Update README.md

README.md

@@ -1,15 +1,23 @@
 ---
+
 language:
+
 - es
+
 license: apache-2.0
+
 tags:
 - "national library of spain"
 - "spanish"
 - "bne"
+- "roberta-base-bne"
+
 datasets:
-- "bne"  
+- "bne"
+
 metrics:
 - "ppl"
+
 widget:
 - text: "Este año las campanadas de La Sexta las presentará <mask>." 
 - text: "David Broncano es un presentador de La <mask>."
@@ -20,10 +28,146 @@ widget:
 
 # RoBERTa base trained with data from National Library of Spain (BNE)
 
+## Table of Contents
+<details>
+<summary>Click to expand</summary>
+
+- [Overview](#overview)
+- [Model Description](#model-description)
+- [How to Use](#how-to-use)
+- [Intended Uses and Limitations](#intended-uses-and-limitations)
+- [Training](#training)
+  - [Training Data](#training-data)
+  - [Training Procedure](#training-procedure)
+- [Evaluation](#evaluation)
+   - [Evaluation Results](#evaluation-results)
+- [Additional Information](#additional-information)
+   - [Authors](#authors)
+   - [Citation Information](#citation-information)
+   - [Contact Information](#contact-information)
+   - [Funding](#funding)
+   - [Licensing Information](#licensing-information)
+   - [Copyright](#copyright)
+   - [Disclaimer](#disclaimer)
+
+</details>
+
+## Overview
+- **Architecture:** roberta-base
+- **Language:** Spanish
+- **Task:** fill-mask
+- **Data:** BNE
+
 ## 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.
 
-## Training corpora and preprocessing 
+## How to Use
+You can use this model directly with a pipeline for fill mask. Since the generation relies on some randomness, we set a seed for reproducibility:
+
+```python
+>>> from transformers import pipeline
+>>> from pprint import pprint
+>>> unmasker = pipeline('fill-mask', model='PlanTL-GOB-ES/roberta-base-bne')
+>>> pprint(unmasker("Gracias a los datos de la BNE se ha podido <mask> este modelo del lenguaje."))
+[{'score': 0.08422081917524338, 
+'token': 3832, 
+'token_str': ' desarrollar', 
+'sequence': 'Gracias a los datos de la BNE se ha podido desarrollar este modelo del lenguaje.'}, 
+{'score': 0.06348305940628052, 
+'token': 3078, 
+'token_str': ' crear', 
+'sequence': 'Gracias a los datos de la BNE se ha podido crear este modelo del lenguaje.'}, 
+{'score': 0.06148449331521988, 
+'token': 2171, 
+'token_str': ' realizar', 
+'sequence': 'Gracias a los datos de la BNE se ha podido realizar este modelo del lenguaje.'}, 
+{'score': 0.056218471378088, 
+'token': 10880, 
+'token_str': ' elaborar', 
+'sequence': 'Gracias a los datos de la BNE se ha podido elaborar este modelo del lenguaje.'}, 
+{'score': 0.05133328214287758, 
+'token': 31915, 
+'token_str': ' validar', 
+'sequence': 'Gracias a los datos de la BNE se ha podido validar este modelo del lenguaje.'}]
+
+```
+
+Here is how to use this model to get the features of a given text in PyTorch:
+
+```python
+>>> from transformers import RobertaTokenizer, RobertaModel
+>>> tokenizer = RobertaTokenizer.from_pretrained('PlanTL-GOB-ES/roberta-base-bne')
+>>> model = RobertaModel.from_pretrained('PlanTL-GOB-ES/roberta-base-bne')
+>>> text = "Gracias a los datos de la BNE se ha podido desarrollar este modelo del lenguaje."
+>>> encoded_input = tokenizer(text, return_tensors='pt')
+>>> output = model(**encoded_input)
+>>> print(output.last_hidden_state.shape)
+torch.Size([1, 19, 768])
+```
+
+## Intended Uses and Limitations
+
+You can use the raw model for fill mask or fine-tune it to a downstream task.
+
+The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of
+unfiltered content from the internet, which is far from neutral. Here's an example of how the model can have biased predictions:
+
+```python
+>>> from transformers import pipeline, set_seed
+>>> from pprint import pprint
+>>> unmasker = pipeline('fill-mask', model='PlanTL-GOB-ES/roberta-base-bne')
+>>> set_seed(42)
+>>> pprint(unmasker("Antonio está pensando en <mask>."))
+[{'score': 0.07950365543365479,
+  'sequence': 'Antonio está pensando en ti.',
+  'token': 486,
+  'token_str': ' ti'},
+ {'score': 0.03375273942947388,
+  'sequence': 'Antonio está pensando en irse.',
+  'token': 13134,
+  'token_str': ' irse'},
+ {'score': 0.031026942655444145,
+  'sequence': 'Antonio está pensando en casarse.',
+  'token': 24852,
+  'token_str': ' casarse'},
+ {'score': 0.030703715980052948,
+  'sequence': 'Antonio está pensando en todo.',
+  'token': 665,
+  'token_str': ' todo'},
+ {'score': 0.02838558703660965,
+  'sequence': 'Antonio está pensando en ello.',
+  'token': 1577,
+  'token_str': ' ello'}]
+
+>>> set_seed(42)
+>>> pprint(unmasker("Mohammed está pensando en <mask>."))
+[{'score': 0.05433618649840355,
+  'sequence': 'Mohammed está pensando en morir.',
+  'token': 9459,
+  'token_str': ' morir'},
+ {'score': 0.0400255024433136,
+  'sequence': 'Mohammed está pensando en irse.',
+  'token': 13134,
+  'token_str': ' irse'},
+ {'score': 0.03705748915672302,
+  'sequence': 'Mohammed está pensando en todo.',
+  'token': 665,
+  'token_str': ' todo'},
+ {'score': 0.03658654913306236,
+  'sequence': 'Mohammed está pensando en quedarse.',
+  'token': 9331,
+  'token_str': ' quedarse'},
+ {'score': 0.03329474478960037,
+  'sequence': 'Mohammed está pensando en ello.',
+  'token': 1577,
+  'token_str': ' ello'}]
+
+```
+
+## Training
+
+### Training Data 
+
 The [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) crawls all .es domains once a year. The training corpus consists of 59TB of WARC files from these crawls, carried out from 2009 to 2019.
 
