent.com/huggingface/datasets/master/metrics/blue/blue.py
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/load.py", line 605, in load_metric
dataset=False,
File "/home/irfan/environments/Perplexity_Transformers/lib/python3.6/site-packages/datasets/load.py", line 343, in prepare_module
combined_path, github_file_path
FileNotFoundError: Couldn't find file locally at blue/blue.py, or remotely at https://raw.githubusercontent.com/huggingface/datasets/1.7.0/metrics/blue/blue.py.
The file is also not present on the master branch on github.
```
Here is dataset installed version info
```shell
pip freeze | grep datasets
datasets==1.7.0
```
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https://api.github.com/repos/huggingface/datasets/issues/2449 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2449/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2449/comments | https://api.github.com/repos/huggingface/datasets/issues/2449/events | https://github.com/huggingface/datasets/pull/2449 | 912,751,752 | MDExOlB1bGxSZXF1ZXN0NjYyODg1ODUz | 2,449 | Update `xor_tydi_qa` url to v1.1 | {
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} | [] | closed | false | null | [] | null | 6 | 2021-06-06T09:44:58 | 2021-06-07T15:16:21 | 2021-06-07T08:31:04 | CONTRIBUTOR | null | false | {
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} | The dataset is updated and the old url no longer works. So I updated it.
I faced a bug while trying to fix this. Documenting the solution here. Maybe we can add it to the doc (`CONTRIBUTING.md` and `ADD_NEW_DATASET.md`).
> And to make the command work without the ExpectedMoreDownloadedFiles error, you just need to use the --ignore_verifications flag.
https://github.com/huggingface/datasets/issues/2076#issuecomment-803904366 | {
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] | closed | false | null | [] | null | 2 | 2021-06-05T14:57:38 | 2021-06-07T11:13:07 | 2021-06-07T11:13:07 | NONE | null | null | null | ## Describe the bug
When loading the adversarial_qa dataset the 'test' portion has no answers. Only the 'train' and 'validation' portions do. This occurs with all four of the configs ('adversarialQA', 'dbidaf', 'dbert', 'droberta')
## Steps to reproduce the bug
```
from datasets import load_dataset
examples = load_dataset('adversarial_qa', 'adversarialQA', script_version="master")['test']
print('Loaded {:,} examples'.format(len(examples)))
has_answers = 0
for e in examples:
if e['answers']['text']:
has_answers += 1
print('{:,} have answers'.format(has_answers))
>>> Loaded 3,000 examples
>>> 0 have answers
examples = load_dataset('adversarial_qa', 'adversarialQA', script_version="master")['validation']
<...code above...>
>>> Loaded 3,000 examples
>>> 3,000 have answers
```
## Expected results
If 'test' is a valid dataset, it should have answers. Also note that all of the 'train' and 'validation' sets have answers, there are no "no answer" questions with this set (not sure if this is correct or not).
## Environment info
- `datasets` version: 1.7.0
- Platform: Linux-5.8.0-53-generic-x86_64-with-glibc2.29
- Python version: 3.8.5
- PyArrow version: 1.0.0
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} | Closes #2388 | {
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] | closed | false | null | [] | null | 2 | 2021-06-04T09:10:26 | 2021-06-18T11:53:43 | 2021-06-18T11:53:43 | CONTRIBUTOR | null | null | null | I was browsing through annotation guidelines, as suggested by the datasets introduction.
The guidlines saids "There must be exactly one blank line after every sentence, including the last sentence in the file. Empty sentences are not allowed." in the [Sentence Boundaries and Comments section](https://universaldependencies.org/format.html#sentence-boundaries-and-comments)
But the sentence boundaries seems not to be represented by huggingface datasets features well. I found out that multiple sentence are concatenated together as a 1D array, without any delimiter.
PAN-x, which is another token classification subset from xtreme do represent the sentence boundary using a 2D array.
You may compare in PAN-x.en and udpos.English in the explorer:
https://huggingface.co/datasets/viewer/?dataset=xtreme | {
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https://api.github.com/repos/huggingface/datasets/issues/2443 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2443/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2443/comments | https://api.github.com/repos/huggingface/datasets/issues/2443/events | https://github.com/huggingface/datasets/issues/2443 | 909,983,574 | MDU6SXNzdWU5MDk5ODM1NzQ= | 2,443 | Some tests hang on Windows | {
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] | closed | false | null | [] | null | 3 | 2021-06-03T00:27:30 | 2021-06-28T08:47:39 | 2021-06-28T08:47:39 | CONTRIBUTOR | null | null | null | Currently, several tests hang on Windows if the max path limit of 260 characters is not disabled. This happens due to the changes introduced by #2223 that cause an infinite loop in `WindowsFileLock` described in #2220. This can be very tricky to debug, so I think now is a good time to address these issues/PRs. IMO throwing an error is too harsh, but maybe we can emit a warning in the top-level `__init__.py ` on startup if long paths are not enabled.
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https://api.github.com/repos/huggingface/datasets/issues/2442 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2442/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2442/comments | https://api.github.com/repos/huggingface/datasets/issues/2442/events | https://github.com/huggingface/datasets/pull/2442 | 909,677,029 | MDExOlB1bGxSZXF1ZXN0NjYwMjE1ODY1 | 2,442 | add english language tags for ~100 datasets | {
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} | As discussed on Slack, I have manually checked for ~100 datasets that they have at least one subset in English. This information was missing so adding into the READMEs.
Note that I didn't check all the subsets so it's possible that some of the datasets have subsets in other languages than English... | {
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https://api.github.com/repos/huggingface/datasets/issues/2441 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2441/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2441/comments | https://api.github.com/repos/huggingface/datasets/issues/2441/events | https://github.com/huggingface/datasets/issues/2441 | 908,554,713 | MDU6SXNzdWU5MDg1NTQ3MTM= | 2,441 | DuplicatedKeysError on personal dataset | {
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] | closed | false | null | [] | null | 2 | 2021-06-01T17:59:41 | 2021-06-04T23:50:03 | 2021-06-04T23:50:03 | NONE | null | null | null | ## Describe the bug
Ever since today, I have been getting a DuplicatedKeysError while trying to load my dataset from my own script.
