Adds UmBERTo: an Italian Language Model trained with Whole Word Masking
Adds umBERTo to Model architectures list

References and benchmarks:
https://github.com/musixmatchresearch/umberto
Thanks @loretoparisi that is awesome! 

We _should_ be able to just load it using `RobertaModel` or `AutoModel`, though. I'll see if we need to make changes to enable this.<|||||>Work in progress on the (remote) tokenizer config in https://github.com/huggingface/transformers/pull/2535

<|||||>@julien-c just checking if there is anything we have to do by our side for this PR. Thank you 🤗 <|||||>[ Umberto Tokenizer ]
Hi @julien-c @thomwolf,
when we try lo load umberto tokenizer with Autotokenizer, this error occurs.
I would like to remember that Umberto Tokenizer inherits from a Roberta Tokenizer

```
>>> tokenizer = AutoTokenizer.from_pretrained("Musixmatch/umberto-commoncrawl-cased-v1")
```
```
I0121 16:22:33.957683 139667243427648 tokenization_utils.py:327] Model name 'Musixmatch/umberto-commoncrawl-cased-v1' not found in model shortcut name list (bert-base-uncased, bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, bert-base-multilingual-cased, bert-base-chinese, bert-base-german-cased, bert-large-uncased-whole-word-masking, bert-large-cased-whole-word-masking, bert-large-uncased-whole-word-masking-finetuned-squad, bert-large-cased-whole-word-masking-finetuned-squad, bert-base-cased-finetuned-mrpc, bert-base-german-dbmdz-cased, bert-base-german-dbmdz-uncased, bert-base-finnish-cased-v1, bert-base-finnish-uncased-v1). Assuming 'Musixmatch/umberto-commoncrawl-cased-v1' is a path or url to a directory containing tokenizer files.
I0121 16:22:33.957921 139667243427648 tokenization_utils.py:359] Didn't find file Musixmatch/umberto-commoncrawl-cased-v1/added_tokens.json. We won't load it.
I0121 16:22:33.957994 139667243427648 tokenization_utils.py:359] Didn't find file Musixmatch/umberto-commoncrawl-cased-v1/special_tokens_map.json. We won't load it.
I0121 16:22:33.958091 139667243427648 tokenization_utils.py:359] Didn't find file Musixmatch/umberto-commoncrawl-cased-v1/tokenizer_config.json. We won't load it.
I0121 16:22:34.470488 139667243427648 tokenization_utils.py:398] loading file https://s3.amazonaws.com/models.huggingface.co/bert/Musixmatch/umberto-commoncrawl-cased-v1/vocab.txt from cache at /root/.cache/torch/transformers/d12b9cd215cbedbd1b21cbb1ab8663b6f1990a661d07b4e8ffafab79f02cfc21
I0121 16:22:34.470605 139667243427648 tokenization_utils.py:395] loading file None
I0121 16:22:34.470653 139667243427648 tokenization_utils.py:395] loading file None
I0121 16:22:34.470714 139667243427648 tokenization_utils.py:395] loading file None
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python3.6/dist-packages/transformers/tokenization_auto.py", line 143, in from_pretrained
    return BertTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils.py", line 302, in from_pretrained
    return cls._from_pretrained(*inputs, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils.py", line 438, in _from_pretrained
    tokenizer = cls(*init_inputs, **init_kwargs)
  File "/usr/local/lib/python3.6/dist-packages/transformers/tokenization_bert.py", line 164, in __init__
    "model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file))
ValueError: Can't find a vocabulary file at path '/root/.cache/torch/transformers/d12b9cd215cbedbd1b21cbb1ab8663b6f1990a661d07b4e8ffafab79f02cfc21'. To load the vocabulary from a Google pretrained model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`
```<|||||>[ Umberto Model ]

Also for Umberto model loading some strange things happen.
Same as tokenizer, Umberto Model inherits from Roberta Model, not from BertModel.

