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--- |
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license: apache-2.0 |
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datasets: |
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- giga_fren |
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- opus100 |
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language: |
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- fr |
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- en |
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--- |
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# Model Card for fr_en-t5-small |
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<!-- Provide a quick summary of what the model is/does. --> |
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This model has been optimized for French and English language processing while minimizing overall size. To achieve this, I only retained relevant parameters and tokens specific to these two languages, ensuring that performance remains as good as the original mt5. |
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## Model Details |
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I used a method outlined in a [blog post](https://towardsdatascience.com/how-to-adapt-a-multilingual-t5-model-for-a-single-language-b9f94f3d9c90) by David Dale to downsize the multilingual T5 model for French and English use cases specifically. By utilizing the giga_fren dataset, I was able to successfully reduce the total number of tokens and decrease both the model and tokenizer sizes by 67% and 80% respectively. |
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### Model Description |
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- **Developed by:** Korventenn |
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- **Model type:** mt5 |
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- **Language(s) (NLP):** French and English |
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- **License:** Apache 2.0 |
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- **Generated from model:** mt5-small |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://colab.research.google.com/drive/1ag0u1WKdvuBeYTz1TrPAGucumiaYmqeW?usp=sharing |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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You can use the raw model for any sequence to sequence task that is focused on either french, english or both. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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``` |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("Korventenn/fr_en-t5-small") |
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model = AutoModelForSeq2SeqLM.from_pretrained("Korventenn/fr_en-t5-small") |
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``` |
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### Training Data |
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<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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[giga_fren](https://huggingface.co/datasets/giga_fren) |
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[opus100](https://huggingface.co/datasets/opus100) |