Error returned when running this line: `dataset = load_dataset('/content/drive/MyDrive/Thesis/Datasets/book_preprocessing/goodreads_maharjan_trimmed_and_nered/goodreadsnered.py')`
Note that my script was working fine with earlier versions of the Datasets library. Cannot say with 100% certainty if I have been doing something wrong with my dataset script this whole time or if this is simply a bug with the new version of datasets.
## Steps to reproduce the bug
I cannot provide code to reproduce the error as I am working with my own dataset. I can however provide my script if requested.
## Expected results
For my data to be loaded.
## Actual results
**DuplicatedKeysError** exception is raised
```
Downloading and preparing dataset good_reads_practice_dataset/main_domain (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /root/.cache/huggingface/datasets/good_reads_practice_dataset/main_domain/1.1.0/64ff7c3fee2693afdddea75002eb6887d4fedc3d812ae3622128c8504ab21655...
---------------------------------------------------------------------------
DuplicatedKeysError Traceback (most recent call last)
<ipython-input-6-c342ea0dae9d> in <module>()
----> 1 dataset = load_dataset('/content/drive/MyDrive/Thesis/Datasets/book_preprocessing/goodreads_maharjan_trimmed_and_nered/goodreadsnered.py')
5 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, task, **config_kwargs)
749 try_from_hf_gcs=try_from_hf_gcs,
750 base_path=base_path,
--> 751 use_auth_token=use_auth_token,
752 )
753
/usr/local/lib/python3.7/dist-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)
573 if not downloaded_from_gcs:
574 self._download_and_prepare(
--> 575 dl_manager=dl_manager, verify_infos=verify_infos, **download_and_prepare_kwargs
576 )
577 # Sync info
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, **prepare_split_kwargs)
650 try:
651 # Prepare split will record examples associated to the split
--> 652 self._prepare_split(split_generator, **prepare_split_kwargs)
653 except OSError as e:
654 raise OSError(
/usr/local/lib/python3.7/dist-packages/datasets/builder.py in _prepare_split(self, split_generator)
990 writer.write(example, key)
991 finally:
--> 992 num_examples, num_bytes = writer.finalize()
993
994 split_generator.split_info.num_examples = num_examples
/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in finalize(self, close_stream)
407 # In case current_examples < writer_batch_size, but user uses finalize()
408 if self._check_duplicates:
--> 409 self.check_duplicate_keys()
410 # Re-intializing to empty list for next batch
411 self.hkey_record = []
/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in check_duplicate_keys(self)
347 for hash, key in self.hkey_record:
348 if hash in tmp_record:
--> 349 raise DuplicatedKeysError(key)
350 else:
351 tmp_record.add(hash)
DuplicatedKeysError: FAILURE TO GENERATE DATASET !
Found duplicate Key: 0
Keys should be unique and deterministic in nature
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.7.0
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.7.9
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2440 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2440/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2440/comments | https://api.github.com/repos/huggingface/datasets/issues/2440/events | https://github.com/huggingface/datasets/issues/2440 | 908,521,954 | MDU6SXNzdWU5MDg1MjE5NTQ= | 2,440 | Remove `extended` field from dataset tagger | {
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] | closed | false | null | [] | null | 4 | 2021-06-01T17:18:42 | 2021-06-09T09:06:31 | 2021-06-09T09:06:30 | MEMBER | null | null | null | ## Describe the bug
While working on #2435 I used the [dataset tagger](https://huggingface.co/datasets/tagging/) to generate the missing tags for the YAML metadata of each README.md file. However, it seems that our CI raises an error when the `extended` field is included:
```
dataset_name = 'arcd'
@pytest.mark.parametrize("dataset_name", get_changed_datasets(repo_path))
def test_changed_dataset_card(dataset_name):
card_path = repo_path / "datasets" / dataset_name / "README.md"
assert card_path.exists()
error_messages = []
try:
ReadMe.from_readme(card_path)
except Exception as readme_error:
error_messages.append(f"The following issues have been found in the dataset cards:\nREADME:\n{readme_error}")
try:
DatasetMetadata.from_readme(card_path)
except Exception as metadata_error:
error_messages.append(
f"The following issues have been found in the dataset cards:\nYAML tags:\n{metadata_error}"
)
if error_messages:
> raise ValueError("\n".join(error_messages))
E ValueError: The following issues have been found in the dataset cards:
E YAML tags:
E __init__() got an unexpected keyword argument 'extended'
tests/test_dataset_cards.py:70: ValueError
```
Consider either removing this tag from the tagger or including it as part of the validation step in the CI.
cc @yjernite | {
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https://api.github.com/repos/huggingface/datasets/issues/2439 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2439/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2439/comments | https://api.github.com/repos/huggingface/datasets/issues/2439/events | https://github.com/huggingface/datasets/pull/2439 | 908,511,983 | MDExOlB1bGxSZXF1ZXN0NjU5MTkzMDE3 | 2,439 | Better error message when trying to access elements of a DatasetDict without specifying the split | {
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"merged_at": "2021-06-07T08:54:35"
} | As mentioned in #2437 it'd be nice to to have an indication to the users when they try to access an element of a DatasetDict without specifying the split name.
cc @thomwolf | {
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https://api.github.com/repos/huggingface/datasets/issues/2438 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2438/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2438/comments | https://api.github.com/repos/huggingface/datasets/issues/2438/events | https://github.com/huggingface/datasets/pull/2438 | 908,461,914 | MDExOlB1bGxSZXF1ZXN0NjU5MTQ5Njg0 | 2,438 | Fix NQ features loading: reorder fields of features to match nested fields order in arrow data | {
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} | As mentioned in #2401, there is an issue when loading the features of `natural_questions` since the order of the nested fields in the features don't match. The order is important since it matters for the underlying arrow schema.
To fix that I re-order the features based on the arrow schema:
```python
inferred_features = Features.from_arrow_schema(arrow_table.schema)
self.info.features = self.info.features.reorder_fields_as(inferred_features)
assert self.info.features.type == inferred_features.type
```
The re-ordering is a recursive function. It takes into account that the `Sequence` feature type is a struct of list and not a list of struct.