```>>> umberto = AutoModel.from_pretrained("Musixmatch/umberto-commoncrawl-cased-v1")
I0121 16:24:03.242502 139667243427648 file_utils.py:362] https://s3.amazonaws.com/models.huggingface.co/bert/Musixmatch/umberto-commoncrawl-cased-v1/config.json not found in cache or force_download set to True, downloading to /tmp/tmpehg26ac4
I0121 16:24:03.746079 139667243427648 file_utils.py:377] copying /tmp/tmpehg26ac4 to cache at /root/.cache/torch/transformers/d4d9f43ce6f9f572d223e54ac6184f961c91f180333f42ba436b19060da64177.446c3bed6ceafbbbe9d3b49b0ae276ee73ef572bdfb42fd076c3ee5e6425952e
I0121 16:24:03.746514 139667243427648 file_utils.py:381] creating metadata file for /root/.cache/torch/transformers/d4d9f43ce6f9f572d223e54ac6184f961c91f180333f42ba436b19060da64177.446c3bed6ceafbbbe9d3b49b0ae276ee73ef572bdfb42fd076c3ee5e6425952e
I0121 16:24:03.747002 139667243427648 file_utils.py:390] removing temp file /tmp/tmpehg26ac4
I0121 16:24:03.747236 139667243427648 configuration_utils.py:185] loading configuration file https://s3.amazonaws.com/models.huggingface.co/bert/Musixmatch/umberto-commoncrawl-cased-v1/config.json from cache at /root/.cache/torch/transformers/d4d9f43ce6f9f572d223e54ac6184f961c91f180333f42ba436b19060da64177.446c3bed6ceafbbbe9d3b49b0ae276ee73ef572bdfb42fd076c3ee5e6425952e
I0121 16:24:03.747532 139667243427648 configuration_utils.py:199] Model config {
  "attention_probs_dropout_prob": 0.1,
  "finetuning_task": null,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "id2label": {
    "0": "LABEL_0",
    "1": "LABEL_1"
  },
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "is_decoder": false,
  "label2id": {
    "LABEL_0": 0,
    "LABEL_1": 1
  },
  "layer_norm_eps": 1e-05,
  "max_position_embeddings": 514,
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "num_labels": 2,
  "output_attentions": false,
  "output_hidden_states": false,
  "output_past": true,
  "pruned_heads": {},
  "torchscript": false,
  "type_vocab_size": 1,
  "use_bfloat16": false,
  "vocab_size": 32005
}

I0121 16:24:04.271546 139667243427648 file_utils.py:362] https://s3.amazonaws.com/models.huggingface.co/bert/Musixmatch/umberto-commoncrawl-cased-v1/pytorch_model.bin not found in cache or force_download set to True, downloading to /tmp/tmp6e0jg2r3


I0121 16:25:02.123574 139667243427648 file_utils.py:377] copying /tmp/tmp6e0jg2r3 to cache at /root/.cache/torch/transformers/faef12cb7f68b2ecafeaed7e33b6fcdbb1772a607d0b602e244d5eab2e5e6dbc.38b3b456347cfe45fa37a33fe149652d871db4c2f36947993be2c6efbdce9356
I0121 16:25:02.441211 139667243427648 file_utils.py:381] creating metadata file for /root/.cache/torch/transformers/faef12cb7f68b2ecafeaed7e33b6fcdbb1772a607d0b602e244d5eab2e5e6dbc.38b3b456347cfe45fa37a33fe149652d871db4c2f36947993be2c6efbdce9356
I0121 16:25:02.441466 139667243427648 file_utils.py:390] removing temp file /tmp/tmp6e0jg2r3
I0121 16:25:02.484607 139667243427648 modeling_utils.py:406] loading weights file https://s3.amazonaws.com/models.huggingface.co/bert/Musixmatch/umberto-commoncrawl-cased-v1/pytorch_model.bin from cache at /root/.cache/torch/transformers/faef12cb7f68b2ecafeaed7e33b6fcdbb1772a607d0b602e244d5eab2e5e6dbc.38b3b456347cfe45fa37a33fe149652d871db4c2f36947993be2c6efbdce9356
