You can deploy this model and start earning money today!

almanach/
camembertav2-base-gsd

Name: camembertav2-base-gsd
Availability: InStock

almanach/camembertav2-base

finetune

mit

model-index

Views

No views yet

Model Card for almanach/camembertav2-base-gsd

almanach/camembertav2-base-gsd is a deberta-v2 model for token classification. It is trained on the GSD dataset for the task of Part-of-Speech Tagging and Dependency Parsing. The model achieves an f1 score of on the GSD dataset.

The model is part of the almanach/camembertav2-base family of model finetunes.

Model Details

Model Description

Developed by: Wissam Antoun (Phd Student at Almanach, Inria-Paris)
Model type: deberta-v2
Language(s) (NLP): French
License: MIT
Finetuned from model : almanach/camembertav2-base

Model Sources

Repository: https://github.com/WissamAntoun/camemberta
Paper: https://arxiv.org/abs/2411.08868

Uses

The model can be used for token classification tasks in French for Part-of-Speech Tagging and Dependency Parsing.

Bias, Risks, and Limitations

The model may exhibit biases based on the training data. The model may not generalize well to other datasets or tasks. The model may also have limitations in terms of the data it was trained on.

How to Get Started with the Model

You can use the models directly with the hopsparser library in server mode https://github.com/hopsparser/hopsparser/blob/main/docs/server.md

Training Details

Training Procedure

Model trained with the hopsparser library on the GSD dataset.

Training Hyperparameters

yml

# Layer dimensions
mlp_input: 1024
mlp_tag_hidden: 16
mlp_arc_hidden: 512
mlp_lab_hidden: 128
# Lexers
lexers:
  - name: word_embeddings
    type: words
    embedding_size: 256
    word_dropout: 0.5
  - name: char_level_embeddings
    type: chars_rnn
    embedding_size: 64
    lstm_output_size: 128
  - name: fasttext
    type: fasttext
  - name: camembertav2_base_p2_17k_last_layer
    type: bert
    model: /scratch/camembertv2/runs/models/camembertav2-base-bf16/post/ckpt-p2-17000/pt/discriminator/
    layers: [11]
    subwords_reduction: "mean"
# Training hyperparameters
encoder_dropout: 0.5
mlp_dropout: 0.5
batch_size: 8
epochs: 64
lr:
  base: 0.00003
  schedule:
    shape: linear
    warmup_steps: 100

Results

UPOS: 0.98572 LAS: 0.94517

Technical Specifications

Model Architecture and Objective

deberta-v2 custom model for token classification.

Citation

BibTeX:

bibtex

@misc{antoun2024camembert20smarterfrench,
      title={CamemBERT 2.0: A Smarter French Language Model Aged to Perfection},
      author={Wissam Antoun and Francis Kulumba and Rian Touchent and Éric de la Clergerie and Benoît Sagot and Djamé Seddah},
      year={2024},
      eprint={2411.08868},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2411.08868},
}

@inproceedings{grobol:hal-03223424,
    title = {Analyse en dépendances du français avec des plongements contextualisés},
    author = {Grobol, Loïc and Crabbé, Benoît},
    url = {https://hal.archives-ouvertes.fr/hal-03223424},
    booktitle = {Actes de la 28ème Conférence sur le Traitement Automatique des Langues Naturelles},
    eventtitle = {TALN-RÉCITAL 2021},
    venue = {Lille, France},
    pdf = {https://hal.archives-ouvertes.fr/hal-03223424/file/HOPS_final.pdf},
    hal_id = {hal-03223424},
    hal_version = {v1},
}