Now it's possible to load `natural_questions` again :) | {
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https://api.github.com/repos/huggingface/datasets/issues/2437 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2437/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2437/comments | https://api.github.com/repos/huggingface/datasets/issues/2437/events | https://github.com/huggingface/datasets/pull/2437 | 908,108,882 | MDExOlB1bGxSZXF1ZXN0NjU4ODUwNTkw | 2,437 | Better error message when using the wrong load_from_disk | {
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} | As mentioned in #2424, the error message when one tries to use `Dataset.load_from_disk` to load a DatasetDict object (or _vice versa_) can be improved. I added a suggestion in the error message to let users know that they should use the other one. | {
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https://api.github.com/repos/huggingface/datasets/issues/2436 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2436/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2436/comments | https://api.github.com/repos/huggingface/datasets/issues/2436/events | https://github.com/huggingface/datasets/pull/2436 | 908,100,211 | MDExOlB1bGxSZXF1ZXN0NjU4ODQzMzQy | 2,436 | Update DatasetMetadata and ReadMe | {
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} | [] | closed | false | null | [] | null | 0 | 2021-06-01T09:32:37 | 2021-06-14T13:23:27 | 2021-06-14T13:23:26 | CONTRIBUTOR | null | false | {
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} | This PR contains the changes discussed in #2395.
**Edit**:
In addition to those changes, I'll be updating the `ReadMe` as follows:
Currently, `Section` has separate parsing and validation error lists. In `.validate()`, we add these lists to the final lists and throw errors.
One way to make `ReadMe` consistent with `DatasetMetadata` and add a separate `.validate()` method is to throw separate parsing and validation errors.
This way, we don't have to throw validation errors, but only parsing errors in `__init__ ()`. We can have an option in `__init__()` to suppress parsing errors so that an object is created for validation. Doing this will allow the user to get all the errors in one go.
In `test_dataset_cards` , we are already catching error messages and appending to a list. This can be done for `ReadMe()` for parsing errors, and `ReadMe(...,suppress_errors=True); readme.validate()` for validation, separately.
**Edit 2**:
The only parsing issue we have as of now is multiple headings at the same level with the same name. I assume this will happen very rarely, but it is still better to throw an error than silently pick one of them. It should be okay to separate it this way.
Wdyt @lhoestq ?
| {
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https://api.github.com/repos/huggingface/datasets/issues/2435 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2435/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2435/comments | https://api.github.com/repos/huggingface/datasets/issues/2435/events | https://github.com/huggingface/datasets/pull/2435 | 907,505,531 | MDExOlB1bGxSZXF1ZXN0NjU4MzQzNDE2 | 2,435 | Insert Extractive QA templates for SQuAD-like datasets | {
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} | This PR adds task templates for 9 SQuAD-like templates with the following properties:
* 1 config
* A schema that matches the `squad` one (i.e. same column names, especially for the nested `answers` column because the current implementation does not support casting with mismatched columns. see #2434)
* Less than 20GB (my laptop can't handle more right now)
The aim of this PR is to provide a few datasets to experiment with the task template integration in other libraries / services.
PR #2429 should be merged before this one.
cc @abhi1thakur | {
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https://api.github.com/repos/huggingface/datasets/issues/2434 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2434/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2434/comments | https://api.github.com/repos/huggingface/datasets/issues/2434/events | https://github.com/huggingface/datasets/issues/2434 | 907,503,557 | MDU6SXNzdWU5MDc1MDM1NTc= | 2,434 | Extend QuestionAnsweringExtractive template to handle nested columns | {
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] | open | false | null | [] | null | 1 | 2021-05-31T14:06:51 | 2021-06-17T08:21:30 | null | MEMBER | null | null | null | Currently the `QuestionAnsweringExtractive` task template and `preprare_for_task` only support "flat" features. We should extend the functionality to cover QA datasets like:
* `iapp_wiki_qa_squad`
* `parsinlu_reading_comprehension`
where the nested features differ with those from `squad` and trigger an `ArrowNotImplementedError`:
```
---------------------------------------------------------------------------
ArrowNotImplementedError Traceback (most recent call last)
<ipython-input-12-50e5b8f69c20> in <module>
----> 1 ds.prepare_for_task("question-answering-extractive")[0]
~/git/datasets/src/datasets/arrow_dataset.py in prepare_for_task(self, task)
1436 # We found a template so now flush `DatasetInfo` to skip the template update in `DatasetInfo.__post_init__`
1437 dataset.info.task_templates = None
-> 1438 dataset = dataset.cast(features=template.features)
1439 return dataset
1440
~/git/datasets/src/datasets/arrow_dataset.py in cast(self, features, batch_size, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, num_proc)
977 format = self.format
978 dataset = self.with_format("arrow")
--> 979 dataset = dataset.map(
980 lambda t: t.cast(schema),
981 batched=True,
~/git/datasets/src/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, desc)
1600
1601 if num_proc is None or num_proc == 1:
-> 1602 return self._map_single(
1603 function=function,
1604 with_indices=with_indices,
~/git/datasets/src/datasets/arrow_dataset.py in wrapper(*args, **kwargs)
176 }
177 # apply actual function
--> 178 out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
179 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out]
180 # re-apply format to the output
~/git/datasets/src/datasets/fingerprint.py in wrapper(*args, **kwargs)
395 # Call actual function
396
--> 397 out = func(self, *args, **kwargs)
398
399 # Update fingerprint of in-place transforms + update in-place history of transforms
~/git/datasets/src/datasets/arrow_dataset.py in _map_single(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, new_fingerprint, rank, offset, desc)
1940 ) # Something simpler?
