Update README.md

README.md

@@ -1,26 +1,53 @@
 ---
 language:
+
 - es
+
 license: apache-2.0
+
 tags:
+
 - "national library of spain"
+
 - "spanish"
+
 - "bne"
+
 - "capitel"
+
 - "pos"
+
 datasets:
+
 - "bne"
-- "capitel"  
+
+- "capitel"
+
 metrics:
+
 - "f1"
+
+inference:
+  parameters:
+    aggregation_strategy: "first"
+
+model-index:
+- name: roberta-large-bne-capiter-pos
+  results:
+  - task: 
+      type: token-classification
+    dataset:
+      type:  pos
+      name: CAPITEL-POS
+    metrics:
+      - name: F1
+        type: f1
+        value: 0.986
+
 widget:
 - text: "Festival de San Sebastián: Johnny Depp recibirá el premio Donostia en pleno rifirrafe judicial con Amber Heard" 
 - text: "El alcalde de Vigo, Abel Caballero, ha comenzado a colocar las luces de Navidad en agosto."
 - text: "Gracias a los datos de la BNE, se ha podido lograr este modelo del lenguaje."
-- text: "El Tribunal Superior de Justicia se pronunció ayer: \"Hay base legal dentro del marco jurídico actual\"."
-inference:
-  parameters:
-    aggregation_strategy: "first"
     
 ---
 
@@ -35,7 +62,13 @@ inference:
 - [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)
@@ -48,28 +81,56 @@ inference:
 </details>
 
 ## Model description
-RoBERTa-large-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) large 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/BSC-TeMU/roberta-large-bne
+The **roberta-large-bne-capitel-pos** is a Part-of-speech-tagging (POS) model for the Spanish language fine-tuned from the [roberta-large-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) large 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
+# Intended uses and limitations
+**roberta-large-bne-capitel-pos** model can be used to Part-of-speech-tagging (POS) a 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:
+
+```python
+from transformers import pipeline
+from pprint import pprint
+
+nlp = pipeline("token-classification", model="PlanTL-GOB-ES/roberta-large-bne-capitel-pos")
+example = "El alcalde de Vigo, Abel Caballero, ha comenzado a colocar las luces de Navidad en agosto."
+
+pos_results = nlp(example)
+pprint(pos_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 one from the [CAPITEL competition at IberLEF 2020](https://sites.google.com/view/capitel2020) (sub-task 2).
 
-## Evaluation 
-F1 Score: 0.9851 (average of 5 runs).
+### Training procedure
+The model was trained with a batch size of 16 and a learning rate of 3e-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.
 
-For evaluation details visit our [GitHub repository](https://github.com/PlanTL-GOB-ES/lm-spanish).
+## Evaluation results
+We evaluated the **roberta-large-bne-capitel-pos** on the CAPITEL-POS test set against standard multilingual and monolingual baselines:
 
 
+| Model        | CAPITEL-POS (F1) | 
+| ------------|:----|
+| roberta-large-bne-capitel-pos | **98.56** |
+| roberta-base-bne-capitel-pos | 98.46 |
+| BETO       | 98.36 |
+| mBERT       | 98.39 |
+| BERTIN | 98.47 |
+| ELECTRA | 98.16 |
+
+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
 
 ### Author
@@ -107,6 +168,8 @@ Intelligence (SEDIA) within the framework of the Plan-TL.},
 }
 
 ```
+
+
 ### Disclaimer
 
 The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.