Correct data structure for PAN-X task in XTREME dataset?
Thanks for noticing ! This looks more reasonable indeed.
Feel free to open a PR Hi @lhoestq 
I made the proposed changes to the `xtreme.py` script. I noticed that I also need to change the schema in the `dataset_infos.json` file.  More specifically the `"features"` part of the PAN-X.LANG dataset:

```json
"features":{
   "word":{
      "dtype":"string",
      "id":null,
      "_type":"Value"
   },
   "ner_tag":{
      "dtype":"string",
      "id":null,
      "_type":"Value"
   },
   "lang":{
      "dtype":"string",
      "id":null,
      "_type":"Value"
   }
}
```
To fit the code above the fields `"word"`, `"ner_tag"`, and `"lang"` would become `"words"`, `ner_tags"` and `"langs"`. In addition the `dtype` should be changed from `"string"` to `"list"`.

 I made this changes but when trying to test this locally with `dataset = load_dataset("xtreme", "PAN-X.en", data_dir='./data')` I face the issue that the `dataset_info.json` file is always overwritten by a downloaded version with the old settings, which then throws an error because the schema does not match. This makes it hard to test the changes locally. Do you have any suggestions on how to deal with that?
 Hi !

You have to point to your local script.
First clone the repo and then:

```python
dataset = load_dataset("./datasets/xtreme", "PAN-X.en")
```
The "xtreme" directory contains "xtreme.py".

You also have to change the features definition in the `_info` method. You could use:

```python
features = nlp.Features({
    "words": [nlp.Value("string")],
    "ner_tags": [nlp.Value("string")],
    "langs": [nlp.Value("string")],
})
```

Hope this helps !
Let me know if you have other questions. Thanks, I am making progress. I got a new error `NonMatchingSplitsSizesError ` (see traceback below), which I suspect is due to the fact that number of rows in the dataset changed (one row per word --> one row per sentence) as well as the number of bytes due to the slightly updated data structure. 

```python
NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='validation', num_bytes=1756492, num_examples=80536, dataset_name='xtreme'), 'recorded': SplitInfo(name='validation', num_bytes=1837109, num_examples=10000, dataset_name='xtreme')}, {'expected': SplitInfo(name='test', num_bytes=1752572, num_examples=80326, dataset_name='xtreme'), 'recorded': SplitInfo(name='test', num_bytes=1833214, num_examples=10000, dataset_name='xtreme')}, {'expected': SplitInfo(name='train', num_bytes=3496832, num_examples=160394, dataset_name='xtreme'), 'recorded': SplitInfo(name='train', num_bytes=3658428, num_examples=20000, dataset_name='xtreme')}]
```
I can fix the error by replacing the values in the `datasets_infos.json` file, which I tested for English. However, to update this for all 40 datasets manually is slightly painful. Is there a better way to update the expected values for all datasets? You can update the json file by calling
```
nlp-cli test ./datasets/xtreme --save_infos --all_configs
``` One more thing about features. I mentioned

```python
features = nlp.Features({
    "words": [nlp.Value("string")],
    "ner_tags": [nlp.Value("string")],
    "langs": [nlp.Value("string")],
})
```

but it's actually not consistent with the way we write datasets. Something like this is simpler to read and more consistent with the way we define datasets:

```python
features = nlp.Features({
    "words": nlp.Sequence(nlp.Value("string")),
    "ner_tags": nlp.Sequence(nlp.Value("string")),
    "langs": nlp.Sequence(nlp.Value("string")),
})
```

Sorry about that Closing this since PR #437 fixed the problem and has been merged to `master`. 
Hi 🤗  team!

## Description of the problem
Thanks to the fix from #416 I am now able to load the NER task in the XTREME dataset as follows:

```python
from nlp import load_dataset
# AmazonPhotos.zip is located in data/
dataset = load_dataset("xtreme", "PAN-X.en", data_dir='./data')
dataset_train = dataset['train']
```

However, I am not sure that `load_dataset()` is returning the correct data structure for NER. 

Currently, every row in `dataset_train` is of the form
```python
{'word': str, 'ner_tag': str, 'lang': str}
```
but I think we actually want something like
```python
{'words': List[str], 'ner_tags': List[str], 'langs': List[str]}
```
so that each row corresponds to a _sequence_ of words associated with each example. With the current data structure I do not think it is possible to transform `dataset_train` into a form suitable for training because we do not know the boundaries between examples.

Indeed, [this line](https://github.com/google-research/xtreme/blob/522434d1aece34131d997a97ce7e9242a51a688a/third_party/utils_tag.py#L58) in the XTREME repo, processes the texts as lists of sentences, tags, and languages.

## Proposed solution
Replace
```python
with open(filepath) as f:
    data = csv.reader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
    for id_, row in enumerate(data):
        if row:
            lang, word = row[0].split(":")[0], row[0].split(":")[1]
            tag = row[1]
            yield id_, {"word": word, "ner_tag": tag, "lang": lang}
```
from  [these lines](https://github.com/huggingface/nlp/blob/ce7d3a1d630b78fe27188d1706f3ea980e8eec43/datasets/xtreme/xtreme.py#L881-L887) of the `_generate_examples()` function with something like

```python
guid_index = 1
with open(filepath, encoding="utf-8") as f:
    words = []
    ner_tags = []
    langs = []
    for line in f:
        if line.startswith("-DOCSTART-") or line == "" or line == "\n":
            if words:
                yield guid_index, {"words": words, "ner_tags": ner_tags, "langs": langs}
                guid_index += 1
                words = []
                ner_tags = []
        else:
            # pan-x data is tab separated
            splits = line.split("\t")
            # strip out en: prefix
            langs.append(splits[0][:2])
            words.append(splits[0][3:])
            if len(splits) > 1:
                labels.append(splits[-1].replace("\n", ""))
            else:
                # examples have no label in test set
                labels.append("O")
```
If you agree, me or @lvwerra would be happy to implement this and create a PR.