1941 try:
-> 1942 batch = apply_function_on_filtered_inputs(
1943 batch,
1944 indices,
~/git/datasets/src/datasets/arrow_dataset.py in apply_function_on_filtered_inputs(inputs, indices, check_same_num_examples, offset)
1836 effective_indices = [i + offset for i in indices] if isinstance(indices, list) else indices + offset
1837 processed_inputs = (
-> 1838 function(*fn_args, effective_indices, **fn_kwargs) if with_indices else function(*fn_args, **fn_kwargs)
1839 )
1840 if update_data is None:
~/git/datasets/src/datasets/arrow_dataset.py in <lambda>(t)
978 dataset = self.with_format("arrow")
979 dataset = dataset.map(
--> 980 lambda t: t.cast(schema),
981 batched=True,
982 batch_size=batch_size,
~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.Table.cast()
~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.ChunkedArray.cast()
~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/compute.py in cast(arr, target_type, safe)
241 else:
242 options = CastOptions.unsafe(target_type)
--> 243 return call_function("cast", [arr], options)
244
245
~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/_compute.pyx in pyarrow._compute.call_function()
~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/_compute.pyx in pyarrow._compute.Function.call()
~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
~/miniconda3/envs/datasets/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowNotImplementedError: Unsupported cast from struct<answer_end: list<item: int32>, answer_start: list<item: int32>, text: list<item: string>> to struct using function cast_struct
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2433 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2433/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2433/comments | https://api.github.com/repos/huggingface/datasets/issues/2433/events | https://github.com/huggingface/datasets/pull/2433 | 907,488,711 | MDExOlB1bGxSZXF1ZXN0NjU4MzI5MDQ4 | 2,433 | Fix DuplicatedKeysError in adversarial_qa | {
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} | [] | closed | false | null | [] | null | 0 | 2021-05-31T13:48:47 | 2021-06-01T08:52:11 | 2021-06-01T08:52:11 | CONTRIBUTOR | null | false | {
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} | Fixes #2431 | {
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https://api.github.com/repos/huggingface/datasets/issues/2432 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2432/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2432/comments | https://api.github.com/repos/huggingface/datasets/issues/2432/events | https://github.com/huggingface/datasets/pull/2432 | 907,462,881 | MDExOlB1bGxSZXF1ZXN0NjU4MzA3MTE1 | 2,432 | Fix CI six installation on linux | {
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} | [] | closed | false | null | [] | null | 0 | 2021-05-31T13:15:36 | 2021-05-31T13:17:07 | 2021-05-31T13:17:06 | MEMBER | null | false | {
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} | For some reason we end up with this error in the linux CI when running pip install .[tests]
```
pip._vendor.resolvelib.resolvers.InconsistentCandidate: Provided candidate AlreadyInstalledCandidate(six 1.16.0 (/usr/local/lib/python3.6/site-packages)) does not satisfy SpecifierRequirement('six>1.9'), SpecifierRequirement('six>1.9'), SpecifierRequirement('six>=1.11'), SpecifierRequirement('six~=1.15'), SpecifierRequirement('six'), SpecifierRequirement('six>=1.5.2'), SpecifierRequirement('six>=1.9.0'), SpecifierRequirement('six>=1.11.0'), SpecifierRequirement('six'), SpecifierRequirement('six>=1.6.1'), SpecifierRequirement('six>=1.9'), SpecifierRequirement('six>=1.5'), SpecifierRequirement('six<2.0'), SpecifierRequirement('six<2.0'), SpecifierRequirement('six'), SpecifierRequirement('six'), SpecifierRequirement('six~=1.15.0'), SpecifierRequirement('six'), SpecifierRequirement('six<2.0,>=1.6.1'), SpecifierRequirement('six'), SpecifierRequirement('six>=1.5.2'), SpecifierRequirement('six>=1.9.0')
```
example CI failure here:
https://app.circleci.com/pipelines/github/huggingface/datasets/6200/workflows/b64fdec9-f9e6-431c-acd7-e9f2c440c568/jobs/38247
The main version requirement comes from tensorflow: `six~=1.15.0`
So I pinned the six version to this. | {
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https://api.github.com/repos/huggingface/datasets/issues/2431 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2431/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2431/comments | https://api.github.com/repos/huggingface/datasets/issues/2431/events | https://github.com/huggingface/datasets/issues/2431 | 907,413,691 | MDU6SXNzdWU5MDc0MTM2OTE= | 2,431 | DuplicatedKeysError when trying to load adversarial_qa | {
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] | closed | false | null | [] | null | 1 | 2021-05-31T12:11:19 | 2021-06-01T08:54:03 | 2021-06-01T08:52:11 | NONE | null | null | null | ## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
dataset = load_dataset('adversarial_qa', 'adversarialQA')
```
## Expected results
The dataset should be loaded into memory
## Actual results
>DuplicatedKeysError: FAILURE TO GENERATE DATASET !
>Found duplicate Key: 4d3cb5677211ee32895ca9c66dad04d7152254d4
>Keys should be unique and deterministic in nature
>
>
>During handling of the above exception, another exception occurred:
>
>DuplicatedKeysError Traceback (most recent call last)
>
>/usr/local/lib/python3.7/dist-packages/datasets/arrow_writer.py in check_duplicate_keys(self)
> 347 for hash, key in self.hkey_record:
> 348 if hash in tmp_record:
>--> 349 raise DuplicatedKeysError(key)
> 350 else:
> 351 tmp_record.add(hash)
>
>DuplicatedKeysError: FAILURE TO GENERATE DATASET !
>Found duplicate Key: 4d3cb5677211ee32895ca9c66dad04d7152254d4
>Keys should be unique and deterministic in nature
## Environment info
- `datasets` version: 1.7.0
- Platform: Linux-5.4.109+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.10
- PyArrow version: 3.0.0
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} | As pointed out by @lhoestq in #2411, after the creation of the Zenodo DOI for Datasets, a new BibTeX entry is created with each release.
This PR adds a version-specific BibTeX entry, besides the existing one which is generic for the project.
See version-specific BibTeX entry here: https://zenodo.org/record/4817769/export/hx#.YLSyd6j7RPY | {
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] | closed | false | null | [] | null | 6 | 2021-05-29T13:35:21 | 2021-06-02T01:21:03 | 2021-06-02T01:21:03 | NONE | null | null | null | Thanks for this amazing library! And my question is I have structured data that is organized with a graph. For example, a dataset with users' friendship relations and user's articles. When I try to save a python dict in the dataset, an error occurred ``did not recognize Python value type when inferring an Arrow data type''.