I0121 16:25:03.654651 139667243427648 modeling_utils.py:480] Weights of BertModel not initialized from pretrained model: ['embeddings.word_embeddings.weight', 'embeddings.position_embeddings.weight', 'embeddings.token_type_embeddings.weight', 'embeddings.LayerNorm.weight', 'embeddings.LayerNorm.bias', 'encoder.layer.0.attention.self.query.weight', 'encoder.layer.0.attention.self.query.bias', 'encoder.layer.0.attention.self.key.weight', 'encoder.layer.0.attention.self.key.bias', 'encoder.layer.0.attention.self.value.weight', 'encoder.layer.0.attention.self.value.bias', 'encoder.layer.0.attention.output.dense.weight', 'encoder.layer.0.attention.output.dense.bias', 'encoder.layer.0.attention.output.LayerNorm.weight', 'encoder.layer.0.attention.output.LayerNorm.bias', 'encoder.layer.0.intermediate.dense.weight', 'encoder.layer.0.intermediate.dense.bias', 'encoder.layer.0.output.dense.weight', 'encoder.layer.0.output.dense.bias', 'encoder.layer.0.output.LayerNorm.weight', 'encoder.layer.0.output.LayerNorm.bias', 'encoder.layer.1.attention.self.query.weight', 'encoder.layer.1.attention.self.query.bias', 'encoder.layer.1.attention.self.key.weight', 'encoder.layer.1.attention.self.key.bias', 'encoder.layer.1.attention.self.value.weight', 'encoder.layer.1.attention.self.value.bias', 'encoder.layer.1.attention.output.dense.weight', 'encoder.layer.1.attention.output.dense.bias', 'encoder.layer.1.attention.output.LayerNorm.weight', 'encoder.layer.1.attention.output.LayerNorm.bias', 'encoder.layer.1.intermediate.dense.weight', 'encoder.layer.1.intermediate.dense.bias', 'encoder.layer.1.output.dense.weight', 'encoder.layer.1.output.dense.bias', 'encoder.layer.1.output.LayerNorm.weight', 'encoder.layer.1.output.LayerNorm.bias', 'encoder.layer.2.attention.self.query.weight', 'encoder.layer.2.attention.self.query.bias', 'encoder.layer.2.attention.self.key.weight', 'encoder.layer.2.attention.self.key.bias', 'encoder.layer.2.attention.self.value.weight', 'encoder.layer.2.attention.self.value.bias', 'encoder.layer.2.attention.output.dense.weight', 'encoder.layer.2.attention.output.dense.bias', 'encoder.layer.2.attention.output.LayerNorm.weight', 'encoder.layer.2.attention.output.LayerNorm.bias', 'encoder.layer.2.intermediate.dense.weight', 'encoder.layer.2.intermediate.dense.bias', 'encoder.layer.2.output.dense.weight', 'encoder.layer.2.output.dense.bias', 'encoder.layer.2.output.LayerNorm.weight', 'encoder.layer.2.output.LayerNorm.bias', 'encoder.layer.3.attention.self.query.weight', 'encoder.layer.3.attention.self.query.bias', 'encoder.layer.3.attention.self.key.weight', 'encoder.layer.3.attention.self.key.bias', 'encoder.layer.3.attention.self.value.weight', 'encoder.layer.3.attention.self.value.bias', 'encoder.layer.3.attention.output.dense.weight', 'encoder.layer.3.attention.output.dense.bias', 'encoder.layer.3.attention.output.LayerNorm.weight', 'encoder.layer.3.attention.output.LayerNorm.bias', 'encoder.layer.3.intermediate.dense.weight', 'encoder.layer.3.intermediate.dense.bias', 'encoder.layer.3.output.dense.weight', 'encoder.layer.3.output.dense.bias', 'encoder.layer.3.output.LayerNorm.weight', 'encoder.layer.3.output.LayerNorm.bias', 'encoder.layer.4.attention.self.query.weight', 'encoder.layer.4.attention.self.query.bias', 'encoder.layer.4.attention.self.key.weight', 'encoder.layer.4.attention.self.key.bias', 'encoder.layer.4.attention.self.value.weight', 