Update README.md

README.md

@@ -30,15 +30,62 @@ widget:
 
 ---
 
-# Catalan BERTa (RoBERTa-base) finetuned for Question Answering.
+# Catalan BERTa (roberta-base-ca) finetuned for Question Answering.
 
-The **roberta-base-ca-cased-qa** is a Question Answering (QA) model for the Catalan language fine-tuned from the [BERTa](https://huggingface.co/PlanTL-GOB-ES/roberta-base-ca) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the BERTa model card for more details).
+## Table of Contents
+- [Model Description](#model-description)
+- [Intended Uses and Limitations](#intended-uses-and-limitations)
+- [How to Use](#how-to-use)
+- [Training](#training)
+  - [Training Data](#training-data)
+  - [Training Procedure](#training-procedure)
+- [Evaluation](#evaluation)
+   - [Variable and Metrics](#variable-and-metrics)
+   - [Evaluation Results](#evaluation-results)
+- [Licensing Information](#licensing-information)
+- [Citation Information](#citation-information)
+- [Funding](#funding)
+- [Contributions](#contributions)
 
-## Datasets
-We used the QA dataset in Catalan called [ViquiQuAD](https://huggingface.co/datasets/projecte-aina/viquiquad) for training and evaluation, and the [XQuAD-ca](https://huggingface.co/datasets/projecte-aina/xquad-ca) test set for evaluation.
+## Model description
 
-## Evaluation and results
-We evaluated the _roberta-base-ca-cased-qa_ on the ViquiQuAD and XQuAD-ca test sets against standard multilingual and monolingual baselines:
+The **roberta-base-ca-cased-qa** is a Question Answering (QA) model for the Catalan language fine-tuned from the roberta-base-ca model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers.
+
+## Intended Uses and Limitations
+
+**roberta-base-ca-cased-qa** model can be used for extractive question answering. The model is limited by its training dataset and may not generalize well for all use cases.
+
+## How to Use
+
+Here is how to use this model:
+
+```python
+from transformers import pipeline
+
+nlp = pipeline("question-answering", model="projecte-aina/roberta-base-ca-cased-qa")
+text = "Quan va començar el Super3?"
+context = "El Super3 o Club Super3 és un univers infantil català creat a partir d'un programa emès per Televisió de Catalunya des del 1991. Està format per un canal de televisió, la revista Súpers!, la Festa dels Súpers i un club que té un milió i mig de socis."
+  
+qa_results = nlp(text, context)
+print(qa_results)
+```
+
+## Training
+
+### Training data
+We used the QA dataset in Catalan called [CatalanQA](https://huggingface.co/datasets/projecte-aina/catalanqa) for training and evaluation, and the [XQuAD-ca](https://huggingface.co/datasets/projecte-aina/xquad-ca) test set for evaluation.
+
+### Training Procedure
+The model was trained with a batch size of 16 and a learning rate of 5e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding development set and then evaluated it on the test set.
+
+## Evaluation
+
+### Variable and Metrics
+
+This model was finetuned maximizing F1 score.
+
+### Evaluation results
+We evaluated the _roberta-base-ca-cased-qa_ on the CatalanQA and XQuAD-ca test sets against standard multilingual and monolingual baselines:
 
 
 | Model        | ViquiQuAD (F1/EM)  | XQuAD-ca (F1/EM) | 
@@ -50,10 +97,12 @@ We evaluated the _roberta-base-ca-cased-qa_ on the ViquiQuAD and XQuAD-ca test s
 
 For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/projecte-aina/club).
 
+## Licensing Information
 
-## Citing 
-If you use any of these resources (datasets or models) in your work, please cite our latest paper:
+[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
 
+## Citation Information 
+If you use any of these resources (datasets or models) in your work, please cite our latest paper:
 ```bibtex
 @inproceedings{armengol-estape-etal-2021-multilingual,
     title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
@@ -74,4 +123,12 @@ If you use any of these resources (datasets or models) in your work, please cite
     doi = "10.18653/v1/2021.findings-acl.437",
     pages = "4933--4946",
 }
-```
+```
+
+### Funding
+This work was funded by the [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](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
+
+
+## Contributions
+
+[N/A]