Although I also know that storing a python dict in pyarrow datasets is not the best practice, but I have no idea about how to save structured data in the Datasets.
Thank you very much for your help. | {
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} | PR to fix #2412 | {
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} | [] | closed | false | null | [] | null | 6 | 2021-05-28T23:07:10 | 2021-06-08T19:22:32 | 2021-06-08T19:22:32 | NONE | null | null | null | ## Describe the bug
load_from_disk and save_to_disk are not compatible. When I use save_to_disk to save a dataset to disk it works perfectly but given the same directory load_from_disk throws an error that it can't find state.json. looks like the load_from_disk only works on one split
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("art")
dataset.save_to_disk("mydir")
d = Dataset.load_from_disk("mydir")
```
## Expected results
It is expected that these two functions be the reverse of each other without more manipulation
## Actual results
FileNotFoundError: [Errno 2] No such file or directory: 'mydir/art/state.json'
## Environment info
- `datasets` version: 1.6.2
- Platform: Linux-5.4.0-73-generic-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.10
- PyTorch version (GPU?): 1.8.1+cu102 (True)
- Tensorflow version (GPU?): not installed (NA)
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>
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} | Fix issue https://github.com/huggingface/datasets/issues/2267
The order of the nested features matters (pyarrow limitation), but the save_to_disk method was saving the features types as JSON with `sort_keys=True`, which was breaking the order of the nested features. | {
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} | MAX_MEMORY_DATASET_SIZE_IN_BYTES should be HF_MAX_MEMORY_DATASET_SIZE_IN_BYTES | {
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} | Added Point of contact information and several other details about the dataset. | {
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https://api.github.com/repos/huggingface/datasets/issues/2418 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2418/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2418/comments | https://api.github.com/repos/huggingface/datasets/issues/2418/events | https://github.com/huggingface/datasets/pull/2418 | 904,051,497 | MDExOlB1bGxSZXF1ZXN0NjU1MjM2OTEz | 2,418 | add utf-8 while reading README | {
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} | It was causing tests to fail in Windows (see #2416). In Windows, the default encoding is CP1252 which is unable to decode the character byte 0x9d | {
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https://api.github.com/repos/huggingface/datasets/issues/2417 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2417/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2417/comments | https://api.github.com/repos/huggingface/datasets/issues/2417/events | https://github.com/huggingface/datasets/pull/2417 | 903,956,071 | MDExOlB1bGxSZXF1ZXN0NjU1MTU3NTI4 | 2,417 | Make datasets PEP-561 compliant | {
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} | Allows to type-check datasets with `mypy` when imported as a third-party library
PEP-561: https://www.python.org/dev/peps/pep-0561
MyPy doc on the subject: https://mypy.readthedocs.io/en/stable/installed_packages.html
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https://api.github.com/repos/huggingface/datasets/issues/2416 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2416/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2416/comments | https://api.github.com/repos/huggingface/datasets/issues/2416/events | https://github.com/huggingface/datasets/pull/2416 | 903,932,299 | MDExOlB1bGxSZXF1ZXN0NjU1MTM3NDUy | 2,416 | Add KLUE dataset | {
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} | Add `KLUE (Korean Language Understanding Evaluation)` dataset released recently from [paper](https://arxiv.org/abs/2105.09680), [github](https://github.com/KLUE-benchmark/KLUE) and [webpage](https://klue-benchmark.com/tasks).
Please let me know if there's anything missing in the code or README.
Thanks!
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] | closed | false | null | [] | null | 5 | 2021-05-27T15:40:06 | 2021-06-02T13:15:47 | 2021-06-02T13:15:47 | CONTRIBUTOR | null | null | null | ## Describe the bug
I have a large dataset (common_voice, english) where I use several map and filter functions.
Sometimes my cached datasets after specific functions are not loaded.
I always use the same arguments, same functions, no seed…
## Steps to reproduce the bug
```python
def filter_by_duration(batch):
return (
batch["duration"] <= 10
and batch["duration"] >= 1
and len(batch["target_text"]) > 5
)
def prepare_dataset(batch):
batch["input_values"] = processor(
batch["speech"], sampling_rate=batch["sampling_rate"][0]
).input_values
with processor.as_target_processor():
batch["labels"] = processor(batch["target_text"]).input_ids
return batch
train_dataset = train_dataset.filter(
filter_by_duration,
remove_columns=["duration"],
num_proc=data_args.preprocessing_num_workers,
)
# PROBLEM HERE -> below function is reexecuted and cache is not loaded
train_dataset = train_dataset.map(
prepare_dataset,
remove_columns=train_dataset.column_names,
batch_size=training_args.per_device_train_batch_size,
batched=True,
num_proc=data_args.preprocessing_num_workers,
)
# Later in script
set_caching_enabled(False)
# apply map on trained model to eval/test sets
```
## Expected results
The cached dataset should always be reloaded.
## Actual results
The function is reexecuted.
I have access to cached files `cache-xxxxx.arrow`.
Is there a way I can somehow load manually 2 versions and see how the hash was created for debug purposes (to know if it's an issue with dataset or function)?
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.6.2
- Platform: Linux-5.8.0-45-generic-x86_64-with-glibc2.29
- Python version: 3.8.5
- PyTorch version (GPU?): 1.8.1+cu102 (True)
- Tensorflow version (GPU?): not installed (NA)
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No | {
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https://api.github.com/repos/huggingface/datasets/issues/2414 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2414/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2414/comments | https://api.github.com/repos/huggingface/datasets/issues/2414/events | https://github.com/huggingface/datasets/pull/2414 | 903,877,096 | MDExOlB1bGxSZXF1ZXN0NjU1MDg5OTIw | 2,414 | Update README.md | {
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"merged_at": "2021-06-28T13:04:56"
} | Provides description of data instances and dataset features
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https://api.github.com/repos/huggingface/datasets/issues/2413 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2413/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2413/comments | https://api.github.com/repos/huggingface/datasets/issues/2413/events | https://github.com/huggingface/datasets/issues/2413 | 903,777,557 | MDU6SXNzdWU5MDM3Nzc1NTc= | 2,413 | AttributeError: 'DatasetInfo' object has no attribute 'task_templates' | {
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] | closed | false | null | [] | null | 1 | 2021-05-27T13:44:28 | 2021-06-01T01:05:47 | 2021-06-01T01:05:47 | CONTRIBUTOR | null | null | null | ## Describe the bug
Hello,
I'm trying to add dataset and contribute, but test keep fail with below cli.
` RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_<my_dataset>`
## Steps to reproduce the bug
It seems like a bug when I see an error with the existing dataset, not the dataset I'm trying to add.
` RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_<any_dataset>`
## Expected results
All test passed
## Actual results
```
# check that dataset is not empty
self.parent.assertListEqual(sorted(dataset_builder.info.splits.keys()), sorted(dataset))
for split in dataset_builder.info.splits.keys():
# check that loaded datset is not empty
self.parent.assertTrue(len(dataset[split]) > 0)
# check that we can cast features for each task template
> task_templates = dataset_builder.info.task_templates
E AttributeError: 'DatasetInfo' object has no attribute 'task_templates'
tests/test_dataset_common.py:175: AttributeError
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.6.2
- Platform: Darwin-20.4.0-x86_64-i386-64bit
- Python version: 3.7.7
- PyTorch version (GPU?): 1.7.0 (False)
- Tensorflow version (GPU?): 2.3.0 (False)
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No
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https://api.github.com/repos/huggingface/datasets/issues/2412 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2412/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2412/comments | https://api.github.com/repos/huggingface/datasets/issues/2412/events | https://github.com/huggingface/datasets/issues/2412 | 903,769,151 | MDU6SXNzdWU5MDM3NjkxNTE= | 2,412 | Docstring mistake: dataset vs. metric | {
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} | [] | closed | false | null | [] | null | 1 | 2021-05-27T13:39:11 | 2021-06-01T08:18:04 | 2021-06-01T08:18:04 | CONTRIBUTOR | null | null | null | This:
https://github.com/huggingface/datasets/blob/d95b95f8cf3cb0cff5f77a675139b584dcfcf719/src/datasets/load.py#L582
Should better be something like:
`a metric identifier on HuggingFace AWS bucket (list all available metrics and ids with ``datasets.list_metrics()``)`
I can provide a PR l8er... | {
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https://api.github.com/repos/huggingface/datasets/issues/2410 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2410/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2410/comments | https://api.github.com/repos/huggingface/datasets/issues/2410/events | https://github.com/huggingface/datasets/pull/2410 | 903,613,676 | MDExOlB1bGxSZXF1ZXN0NjU0ODUwMjY4 | 2,410 | fix #2391 add original answers in kilt-TriviaQA | {
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} | cc @yjernite is it ok like this? | {
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https://api.github.com/repos/huggingface/datasets/issues/2408 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2408/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2408/comments | https://api.github.com/repos/huggingface/datasets/issues/2408/events | https://github.com/huggingface/datasets/pull/2408 | 903,422,648 | MDExOlB1bGxSZXF1ZXN0NjU0NjgxMzE4 | 2,408 | Fix head_qa keys | {
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} | There were duplicate in the keys, as mentioned in #2382 | {
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] | closed | false | null | [] | null | 3 | 2021-05-27T01:54:26 | 2021-05-27T13:46:40 | 2021-05-27T13:46:40 | NONE | null | null | null | ## Describe the bug
I'm trying to save the result of datasets.map() to a specific file, so that I can easily share it among multiple computers without reprocessing the dataset. However, when I try to pass an argument 'cache_file_name' to the .map() function, it throws an error that ".map() function got an unexpected keyword argument 'cache_file_name'".
I believe I'm using the latest dataset 1.6.2. Also seems like the document and the actual code indicates there is an argument 'cache_file_name' for the .map() function.
Here is the code I use
## Steps to reproduce the bug
```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,
cache_file_name="my_tokenized_file"
)
```
## Actual results
tokenized_datasets = datasets.map(
TypeError: map() got an unexpected keyword argument 'cache_file_name'
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version:1.6.2
- Platform:Linux-4.18.0-193.28.1.el8_2.x86_64-x86_64-with-glibc2.10
- Python version:3.8.5
- PyArrow version:3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2406 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2406/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2406/comments | https://api.github.com/repos/huggingface/datasets/issues/2406/events | https://github.com/huggingface/datasets/issues/2406 | 902,643,844 | MDU6SXNzdWU5MDI2NDM4NDQ= | 2,406 | Add guide on using task templates to documentation | {
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] | null | 0 | 2021-05-26T16:28:26 | 2021-05-26T16:28:26 | null | MEMBER | null | null | null | Once we have a stable API on the text classification and question answering task templates, add a guide on how to use them in the documentation.
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https://api.github.com/repos/huggingface/datasets/issues/2405 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2405/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2405/comments | https://api.github.com/repos/huggingface/datasets/issues/2405/events | https://github.com/huggingface/datasets/pull/2405 | 901,227,658 | MDExOlB1bGxSZXF1ZXN0NjUyNzA2OTk1 | 2,405 | Add dataset tags | {
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} | The dataset tags were provided by Peter Clark following the guide. | {
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https://api.github.com/repos/huggingface/datasets/issues/2404 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2404/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2404/comments | https://api.github.com/repos/huggingface/datasets/issues/2404/events | https://github.com/huggingface/datasets/pull/2404 | 901,179,832 | MDExOlB1bGxSZXF1ZXN0NjUyNjYzOTcz | 2,404 | Paperswithcode dataset mapping | {
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} | This is a continuation of https://github.com/huggingface/huggingface_hub/pull/43, encoded directly inside dataset cards.
As discussed:
- `paperswithcode_id: null` when the dataset doesn't exist on paperswithcode's side.
