Update README.md

README.md

@@ -51,12 +51,63 @@ widget:
 
 # Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for Named Entity Recognition.
 
+## 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)
+
+## Model description
+
 The **roberta-base-ca-v2-cased-ner** is a Named Entity Recognition (NER) model for the Catalan language fine-tuned from the [roberta-base-ca-v2](https://huggingface.co/projecte-aina/roberta-base-ca-v2) 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 roberta-base-ca-v2 model card for more details).
 
-## Datasets
+## Intended Uses and Limitations
+
+**roberta-base-ca-v2-cased-ner** model can be used to recognize Named Entities in the provided text. 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 in PyTorch:
+
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
+from pprint import pprint
+
+tokenizer = AutoTokenizer.from_pretrained("projecte-aina/roberta-base-ca-v2-cased-ner")
+model = AutoModelForTokenClassification.from_pretrained("projecte-aina/roberta-base-ca-v2-cased-ner")
+
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+example = "Em dic Lluïsa i visc a Santa Maria del Camí."
+
+ner_results = nlp(example)
+pprint(ner_results)
+```
+
+## Training
+
+### Training data
 We used the NER dataset in Catalan called [Ancora-ca-NER](https://huggingface.co/datasets/projecte-aina/ancora-ca-ner) for training and evaluation.
 
-## Evaluation and results
+### 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-v2-cased-ner_ on the Ancora-ca-ner test set against standard multilingual and monolingual baselines:
 
 | Model        | Ancora-ca-ner (F1)| 
@@ -68,7 +119,12 @@ We evaluated the _roberta-base-ca-v2-cased-ner_ on the Ancora-ca-ner test set ag
 
 For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/projecte-aina/club).
 
-## Citing 
+## Licensing Information
+
+[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,
@@ -94,3 +150,7 @@ If you use any of these resources (datasets or models) in your work, please cite
 
 ### Funding
 This work was funded by the [Catalan Government](https://politiquesdigitals.gencat.cat/en/inici/index.html) within the framework of the [AINA project.](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
+
+## Contributions
+
+[N/A]