Update README.md

README.md

@@ -1,3 +1,57 @@
 ---
 license: apache-2.0
+datasets:
+- giga_fren
+- opus100
+language:
+- fr
+- en
 ---
+
+
+# Model Card for fr_en-t5-small
+
+<!-- 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/1ag0u1WKdvuBeYTz1TrPAGucumiaYmqeW?usp=sharing
+
+## 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-small")
+
+model = AutoModelForSeq2SeqLM.from_pretrained("Korventenn/fr_en-t5-small")
+```
+
+### 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)
+[opus100](https://huggingface.co/datasets/opus100)