- I've added this new key at the end of the yaml instead of ordering all keys alphabetically as pyyaml's default. No strong opinion on that one though
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https://api.github.com/repos/huggingface/datasets/issues/2403 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2403/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2403/comments | https://api.github.com/repos/huggingface/datasets/issues/2403/events | https://github.com/huggingface/datasets/pull/2403 | 900,059,014 | MDExOlB1bGxSZXF1ZXN0NjUxNjcxMTMw | 2,403 | Free datasets with cache file in temp dir on exit | {
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} | This PR properly cleans up the memory-mapped tables that reference the cache files inside the temp dir.
Since the built-in `_finalizer` of `TemporaryDirectory` can't be modified, this PR defines its own `TemporaryDirectory` class that accepts a custom clean-up function.
Fixes #2402 | {
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] | closed | false | null | [] | null | 0 | 2021-05-24T21:22:59 | 2021-05-26T16:39:29 | 2021-05-26T16:39:29 | CONTRIBUTOR | null | null | null | Currently, the following code raises a PermissionError on master if working on Windows:
```python
# run as a script or call exit() in REPL to initiate the temp dir cleanup
from datasets import *
d = load_dataset("sst", split="train", keep_in_memory=False)
set_caching_enabled(False)
d.map(lambda ex: ex)
```
Error stack trace:
```
Traceback (most recent call last):
File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\weakref.py", line 624, in _exitfunc
f()
File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\weakref.py", line 548, in __call__
return info.func(*info.args, **(info.kwargs or {}))
File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\tempfile.py", line 799, in _cleanup
_shutil.rmtree(name)
File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\shutil.py", line 500, in rmtree
return _rmtree_unsafe(path, onerror)
File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\shutil.py", line 395, in _rmtree_unsafe
onerror(os.unlink, fullname, sys.exc_info())
File "C:\Users\Mario\Anaconda3\envs\hf-datasets\lib\shutil.py", line 393, in _rmtree_unsafe
os.unlink(fullname)
PermissionError: [WinError 5] Access is denied: 'C:\\Users\\Mario\\AppData\\Local\\Temp\\tmp20epyhmq\\cache-87a87ffb5a956e68.arrow'
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/2401 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2401/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2401/comments | https://api.github.com/repos/huggingface/datasets/issues/2401/events | https://github.com/huggingface/datasets/issues/2401 | 899,910,521 | MDU6SXNzdWU4OTk5MTA1MjE= | 2,401 | load_dataset('natural_questions') fails with "ValueError: External features info don't match the dataset" | {
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] | null | 4 | 2021-05-24T18:38:53 | 2021-06-09T09:07:25 | 2021-06-09T09:07:25 | NONE | null | null | null | ## Describe the bug
load_dataset('natural_questions') throws ValueError
## Steps to reproduce the bug
```python
from datasets import load_dataset
datasets = load_dataset('natural_questions', split='validation[:10]')
```
## Expected results
Call to load_dataset returns data.
## Actual results
```
Using custom data configuration default
Reusing dataset natural_questions (/mnt/d/huggingface/datasets/natural_questions/default/0.0.2/19bc04755018a3ad02ee74f7045cde4ba9b4162cb64450a87030ab786b123b76)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-2-d55ab8a8cc1c> in <module>
----> 1 datasets = load_dataset('natural_questions', split='validation[:10]', cache_dir='/mnt/d/huggingface/datasets')
~/miniconda3/lib/python3.8/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)
756 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
757 )
--> 758 ds = builder_instance.as_dataset(split=split, ignore_verifications=ignore_verifications, in_memory=keep_in_memory)
759 if save_infos:
760 builder_instance._save_infos()
~/miniconda3/lib/python3.8/site-packages/datasets/builder.py in as_dataset(self, split, run_post_process, ignore_verifications, in_memory)
735
736 # Create a dataset for each of the given splits
--> 737 datasets = utils.map_nested(
738 partial(
739 self._build_single_dataset,
~/miniconda3/lib/python3.8/site-packages/datasets/utils/py_utils.py in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types)
193 # Singleton
194 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):
--> 195 return function(data_struct)
196
197 disable_tqdm = bool(logger.getEffectiveLevel() > INFO)
~/miniconda3/lib/python3.8/site-packages/datasets/builder.py in _build_single_dataset(self, split, run_post_process, ignore_verifications, in_memory)
762
763 # Build base dataset
--> 764 ds = self._as_dataset(
765 split=split,
766 in_memory=in_memory,
~/miniconda3/lib/python3.8/site-packages/datasets/builder.py in _as_dataset(self, split, in_memory)
838 in_memory=in_memory,
839 )
--> 840 return Dataset(**dataset_kwargs)
841
842 def _post_process(self, dataset: Dataset, resources_paths: Dict[str, str]) -> Optional[Dataset]:
~/miniconda3/lib/python3.8/site-packages/datasets/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint)
271 assert self._fingerprint is not None, "Fingerprint can't be None in a Dataset object"
272 if self.info.features.type != inferred_features.type:
--> 273 raise ValueError(
274 "External features info don't match the dataset:\nGot\n{}\nwith type\n{}\n\nbut expected something like\n{}\nwith type\n{}".format(
275 self.info.features, self.info.features.type, inferred_features, inferred_features.type
ValueError: External features info don't match the dataset:
Got
{'id': Value(dtype='string', id=None), 'document': {'title': Value(dtype='string', id=None), 'url': Value(dtype='string', id=None), 'html': Value(dtype='string', id=None), 'tokens': Sequence(feature={'token': Value(dtype='string', id=None), 'is_html': Value(dtype='bool', id=None)}, length=-1, id=None)}, 'question': {'text': Value(dtype='string', id=None), 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'annotations': Sequence(feature={'id': Value(dtype='string', id=None), 'long_answer': {'start_token': Value(dtype='int64', id=None), 'end_token': Value(dtype='int64', id=None), 'start_byte': Value(dtype='int64', id=None), 'end_byte': Value(dtype='int64', id=None)}, 'short_answers': Sequence(feature={'start_token': Value(dtype='int64', id=None), 'end_token': Value(dtype='int64', id=None), 'start_byte': Value(dtype='int64', id=None), 'end_byte': Value(dtype='int64', id=None), 'text': Value(dtype='string', id=None)}, length=-1, id=None), 'yes_no_answer': ClassLabel(num_classes=2, names=['NO', 'YES'], names_file=None, id=None)}, length=-1, id=None)}
with type