'encoder.layer.4.attention.self.value.bias', 'encoder.layer.4.attention.output.dense.weight', 'encoder.layer.4.attention.output.dense.bias', 'encoder.layer.4.attention.output.LayerNorm.weight', 'encoder.layer.4.attention.output.LayerNorm.bias', 'encoder.layer.4.intermediate.dense.weight', 'encoder.layer.4.intermediate.dense.bias', 'encoder.layer.4.output.dense.weight', 'encoder.layer.4.output.dense.bias', 'encoder.layer.4.output.LayerNorm.weight', 'encoder.layer.4.output.LayerNorm.bias', 'encoder.layer.5.attention.self.query.weight', 'encoder.layer.5.attention.self.query.bias', 'encoder.layer.5.attention.self.key.weight', 'encoder.layer.5.attention.self.key.bias', 'encoder.layer.5.attention.self.value.weight', 'encoder.layer.5.attention.self.value.bias', 'encoder.layer.5.attention.output.dense.weight', 'encoder.layer.5.attention.output.dense.bias', 'encoder.layer.5.attention.output.LayerNorm.weight', 'encoder.layer.5.attention.output.LayerNorm.bias', 'encoder.layer.5.intermediate.dense.weight', 'encoder.layer.5.intermediate.dense.bias', 'encoder.layer.5.output.dense.weight', 'encoder.layer.5.output.dense.bias', 'encoder.layer.5.output.LayerNorm.weight', 'encoder.layer.5.output.LayerNorm.bias', 'encoder.layer.6.attention.self.query.weight', 'encoder.layer.6.attention.self.query.bias', 'encoder.layer.6.attention.self.key.weight', 'encoder.layer.6.attention.self.key.bias', 'encoder.layer.6.attention.self.value.weight', 'encoder.layer.6.attention.self.value.bias', 'encoder.layer.6.attention.output.dense.weight', 'encoder.layer.6.attention.output.dense.bias', 'encoder.layer.6.attention.output.LayerNorm.weight', 'encoder.layer.6.attention.output.LayerNorm.bias', 'encoder.layer.6.intermediate.dense.weight', 'encoder.layer.6.intermediate.dense.bias', 'encoder.layer.6.output.dense.weight', 'encoder.layer.6.output.dense.bias', 'encoder.layer.6.output.LayerNorm.weight', 'encoder.layer.6.output.LayerNorm.bias', 'encoder.layer.7.attention.self.query.weight', 'encoder.layer.7.attention.self.query.bias', 'encoder.layer.7.attention.self.key.weight', 'encoder.layer.7.attention.self.key.bias', 'encoder.layer.7.attention.self.value.weight', 'encoder.layer.7.attention.self.value.bias', 'encoder.layer.7.attention.output.dense.weight', 'encoder.layer.7.attention.output.dense.bias', 'encoder.layer.7.attention.output.LayerNorm.weight', 'encoder.layer.7.attention.output.LayerNorm.bias', 'encoder.layer.7.intermediate.dense.weight', 'encoder.layer.7.intermediate.dense.bias', 'encoder.layer.7.output.dense.weight', 'encoder.layer.7.output.dense.bias', 'encoder.layer.7.output.LayerNorm.weight', 'encoder.layer.7.output.LayerNorm.bias', 'encoder.layer.8.attention.self.query.weight', 'encoder.layer.8.attention.self.query.bias', 'encoder.layer.8.attention.self.key.weight', 'encoder.layer.8.attention.self.key.bias', 'encoder.layer.8.attention.self.value.weight', 'encoder.layer.8.attention.self.value.bias', 'encoder.layer.8.attention.output.dense.weight', 'encoder.layer.8.attention.output.dense.bias', 'encoder.layer.8.attention.output.LayerNorm.weight', 'encoder.layer.8.attention.output.LayerNorm.bias', 'encoder.layer.8.intermediate.dense.weight', 'encoder.layer.8.intermediate.dense.bias', 'encoder.layer.8.output.dense.weight', 'encoder.layer.8.output.dense.bias', 'encoder.layer.8.output.LayerNorm.weight', 'encoder.layer.8.output.LayerNorm.bias', 