|
--- |
|
datasets: |
|
- gguichard/coref_dataset |
|
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], |
|
# ..., |
|
``` |
|
|
