

# Model Card for fr_en-t5-large

<!-- Provide a quick summary of what the model is/does. -->

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.

## Model Details
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 38% and 80% respectively.

### Model Description

- **Developed by:** Korventenn
- **Model type:** mt5
- **Language(s) (NLP):** French and English
- **License:** Apache 2.0
- **Generated from model:** mt5-large

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** https://colab.research.google.com/drive/1cDWtO5BqWMm_nxnM7lHmPEKMWMejHdBJ#scrollTo=s6ebzRxA1VGv

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
You can use the raw model for any sequence to sequence task that is focused on either french, english or both.


## How to Get Started with the Model

Use the code below to get started with the model.
```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Korventenn/fr_en-t5-large")

model = AutoModelForSeq2SeqLM.from_pretrained("Korventenn/fr_en-t5-large")
```

### Training Data

<!-- 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. -->

[giga_fren](https://huggingface.co/datasets/giga_fren)
{"language": ["fr", "en"], "license": "apache-2.0", "datasets": ["giga_fren"]}
transformers pytorch safetensors mt5 text2text-generation fr en dataset:giga_fren license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #mt5 #text2text-generation #fr #en #dataset-giga_fren #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 



# Model Card for fr_en-t5-large



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.

## Model Details
I used a method outlined in a blog post 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 38% and 80% respectively.

### Model Description

- Developed by: Korventenn
- Model type: mt5
- Language(s) (NLP): French and English
- License: Apache 2.0
- Generated from model: mt5-large

### Model Sources [optional]



- Repository: URL

## Uses


You can use the raw model for any sequence to sequence task that is focused on either french, english or both.


## How to Get Started with the Model

Use the code below to get started with the model.


### Training Data



giga_fren
# Model Card for fr_en-t5-large



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. ## Model Details
I used a method outlined in a blog post 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 38% and 80% respectively. ### Model Description

- Developed by: Korventenn
- Model type: mt5
- Language(s) (NLP): French and English
- License: Apache 2.0
- Generated from model: mt5-large ### Model Sources [optional]



- Repository: URL ## Uses


You can use the raw model for any sequence to sequence task that is focused on either french, english or both. ## How to Get Started with the Model

Use the code below to get started with the model. ### Training Data



giga_fren
TAGS
#transformers #pytorch #safetensors #mt5 #text2text-generation #fr #en #dataset-giga_fren #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # Model Card for fr_en-t5-large



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. ## Model Details
I used a method outlined in a blog post 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 38% and 80% respectively. ### Model Description

- Developed by: Korventenn
- Model type: mt5
- Language(s) (NLP): French and English
- License: Apache 2.0
- Generated from model: mt5-large ### Model Sources [optional]



- Repository: URL ## Uses


You can use the raw model for any sequence to sequence task that is focused on either french, english or both. ## How to Get Started with the Model

Use the code below to get started with the model. ### Training Data



giga_fren