'encoder.layer.9.attention.self.query.weight', 'encoder.layer.9.attention.self.query.bias', 'encoder.layer.9.attention.self.key.weight', 'encoder.layer.9.attention.self.key.bias', 'encoder.layer.9.attention.self.value.weight', 'encoder.layer.9.attention.self.value.bias', 'encoder.layer.9.attention.output.dense.weight', 'encoder.layer.9.attention.output.dense.bias', 'encoder.layer.9.attention.output.LayerNorm.weight', 'encoder.layer.9.attention.output.LayerNorm.bias', 'encoder.layer.9.intermediate.dense.weight', 'encoder.layer.9.intermediate.dense.bias', 'encoder.layer.9.output.dense.weight', 'encoder.layer.9.output.dense.bias', 'encoder.layer.9.output.LayerNorm.weight', 'encoder.layer.9.output.LayerNorm.bias', 'encoder.layer.10.attention.self.query.weight', 'encoder.layer.10.attention.self.query.bias', 'encoder.layer.10.attention.self.key.weight', 'encoder.layer.10.attention.self.key.bias', 'encoder.layer.10.attention.self.value.weight', 'encoder.layer.10.attention.self.value.bias', 'encoder.layer.10.attention.output.dense.weight', 'encoder.layer.10.attention.output.dense.bias', 'encoder.layer.10.attention.output.LayerNorm.weight', 'encoder.layer.10.attention.output.LayerNorm.bias', 'encoder.layer.10.intermediate.dense.weight', 'encoder.layer.10.intermediate.dense.bias', 'encoder.layer.10.output.dense.weight', 'encoder.layer.10.output.dense.bias', 'encoder.layer.10.output.LayerNorm.weight', 'encoder.layer.10.output.LayerNorm.bias', 'encoder.layer.11.attention.self.query.weight', 'encoder.layer.11.attention.self.query.bias', 'encoder.layer.11.attention.self.key.weight', 'encoder.layer.11.attention.self.key.bias', 'encoder.layer.11.attention.self.value.weight', 'encoder.layer.11.attention.self.value.bias', 'encoder.layer.11.attention.output.dense.weight', 'encoder.layer.11.attention.output.dense.bias', 'encoder.layer.11.attention.output.LayerNorm.weight', 'encoder.layer.11.attention.output.LayerNorm.bias', 'encoder.layer.11.intermediate.dense.weight', 'encoder.layer.11.intermediate.dense.bias', 'encoder.layer.11.output.dense.weight', 'encoder.layer.11.output.dense.bias', 'encoder.layer.11.output.LayerNorm.weight', 'encoder.layer.11.output.LayerNorm.bias', 'pooler.dense.weight', 'pooler.dense.bias']
I0121 16:25:03.654866 139667243427648 modeling_utils.py:483] Weights from pretrained model not used in BertModel: ['roberta.embeddings.word_embeddings.weight', 'roberta.embeddings.position_embeddings.weight', 'roberta.embeddings.token_type_embeddings.weight', 'roberta.embeddings.LayerNorm.weight', 'roberta.embeddings.LayerNorm.bias', 'roberta.encoder.layer.0.attention.self.query.weight', 'roberta.encoder.layer.0.attention.self.query.bias', 'roberta.encoder.layer.0.attention.self.key.weight', 'roberta.encoder.layer.0.attention.self.key.bias', 'roberta.encoder.layer.0.attention.self.value.weight', 'roberta.encoder.layer.0.attention.self.value.bias', 'roberta.encoder.layer.0.attention.output.dense.weight', 'roberta.encoder.layer.0.attention.output.dense.bias', 'roberta.encoder.layer.0.attention.output.LayerNorm.weight', 'roberta.encoder.layer.0.attention.output.LayerNorm.bias', 'roberta.encoder.layer.0.intermediate.dense.weight', 'roberta.encoder.layer.0.intermediate.dense.bias', 'roberta.encoder.layer.0.output.dense.weight', 'roberta.encoder.layer.0.output.dense.bias', 