Update README.md

README.md

@@ -1,18 +1,42 @@
 ---
 language:
+
 - es
+
 license: apache-2.0
+
 tags:
+
 - "national library of spain"
+
 - "spanish"
+
 - "bne"
+
 - "qa"
+
 - "question answering"
+
 datasets:
+
 - "PlanTL-GOB-ES/SQAC"  
+
 metrics:
 - "f1"
-
+- "exact match"
+
+model-index:
+- name: roberta-base-bne-sqac
+  results:
+  - task: 
+      type: question-answering
+    dataset:
+      type: "PlanTL-GOB-ES/SQAC" 
+      name: SQAC
+    metrics:
+      - name: F1
+        type: f1
+        value: 0.7923
 ---
 
 # Spanish RoBERTa-base trained on BNE finetuned for Spanish Question Answering Corpus (SQAC) dataset.
@@ -26,7 +50,13 @@ metrics:
 - [How to use](#how-to-use)
 - [Limitations and bias](#limitations-and-bias)
 - [Training](#training)
+- [Training](#training)
+  - [Training data](#training-data)
+  - [Training procedure](#training-procedure)
+- [Evaluation](#evaluation)
 - [Evaluation](#evaluation)
+   - [Variable and metrics](#variable-and-metrics)
+   - [Evaluation results](#evaluation-results)
 - [Additional information](#additional-information)
   - [Author](#author)
   - [Contact information](#contact-information)
@@ -39,24 +69,49 @@ metrics:
 </details>
 
 ## Model description
-RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the  [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019.
-
-Original pre-trained model can be found here: https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne
+The **roberta-base-bne-sqac** is a Question Answering (QA) model for the Spanish language fine-tuned from the [roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text, processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019.
 
 ## Intended uses and limitations
 
+**roberta-base-bne-sqac** 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
 
+```python
+from transformers import pipeline
+nlp = pipeline("question-answering", model="PlanTL-GOB-ES/roberta-base-bne-sqac")
+text = "¿Dónde vivo?"
+context = "Me llamo Wolfgang y vivo en Berlin"
+  
+qa_results = nlp(text, context)
+print(qa_results)
+```
+
 ## Limitations and bias
 At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.
 
 ## Training
-The dataset used is the [SQAC corpus](https://huggingface.co/datasets/PlanTL-GOB-ES/SQAC).
 
-## Evaluation 
-F1 Score: 0.7923 (average of 5 runs).
+### Training data
+We used the QA dataset in Spanish called [SQAC corpus](https://huggingface.co/datasets/PlanTL-GOB-ES/SQAC) 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 results
+We evaluated the **roberta-base-bne-sqac** on the SQAC test set against standard multilingual and monolingual baselines:
+
+
+| Model        | SQAC (F1) | 
+| ------------|:----|
+| roberta-large-bne-sqac | **82.02** |
+| roberta-base-bne-sqac | 79.23|
+| BETO       | 79.23 |
+| mBERT       | 75.62 |
+| BERTIN | 76.78 |
+| ELECTRA | 73.83 |
 
-For evaluation details visit our [GitHub repository](https://github.com/PlanTL-GOB-ES/lm-spanish).
+For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/PlanTL-GOB-ES/lm-spanish).
 
 ## Additional information