 To obtain a high-quality training corpus, the corpus has been preprocessed with a pipeline of operations, including among the others, sentence splitting, language detection, filtering of bad-formed sentences and deduplication of repetitive contents. During the process document boundaries are kept. This resulted into 2TB of Spanish clean corpus. Further global deduplication among the corpus is applied, resulting into 570GB of text.
@@ -34,13 +178,38 @@ Some of the statistics of the corpus:
 |---------|---------------------|------------------|-----------|
 | BNE     |         201,080,084 |  135,733,450,668 |     570GB |
 
-## Tokenization and pre-training 
-The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original [RoBERTA](https://arxiv.org/abs/1907.11692) model with a vocabulary size of 50,262 tokens. The RoBERTa-base-bne pre-training consists of a masked language model training that follows the approach employed for the RoBERTa base. The training lasted a total of 48 hours with 16 computing nodes each one with 4 NVIDIA V100 GPUs of 16GB VRAM.
+### Training Procedure
+The configuration of the **RoBERTa-base-bne** model is as follows:
+ - RoBERTa-b: 12-layer, 768-hidden, 12-heads, 125M parameters.
+The pretraining objective used for this architecture is masked language modeling without next sentence prediction.
+The training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original [RoBERTA](https://arxiv.org/abs/1907.11692) model with a vocabulary size of 50,262 tokens. 
+The RoBERTa-base-bne pre-training consists of a masked language model training that follows the approach employed for the RoBERTa base. The training lasted a total of 48 hours with 16 computing nodes each one with 4 NVIDIA V100 GPUs of 16GB VRAM.
+
+## Evaluation
+
+### Evaluation Results
+When fine-tuned on downstream tasks, this model achieves the following results:
+| Dataset      | Metric   | [**RoBERTa-b**](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne)   |
+|--------------|----------|------------|
+| MLDoc        | F1       |     0.9664 |
+| CoNLL-NERC   | F1       |     0.8851 |
+| CAPITEL-NERC | F1       |     0.8960 |
+| PAWS-X       | F1       |     0.9020 |
+| UD-POS       | F1       |     0.9907 |
+| CAPITEL-POS  | F1       |     0.9846 |
+| SQAC         | F1       |     0.7923 |
+| STS          | Combined |     0.8533 |
+| XNLI         | Accuracy |     0.8016 |
+
+For more evaluation details visit our [GitHub repository](https://github.com/PlanTL-GOB-ES/lm-spanish) or [paper](http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6405). 
 
-## Evaluation and results
-For evaluation details visit our [GitHub repository](https://github.com/PlanTL-GOB-ES/lm-spanish).
+## Additional Information
 
-## Citing 
+### Authors 
+
+The Text Mining Unit from Barcelona Supercomputing Center.
+
+### Citation Information
 If you use this model, please cite our [paper](http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6405):
 ```
 @article{,
@@ -61,28 +230,37 @@ Intelligence (SEDIA) within the framework of the Plan-TL.},
 
 ```
 
-## Copyright
+### Contact Information
 
-Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)
+For further information, send an email to <plantl-gob-es@bsc.es>
 
-## Licensing information
+### Funding
 
-[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
+This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.
 
-## Funding
+### Licensing Information
 
-This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.
-## Disclaimer
+This work is licensed under a [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
+
+### Copyright
+
+Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)
+
+### Disclaimer
+
+<details>
+<summary>Click to expand</summary>
 
 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.
 
-When third  parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of artificial intelligence.
+When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
 
-In no event shall the owner of the models (SEDIA – State Secretariat for digitalization and artificial intelligence) nor the creator (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.
+In no event shall the owner of the models (SEDIA – State Secretariat for Digitalization and Artificial Intelligence) nor the creator (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.
 
 
 Los modelos publicados en este repositorio tienen una finalidad generalista y están a disposición de terceros. Estos modelos pueden tener sesgos y/u otro tipo de distorsiones indeseables.
 
 Cuando terceros desplieguen o proporcionen sistemas y/o servicios a otras partes usando alguno de estos modelos (o utilizando sistemas basados en estos modelos) o se conviertan en usuarios de los modelos, deben tener en cuenta que es su responsabilidad mitigar los riesgos derivados de su uso y, en todo caso, cumplir con la normativa aplicable, incluyendo la normativa en materia de uso de inteligencia artificial.
 
-En ningún caso el propietario de los modelos (SEDIA – Secretaría de Estado de Digitalización e Inteligencia Artificial) ni el creador (BSC – Barcelona Supercomputing Center) serán responsables de los resultados derivados del uso que hagan terceros de estos modelos.
+En ningún caso el propietario de los modelos (SEDIA – Secretaría de Estado de Digitalización e Inteligencia Artificial) ni el creador (BSC – Barcelona Supercomputing Center) serán responsables de los resultados derivados del uso que hagan terceros de estos modelos.
+</details>