'roberta.encoder.layer.0.output.LayerNorm.weight', 'roberta.encoder.layer.0.output.LayerNorm.bias', 'roberta.encoder.layer.1.attention.self.query.weight', 'roberta.encoder.layer.1.attention.self.query.bias', 'roberta.encoder.layer.1.attention.self.key.weight', 'roberta.encoder.layer.1.attention.self.key.bias', 'roberta.encoder.layer.1.attention.self.value.weight', 'roberta.encoder.layer.1.attention.self.value.bias', 'roberta.encoder.layer.1.attention.output.dense.weight', 'roberta.encoder.layer.1.attention.output.dense.bias', 'roberta.encoder.layer.1.attention.output.LayerNorm.weight', 'roberta.encoder.layer.1.attention.output.LayerNorm.bias', 'roberta.encoder.layer.1.intermediate.dense.weight', 'roberta.encoder.layer.1.intermediate.dense.bias', 'roberta.encoder.layer.1.output.dense.weight', 'roberta.encoder.layer.1.output.dense.bias', 'roberta.encoder.layer.1.output.LayerNorm.weight', 'roberta.encoder.layer.1.output.LayerNorm.bias', 'roberta.encoder.layer.2.attention.self.query.weight', 'roberta.encoder.layer.2.attention.self.query.bias', 'roberta.encoder.layer.2.attention.self.key.weight', 'roberta.encoder.layer.2.attention.self.key.bias', 'roberta.encoder.layer.2.attention.self.value.weight', 'roberta.encoder.layer.2.attention.self.value.bias', 'roberta.encoder.layer.2.attention.output.dense.weight', 'roberta.encoder.layer.2.attention.output.dense.bias', 'roberta.encoder.layer.2.attention.output.LayerNorm.weight', 'roberta.encoder.layer.2.attention.output.LayerNorm.bias', 'roberta.encoder.layer.2.intermediate.dense.weight', 'roberta.encoder.layer.2.intermediate.dense.bias', 'roberta.encoder.layer.2.output.dense.weight', 'roberta.encoder.layer.2.output.dense.bias', 'roberta.encoder.layer.2.output.LayerNorm.weight', 'roberta.encoder.layer.2.output.LayerNorm.bias', 'roberta.encoder.layer.3.attention.self.query.weight', 'roberta.encoder.layer.3.attention.self.query.bias', 'roberta.encoder.layer.3.attention.self.key.weight', 'roberta.encoder.layer.3.attention.self.key.bias', 'roberta.encoder.layer.3.attention.self.value.weight', 'roberta.encoder.layer.3.attention.self.value.bias', 'roberta.encoder.layer.3.attention.output.dense.weight', 'roberta.encoder.layer.3.attention.output.dense.bias', 'roberta.encoder.layer.3.attention.output.LayerNorm.weight', 'roberta.encoder.layer.3.attention.output.LayerNorm.bias', 'roberta.encoder.layer.3.intermediate.dense.weight', 'roberta.encoder.layer.3.intermediate.dense.bias', 'roberta.encoder.layer.3.output.dense.weight', 'roberta.encoder.layer.3.output.dense.bias', 'roberta.encoder.layer.3.output.LayerNorm.weight', 'roberta.encoder.layer.3.output.LayerNorm.bias', 'roberta.encoder.layer.4.attention.self.query.weight', 'roberta.encoder.layer.4.attention.self.query.bias', 'roberta.encoder.layer.4.attention.self.key.weight', 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'lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight']```<|||||>Hi @loretoparisi and all,

- I've added a `"model_type": "camembert"` to both your config.json files on our S3, so tokenizer is now properly instantiated as a CamembertTokenizer (i.e. admit a `sentencepiece.bpe.model` file): https://s3.amazonaws.com/models.huggingface.co/bert/Musixmatch/umberto-commoncrawl-cased-v1/config.json
- I've uploaded the two `sentencepiece.bpe.model` files from your repo.

