Update README.md

README.md

@@ -4,4 +4,151 @@ datasets:
 language:
 - fr
 library_name: transformers
----
+---
+
+# CamemBERT: a Tasty French Language Model
+
+## Table of Contents
+- [Model Details](#model-details)
+- [Uses](#uses)
+- [Risks, Limitations and Biases](#risks-limitations-and-biases)
+- [Training](#training)
+- [Evaluation](#evaluation)
+- [Citation Information](#citation-information)
+- [How to Get Started With the Model](#how-to-get-started-with-the-model)
+
+- ## Model Details
+- **Model Description:**
+This model is a state-of-the-art language model for French coreference resolution.
+- **Developed by:** Grégory Guichard
+- **Model Type:** Token Classification
+- **Language(s):** French
+- **License:** MIT
+- **Parent Model:** See the [Camembert-large model](https://huggingface.co/camembert/camembert-large) for more information about the RoBERTa   base model.
+- **Resources for more information:**
+
+  
+## Uses
+
+#### Direct Use
+
+This model can be used for Token Classification tasks.
+
+
+## Risks, Limitations and Biases
+**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.**
+
+Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
+
+This model was pretrained on a subcorpus of OSCAR multilingual corpus. Some of the limitations and risks associated with the OSCAR dataset, which are further detailed in the [OSCAR dataset card](https://huggingface.co/datasets/oscar), include the following: 
+
+> The quality of some OSCAR sub-corpora might be lower than expected, specifically for the lowest-resource languages.
+
+> Constructed from Common Crawl, Personal and sensitive information might be present.
+
+
+
+## Training
+
+
+#### Training Data
+OSCAR or Open Super-large Crawled Aggregated coRpus is a multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the Ungoliant architecture.
+
+
+#### Training Procedure
+
+| Model                          | #params                        | Arch. | Training data                     |
+|--------------------------------|--------------------------------|-------|-----------------------------------|
+| `camembert-base` | 110M   | Base  | OSCAR (138 GB of text)            |
+| `camembert/camembert-large`              | 335M    | Large | CCNet (135 GB of text)            |
+| `camembert/camembert-base-ccnet`         | 110M    | Base  | CCNet (135 GB of text)            |
+| `camembert/camembert-base-wikipedia-4gb` | 110M    | Base  | Wikipedia (4 GB of text)          |
+| `camembert/camembert-base-oscar-4gb`     | 110M    | Base  | Subsample of OSCAR (4 GB of text) |
+| `camembert/camembert-base-ccnet-4gb`     | 110M    | Base  | Subsample of CCNet (4 GB of text) |
+
+## Evaluation
+
+
+The model developers evaluated CamemBERT using four different downstream tasks for French: part-of-speech (POS) tagging, dependency parsing, named entity recognition (NER) and natural language inference (NLI).
+
+
+
+## Citation Information
+
+```bibtex
+@inproceedings{martin2020camembert,
+  title={CamemBERT: a Tasty French Language Model},
+  author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t},
+  booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
+  year={2020}
+}
+```
+
+## How to Get Started With the Model
+
+##### Load CamemBERT and its sub-word tokenizer :
+```python
+from transformers import CamembertModel, CamembertTokenizer
+
+# You can replace "camembert-base" with any other model from the table, e.g. "camembert/camembert-large".
+tokenizer = CamembertTokenizer.from_pretrained("camembert-base")
+camembert = CamembertModel.from_pretrained("camembert-base")
+
+camembert.eval()  # disable dropout (or leave in train mode to finetune)
+
+```
+
+##### Filling masks using pipeline 
+```python
+from transformers import pipeline 
+
+camembert_fill_mask  = pipeline("fill-mask", model="camembert-base", tokenizer="camembert-base")
+results = camembert_fill_mask("Le camembert est <mask> :)")
+# results
+#[{'sequence': '<s> Le camembert est délicieux :)</s>', 'score': 0.4909103214740753, 'token': 7200},
+# {'sequence': '<s> Le camembert est excellent :)</s>', 'score': 0.10556930303573608, 'token': 2183}, 
+# {'sequence': '<s> Le camembert est succulent :)</s>', 'score': 0.03453315049409866, 'token': 26202}, 
+# {'sequence': '<s> Le camembert est meilleur :)</s>', 'score': 0.03303130343556404, 'token': 528}, 
+# {'sequence': '<s> Le camembert est parfait :)</s>', 'score': 0.030076518654823303, 'token': 1654}]
+
+```
+
+##### Extract contextual embedding features from Camembert output 
+```python
+import torch
+# Tokenize in sub-words with SentencePiece
+tokenized_sentence = tokenizer.tokenize("J'aime le camembert !")
+# ['▁J', "'", 'aime', '▁le', '▁ca', 'member', 't', '▁!'] 
+
+# 1-hot encode and add special starting and end tokens 
+encoded_sentence = tokenizer.encode(tokenized_sentence)
+# [5, 121, 11, 660, 16, 730, 25543, 110, 83, 6] 
+# NB: Can be done in one step : tokenize.encode("J'aime le camembert !")
+
+# Feed tokens to Camembert as a torch tensor (batch dim 1)
+encoded_sentence = torch.tensor(encoded_sentence).unsqueeze(0)
+embeddings, _ = camembert(encoded_sentence)
+# embeddings.detach()
+# embeddings.size torch.Size([1, 10, 768])
+# tensor([[[-0.0254,  0.0235,  0.1027,  ..., -0.1459, -0.0205, -0.0116],
+#         [ 0.0606, -0.1811, -0.0418,  ..., -0.1815,  0.0880, -0.0766],
+#         [-0.1561, -0.1127,  0.2687,  ..., -0.0648,  0.0249,  0.0446],
+#         ...,
+```
+
+##### Extract contextual embedding features from all Camembert layers
+```python
+from transformers import CamembertConfig
+# (Need to reload the model with new config)
+config = CamembertConfig.from_pretrained("camembert-base", output_hidden_states=True)
+camembert = CamembertModel.from_pretrained("camembert-base", config=config)
+
+embeddings, _, all_layer_embeddings = camembert(encoded_sentence)
+#  all_layer_embeddings list of len(all_layer_embeddings) == 13 (input embedding layer + 12 self attention layers)
+all_layer_embeddings[5]
+# layer 5 contextual embedding : size torch.Size([1, 10, 768])
+#tensor([[[-0.0032,  0.0075,  0.0040,  ..., -0.0025, -0.0178, -0.0210],
+#         [-0.0996, -0.1474,  0.1057,  ..., -0.0278,  0.1690, -0.2982],
+#         [ 0.0557, -0.0588,  0.0547,  ..., -0.0726, -0.0867,  0.0699],
+#         ...,
+```