Create README.md

README.md

@@ -0,0 +1,97 @@
+---
+license: cc-by-nc-sa-2.0
+language:
+- fr
+pipeline_tag: feature-extraction
+library_name: fairseq
+---
+
+# Pantagruel: Unified Self-Supervised Encoders for French Text and Speech
+
+**Summary**
+
+Pantagruel is a family of self-supervised encoder models for French text and speech, with separate models trained for each modality. Rather than relying only on masked input-level reconstruction, Pantagruel encoders learn contextualized representations in feature space following the [data2vec 2.0](https://arxiv.org/abs/2212.07525) / [JEPA (Joint-Embedding Predictive Architecture)](https://arxiv.org/abs/2301.08243) paradigm. 
+
+Pantagruel adopts data2vec 2.0-style teacher–student setup: a student encoder processes partially visible inputs and is trained to predict latent representations produced by a teacher encoder that observes the full, unmasked inputs. The teacher is implemented as an exponential moving average (EMA) of the student. This feature-space prediction objective is used for both speech and text models. For text, it is combined with an additional masked language modeling (MLM) loss to better capture fine-grained syntactic and semantic information.
+
+The models were pre-trained using `fairseq` library (v0.12.2) and converted to HuggingFacce's `transformers` format. For best compatibility, we recommend using `transformers==4.57.0` or `4.56.2`, together with `tokenizers==0.22.1` and `sentencepiece==0.1.99`.
+
+- **Paper**: https://arxiv.org/abs/2601.05911
+- **Pre-training code**: to be updated soon. 
+
+
+## Speech-only models
+
+Please refer to our [speech-only collection](https://huggingface.co/collections/PantagrueLLM/speech-only-models) for more details.
+
+## Text-only models
+
+Please refer to our [text-only collection](https://huggingface.co/collections/PantagrueLLM/text-only-models) for more details.
+
+## Text-only models
+Pantagruel text encoders are trained on large-scale French text corpora, including Wikipedia 2019, OSCAR 2019, and CroissantLLM. In addition to feature-space prediction, text models incorporate masked language modeling (MLM) to better capture fine-grained syntactic and semantic information. These models produce strong sentence and token-level representations for downstream NLP tasks.
+
+**Note on model naming convention:** Models that include `camtok` in their name use CamemBERT's tokenizer. If no tokenizer is specified, the model uses our custom tokenizer. All text-based models are trained using the data2vec 2.0 masked feature prediction objective. Models with an `MLM` suffix additionally incorporate the masked language modeling (MLM) objective alongside the main data2vec 2.0 objective.
+
+The table below presents the accuracy of the natural language inference task on the French XNLI dataset.
+
+| **Model name (HuggingFace / Paper)** | **Arch/ Params** | **Pretrained dataset** | **Accuracy on XNLI (FR) (dev / test)** | **Note** |
+|------------------------|-----------------|----------------------|---------------------------------------|----------|
+| text-base-camtok-wiki / Pantagruel-B-camtok-Wk | Base (110M) | French Wikipedia 2019 (4GB) | 76.94% / 77.43% | for ablation study purpose |
+| text-base-wiki / Pantagruel-B-Wk | Base (125M) | French Wikipedia 2019 (4GB) | 77.40% / 78.41% | for ablation study purpose |
+| text-base-wiki-mlm / Pantagruel-B-Wk-MLM | Base (125M) | French Wikipedia 2019 (4GB) | 78.25% / 78.41% | |
+| text-base-camtok-oscar / Pantagruel-B-camtok-Osc | Base (110M) | OSCAR 2019 (138GB) | 80.40% / 80.53% | |
+| text-base-oscar-mlm / Pantagruel-B-Osc-MLM | Base (125M) | OSCAR 2019 (138GB) | 81.11% / 81.52% | |
+| text-base-croissant-mlm / Pantagruel-B-Crs-MLM | Base (125M) | croissantLLM (1.5GB) | 80.91% / 81.05% | |
+
+For more downstream tasks and evaluation datasets, please refer to [our paper](https://arxiv.org/abs/2601.05911).
+
+## Usage
+Our models can be used with `AutoModel` and `AutoConfig` classes to extract features as below. Other common classes for text-related downstream tasks, including `AutoModelForMaskedLM`, `AutoModelForSequenceClassification`, `AutoModelForMultipleChoice`, `AutoModelForTokenClassification`, and `AutoModelForQuestionAnswering` are also supported. We are currently working to merge the modeling files into the official Hugging Face repository, which will enable native use of the `Pantagruel` classes.
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModel
+
+# Load the tokenizer and model
+model_name = "PantagrueLLM/text-base-oscar-mlm"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModel.from_pretrained(model_name, trust_remote_code=True)
+model.eval()
+
+# Example input
+sentences = [
+    "Bonjour, comment allez-vous ?",
+    "Le chat dort sur le tapis."
+]
+
+# Tokenize input
+inputs = tokenizer(
+    sentences,
+    padding=True,
+    truncation=True,
+    return_tensors="pt"
+)
+
+# Forward pass to get hidden states
+with torch.no_grad():
+    outputs = model(**inputs)
+
+# Token-level embeddings
+token_embeddings = outputs.last_hidden_state
+print(token_embeddings.shape)
+# Shape: (batch_size, sequence_length, hidden_size)
+```
+
+## Citation
+If you use these models or find them useful in your research, publications, or applications, please cite the following work:
+
+```bibtex
+@article{le2026pantagruel,
+  title={Pantagruel: Unified Self-Supervised Encoders for French Text and Speech},
+  author={Le, Phuong-Hang and Pelloin, Valentin and Chatelain, Arnault and Bouziane, Maryem and Ghennai, Mohammed and Guan, Qianwen and Milintsevich, Kirill and Mdhaffar, Salima and Mannion, Aidan and Defauw, Nils and others},
+  journal={arXiv preprint arXiv:2601.05911},
+  year={2026}
+}
+```
+For more information, see the full paper: https://arxiv.org/abs/2601.05911.