Update README.md

README.md

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 This model is a fine-tuned version of [mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) for a **novel extractive task**
 which consists of **identifying the explanation of the correct answer** written by medical doctors. The model
-has been fine-tuned using the multilingual [https://huggingface.co/datasets/HiTZ/casimedicos-squad](https://huggingface.co/datasets/HiTZ/casimedicos-squad) dataset.
+has been fine-tuned using the multilingual [https://huggingface.co/datasets/HiTZ/casimedicos-squad](https://huggingface.co/datasets/HiTZ/casimedicos-squad) dataset,
+which includes English, French, Italian and Spanish.
 
 
 ## Performance
 
-F1 partial match scores (as defined in [SQuAD extractive QA task](https://huggingface.co/datasets/rajpurkar/squad_v2) are reported in the following
-table:
+The model scores **74.64 F1 partial match** (as defined in [SQuAD extractive QA task](https://huggingface.co/datasets/rajpurkar/squad_v2) averaged across the 4 languages.
 
-<img src="https://raw.githubusercontent.com/hitz-zentroa/multilingual-abstrct/main/resources/multilingual-abstrct-results.png" style="width: 75%;">
+<!--<img src="https://raw.githubusercontent.com/hitz-zentroa/multilingual-abstrct/main/resources/multilingual-abstrct-results.png" style="width: 75%;"> -->
 
-### Training hyperparameters
+### Fine-tuning hyperparameters
 
 The following hyperparameters were used during training:
 - learning_rate: 5e-05
-- train_batch_size: 16
+- train_batch_size: 48
 - eval_batch_size: 8
-- seed: 42
+- seed: random
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 3.0
+- num_epochs: 20.0
 
 ### Framework versions
 
-- Transformers 4.40.0.dev0
+- Transformers 4.30.0.dev0
 - Pytorch 2.1.2+cu121
 - Datasets 2.16.1
 - Tokenizers 0.15.2
 
-**Contact**: [Anar Yeginbergen](https://ixa.ehu.eus/node/13807?language=en) and [Rodrigo Agerri](https://ragerri.github.io/)
+**Contact**: [Iakes Goenaga](http://www.hitz.eus/es/node/65) and [Rodrigo Agerri](https://ragerri.github.io/)
 HiTZ Center - Ixa, University of the Basque Country UPV/EHU