Model Card for dummy-model

Model Details

Model Description

This model is based on CamemBERT, a French language model built on the RoBERTa architecture. It is used for the fill-mask task, predicting masked tokens in French text.

• Developed by: [More Information Needed]
• Funded by [optional]: [More Information Needed]
• Shared by [optional]: farid678
• Model type: Transformer-based masked language model (RoBERTa architecture)
• Language(s) (NLP): French (fr)
• License: MIT
• Finetuned from model: camembert-base

Model Sources [optional]

• Repository: https://huggingface.co/farid678/dummy-model
• Paper: CamemBERT: a Tasty French Language Model
• Demo: [More Information Needed]

Uses

Direct Use

This model can be used directly for masked language modeling (fill-mask) on French text — predicting the most likely word(s) to fill in a <mask> token within a sentence.

Downstream Use [optional]

The underlying CamemBERT architecture can be fine-tuned for downstream French NLP tasks such as text classification, named entity recognition, part-of-speech tagging, and question answering.

Out-of-Scope Use

This model is not intended for languages other than French, and should not be used to generate factual claims, as masked language models are not designed for reliable factual generation.

Bias, Risks, and Limitations

As with other large pretrained language models trained on web-scraped text, this model may reflect social, cultural, or gender biases present in its training data. Predictions should not be used in sensitive or high-stakes applications without further evaluation.

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model. Evaluate the model's outputs for bias before deploying in production use cases.

How to Get Started with the Model

from transformers import pipeline

fill_mask = pipeline("fill-mask", model="farid678/dummy-model")
fill_mask("Le camembert est <mask> !")

Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

• Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

RoBERTa-based transformer encoder (CamemBERT), trained with the masked language modeling objective.

Compute Infrastructure

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Hardware

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Software

• transformers

Citation [optional]

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}
}

APA:

Martin, L., Muller, B., Suárez, P. J. O., Dupont, Y., Romary, L., de la Clergerie, É. V., Seddah, D., & Sagot, B. (2020). CamemBERT: a Tasty French Language Model. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.

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