FlorQARAG

FLOR-6.3B Model optimized for QA

<strong><span style="color:darkred">⚠️NOTICE⚠️: This model has been deprecated and is no longer actively maintained or supported. To access the latest models with enhanced features, better performance, and ongoing support, please visit <a style="color:darkred" href="https://huggingface.co/BSC-LT">https://huggingface.co/BSC-LT</a></span></strong>

Table of Contents

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• Model description
• Intended uses and limitations
• How to use
• Limitations and bias
• Training
• Evaluation
• Additional information

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Model description

FlorQARAG is a 6.3B-parameter transformer-based causal language model for Catalan, Spanish, and English, trained on a customized QA dataset from various sources especifically to be used in RAG (Retrieval-Aumented Generation) Applications. The dataset used to fine tune the model is: PureInstructQA

Intended uses and limitations

The FlorQARAG model is ready-to-use for RAG applications optimized for Catalan language. It can perform text-generation Question Answering in the context of RAG applications.

How to use

import torch
from transformers import pipeline

pipe = pipeline("text-generation", model="projecte-aina/FlorQARAG")

instruction = "Quants habitants té Mataró?"

context = "Mataró és una ciutat de Catalunya, capital de la comarca del Maresme. Situada al litoral mediterrani, a uns 30 km al nord-est de Barcelona, ha estat tradicionalment un centre administratiu de rellevància territorial i un pol de dinamisme econòmic. Compta amb prop de 130.000 habitants, essent actualment la vuitena població del Principat i la tretzena dels Països Catalans. "

# We need to format the prompt and context using ### and \n

def givePrediction(instruction, context, max_new_tokens=50, repetition_penalty=1.2, top_k=50, top_p=0.95, do_sample=True, temperature=0.5)
    text = f"### Instruction\n{{instruction}}\n### Context\n{{context}}\n### Answer\n"
    response = pipe(text.format(instruction=instruction, context=context),temperature=temperature,repetition_penalty=repetition_penalty, max_new_tokens=max_new_tokens,top_k=top_k, top_p=top_p, do_sample=do_sample)[0]["generated_text"]
    answer = response.split("###")[-1][8:-1]
    return answer

answer = givePrediction(instruction, context)

print(answer)
'130 000'

Limitations and bias

At the time of submission, no measures have been taken to estimate the bias and toxicity embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.

Training

Instruction Data

The training corpus is composed of 82,539 QA instruction following examples. See Data Card at PureInstructQA.

Additional information

Author

The Language Technologies Unit from Barcelona Supercomputing Center.

Contact

For further information, please send an email to [email protected].

Copyright

Copyright(c) 2023 by Language Technologies Unit, Barcelona Supercomputing Center.

License

Apache License, Version 2.0

Funding

This work was funded by [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of Projecte AINA.

Disclaimer

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The model published in this repository is intended for a generalist purpose and is available to third parties under a permissive Apache License, Version 2.0.

Be aware that the model may have biases and/or any other undesirable distortions.

When third parties deploy or provide systems and/or services to other parties using this model (or any system based on it) or become users of the model, they should note that it is their responsibility to mitigate the risks arising from its use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.

In no event shall the owner and creator of the model (Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties.

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