**So, doing the following should now work out of the box:**
```
tokenizer = AutoTokenizer.from_pretrained("Musixmatch/umberto-commoncrawl-cased-v1")
umberto = AutoModel.from_pretrained("Musixmatch/umberto-commoncrawl-cased-v1")
```
(same thing for the wikipedia model)

Can you check that it works fine now? I'll add shortcut names in a separate commit as the PR will be much shorter.

Finally, can you add a README.md file to the same folders on our S3, and it will be rendered on your model's page: https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1

You can use this file to describe your model, which datasets did you train on, eval results, etc.

Grazie mille!<|||||>@julien-c 👍 great! We are applying the changes! cc @simonefrancia<|||||>1) We tested `AutoTokenizer` and `AutoModel` with both `Musixmatch/umberto-commoncrawl-cased-v1` and `Musixmatch/umberto-wikipedia-uncased-v1` and this code worked:

```python
import torch
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("[name_tokenizer]") # do_lower_case=True if uncased
umberto = AutoModel.from_pretrained("[name_model]")

encoded_input = tokenizer.encode("Umberto Eco è stato un grande scrittore")
input_ids = torch.tensor(encoded_input).unsqueeze(0)  # Batch size 1
outputs = umberto(input_ids)
last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output
```

2) for the error [here](https://github.com/huggingface/transformers/pull/2661), we do testing in the same way and no error appears to us:

<img width="1676" alt="Schermata 2020-01-28 alle 10 48 59" src="https://user-images.githubusercontent.com/7140210/73260860-27325d00-41cb-11ea-9ba8-162d820559bc.png">
     So probably it's a Heisenbug


3) We did two README.md, one for each model:
 `Umberto-commoncrawl-cased` : [link](https://mxmdownloads.s3.amazonaws.com/umberto/README_UMBERTO_COMMONCRAWL.MD)
 `Umberto-wikipedia-uncased` : [link](https://mxmdownloads.s3.amazonaws.com/umberto/README_UMBERTO_WIKIPEDIA.MD)


That's all, if you need other, here we are cc @loretoparisi . Thanks!<|||||>Great, I've uploaded the READMEs to our S3 so that they'll be displayed on the model pages.

I've also uploaded this tokenizer_config.json so you don't need to specify `do_lower_case: true` anymore: https://s3.amazonaws.com/models.huggingface.co/bert/Musixmatch/umberto-wikipedia-uncased-v1/tokenizer_config.json

Finally, I've updated your READMEs slightly to:
- add more info from your repo's readme
- add an example use case for our new FillMaskPipeline:

```python
from transformers import pipeline

fill_mask = pipeline(
	"fill-mask",
	model="Musixmatch/umberto-wikipedia-uncased-v1",
	tokenizer="Musixmatch/umberto-wikipedia-uncased-v1"
)

result = fill_mask("Umberto Eco è <mask> un grande scrittore")
```

I'll close this issue and merge #2661 

Thanks again!<|||||>@julien-c  Hi, we saw from https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1 that we don't have the Tensorflow version of our model available for the community. How can we create and upload it? Thanks<|||||>Hi @simonefrancia, check out this comment: https://github.com/huggingface/transformers/issues/2901#issuecomment-591710959