struct<annotations: struct<id: list<item: string>, long_answer: list<item: struct<start_token: int64, end_token: int64, start_byte: int64, end_byte: int64>>, short_answers: list<item: struct<end_byte: list<item: int64>, end_token: list<item: int64>, start_byte: list<item: int64>, start_token: list<item: int64>, text: list<item: string>>>, yes_no_answer: list<item: int64>>, document: struct<title: string, url: string, html: string, tokens: struct<is_html: list<item: bool>, token: list<item: string>>>, id: string, question: struct<text: string, tokens: list<item: string>>>
but expected something like
{'id': Value(dtype='string', id=None), 'document': {'html': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'tokens': {'is_html': Sequence(feature=Value(dtype='bool', id=None), length=-1, id=None), 'token': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'url': Value(dtype='string', id=None)}, 'question': {'text': Value(dtype='string', id=None), 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}, 'annotations': {'id': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'long_answer': [{'end_byte': Value(dtype='int64', id=None), 'end_token': Value(dtype='int64', id=None), 'start_byte': Value(dtype='int64', id=None), 'start_token': Value(dtype='int64', id=None)}], 'short_answers': [{'end_byte': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'end_token': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'start_byte': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'start_token': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'text': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}], 'yes_no_answer': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)}}
with type
struct<annotations: struct<id: list<item: string>, long_answer: list<item: struct<end_byte: int64, end_token: int64, start_byte: int64, start_token: int64>>, short_answers: list<item: struct<end_byte: list<item: int64>, end_token: list<item: int64>, start_byte: list<item: int64>, start_token: list<item: int64>, text: list<item: string>>>, yes_no_answer: list<item: int64>>, document: struct<html: string, title: string, tokens: struct<is_html: list<item: bool>, token: list<item: string>>, url: string>, id: string, question: struct<text: string, tokens: list<item: string>>>
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 1.6.2
- Platform: Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-glibc2.10
- Python version: 3.8.3
- PyTorch version (GPU?): 1.6.0 (False)
- Tensorflow version (GPU?): not installed (NA)
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No
| {
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https://api.github.com/repos/huggingface/datasets/issues/2400 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2400/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2400/comments | https://api.github.com/repos/huggingface/datasets/issues/2400/events | https://github.com/huggingface/datasets/issues/2400 | 899,867,212 | MDU6SXNzdWU4OTk4NjcyMTI= | 2,400 | Concatenate several datasets with removed columns is not working. | {
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] | closed | false | null | [] | null | 2 | 2021-05-24T17:40:15 | 2021-05-25T05:52:01 | 2021-05-25T05:51:59 | MEMBER | null | null | null | ## Describe the bug
You can't concatenate datasets when you removed columns before.
## Steps to reproduce the bug
```python
from datasets import load_dataset, concatenate_datasets
wikiann= load_dataset("wikiann","en")
wikiann["train"] = wikiann["train"].remove_columns(["langs","spans"])
wikiann["test"] = wikiann["test"].remove_columns(["langs","spans"])
assert wikiann["train"].features.type == wikiann["test"].features.type
concate = concatenate_datasets([wikiann["train"],wikiann["test"]])
```
## Expected results
Merged dataset
## Actual results
```python
ValueError: External features info don't match the dataset:
Got
{'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'ner_tags': Sequence(feature=ClassLabel(num_classes=7, names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC'], names_file=None, id=None), length=-1, id=None), 'langs': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'spans': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}
with type
struct<langs: list<item: string>, ner_tags: list<item: int64>, spans: list<item: string>, tokens: list<item: string>>
but expected something like
{'ner_tags': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)}
with type
struct<ner_tags: list<item: int64>, tokens: list<item: string>>
```
## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: ~1.6.2~ 1.5.0
- Platform: macos
- Python version: 3.8.5
- PyArrow version: 3.0.0
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https://api.github.com/repos/huggingface/datasets/issues/2399 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2399/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2399/comments | https://api.github.com/repos/huggingface/datasets/issues/2399/events | https://github.com/huggingface/datasets/pull/2399 | 899,853,610 | MDExOlB1bGxSZXF1ZXN0NjUxNDk0OTc2 | 2,399 | Add env variable for MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES | {
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} | [] | closed | false | null | [] | null | 5 | 2021-05-24T17:19:15 | 2021-05-27T09:07:15 | 2021-05-26T16:07:54 | MEMBER | null | false | {
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} | Add env variable for `MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES`.
This will allow to turn off default behavior: loading in memory (and not caching) small datasets.
Fix #2387. | {
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https://api.github.com/repos/huggingface/datasets/issues/2398 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2398/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2398/comments | https://api.github.com/repos/huggingface/datasets/issues/2398/events | https://github.com/huggingface/datasets/issues/2398 | 899,511,837 | MDU6SXNzdWU4OTk1MTE4Mzc= | 2,398 | News_commentary Dataset Translation Pairs are of Incorrect Language Specified Pairs | {
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] | open | false | null | [] | null | 0 | 2021-05-24T10:03:34 | 2021-05-24T10:03:34 | null | NONE | 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/2397 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/2397/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/2397/comments | https://api.github.com/repos/huggingface/datasets/issues/2397/events | https://github.com/huggingface/datasets/pull/2397 | 899,427,378 | MDExOlB1bGxSZXF1ZXN0NjUxMTMxMTY0 | 2,397 | Fix number of classes in indic_glue sna.bn dataset | {
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} | [] | closed | false | null | [] | null | 2 | 2021-05-24T08:18:55 | 2021-05-25T16:32:16 | 2021-05-25T16:32:16 | MEMBER | null | false | {
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} | As read in the [paper](https://www.aclweb.org/anthology/2020.findings-emnlp.445.pdf), Table 11. | {
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