Update README.md

README.md

@@ -1,51 +1,72 @@
 ---
+pipeline_tag: text-classification
+
 language:
+
 - ca
-pipeline_tag: text-classification
+
 license: apache-2.0
+
 tags:
+
 - "catalan"
+
 - "semantic textual similarity"
+
 - "sts-ca"
+
 - "CaText"
+
 - "Catalan Textual Corpus"
+
 datasets:
-- "projecte-aina/sts-ca"  
+
+- "projecte-aina/sts-ca"
+
 metrics:
-- "pearson"
+
+- "combined_score"
+
 model-index:
+
 - name: roberta-base-ca-cased-sts
   results:
   - task: 
       type: text-classification
     dataset:
       type:   projecte-aina/sts-ca
-      name: sts-ca
+      name: STS-ca
     metrics:
-      - type: pearson
-        value: 0.7973
+      - name: Pearson
+        type: Pearson
+        value: 0.797
         
 ---
 
-# Catalan BERTa (RoBERTa-base) finetuned for Semantic Textual Similarity.
+# Catalan BERTa (roberta-base-ca) finetuned for Semantic Textual Similarity.
 
-The **roberta-base-ca-cased-sts** is a Semantic Textual Similarity (STS) 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 STS dataset in Catalan called [STS-ca](https://huggingface.co/datasets/projecte-aina/sts-ca) for training and evaluation.
+## Model description
 
-## Evaluation and results
-We evaluated the _roberta-base-ca-cased-sts_ on the STS-ca test set against standard multilingual and monolingual baselines:
+The **roberta-base-ca-cased-sts** is a Semantic Textual Similarity (STS) 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 (check the roberta-base-ca model card for more details).
 
-| Model       | STS-ca (Pearson)   | 
-|:------------|:----|
-| roberta-base-ca-cased-sts | **79.73** |
-| mBERT       | 76.34 | 
-| XLM-RoBERTa | 75.40 |
-| WikiBERT-ca | 77.18 | 
+## Intended Uses and Limitations
 
-
-For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/projecte-aina/club).
+**roberta-base-ca-cased-sts** model can be used to assess the similarity between two snippets of text. The model is limited by its training dataset and may not generalize well for all use cases.
 
 ## How to use
 To get the correct<sup>1</sup> model's prediction scores with values between 0.0 and 5.0, use the following code:
@@ -70,20 +91,50 @@ sentence_pairs = [("El llibre va caure per la finestra.", "El llibre va sortir v
 
 predictions = pipe(prepare(sentence_pairs), add_special_tokens=False)
 
-# convert back to scores to the original 1 and 5 interval
+# convert back to scores to the original 0 and 5 interval
 for prediction in predictions:
     prediction['score'] = logit(prediction['score'])
 print(predictions)
 ```
 Expected output:
 ```
-[{'label': 'SIMILARITY', 'score': 2.4280577200108384}, 
-{'label': 'SIMILARITY', 'score': 2.132843521240822}, 
-{'label': 'SIMILARITY', 'score': 1.615101695426227}]
+[{'label': 'SIMILARITY', 'score': 2.118301674983813}, 
+{'label': 'SIMILARITY', 'score': 2.1799755855125853}, 
+{'label': 'SIMILARITY', 'score': 0.9511617858568939}]
 ```
 
 <sup>1</sup> _**avoid using the widget** scores since they are normalized and do not reflect the original annotation values._
-## Citing 
+
+## Training
+
+### Training data
+We used the STS dataset in Catalan called [STS-ca](https://huggingface.co/datasets/projecte-aina/sts-ca) for training and 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 the average score between the Pearson and Spearman correlations.
+
+## Evaluation results
+We evaluated the _roberta-base-ca-cased-sts_ on the STS-ca test set against standard multilingual and monolingual baselines:
+
+| Model       | STS-ca (Pearson score)   | 
+| ------------|:-------------|
+| roberta-base-ca-cased-sts | 79.73 |
+| mBERT       | 74.26 |
+| XLM-RoBERTa | 61.61 |
+
+For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/projecte-aina/club).
+
+## 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,
@@ -106,3 +157,10 @@ If you use any of these resources (datasets or models) in your work, please cite
     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]