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--- |
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language: es |
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tags: |
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- sagemaker |
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- roberta-bne |
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- TextClassification |
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- SentimentAnalysis |
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license: apache-2.0 |
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datasets: |
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- IMDbreviews_es |
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metrics: |
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- accuracy |
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model-index: |
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- name: roberta_bne_sentiment_analysis_es |
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results: |
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- task: |
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name: Sentiment Analysis |
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type: sentiment-analysis |
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dataset: |
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name: "IMDb Reviews in Spanish" |
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type: IMDbreviews_es |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9106666666666666 |
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- name: F1 Score |
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type: f1 |
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value: 0.9090909090909091 |
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- name: Precision |
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type: precision |
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value: 0.9063852813852814 |
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- name: Recall |
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type: recall |
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value: 0.9118127381600436 |
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widget: |
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- text: "Se trata de una película interesante, con un solido argumento y un gran interpretación de su actor principal" |
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--- |
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# Model roberta_bne_sentiment_analysis_es |
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## **A finetuned model for Sentiment analysis in Spanish** |
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This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container, |
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The base model is **RoBERTa-base-bne** which is a RoBERTa base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB. |
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It was trained by The [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) |
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**RoBERTa BNE Citation** |
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Check out the paper for all the details: https://arxiv.org/abs/2107.07253 |
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``` |
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@article{gutierrezfandino2022, |
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author = {Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquin Silveira-Ocampo and Casimiro Pio Carrino and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Aitor Gonzalez-Agirre and Marta Villegas}, |
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title = {MarIA: Spanish Language Models}, |
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journal = {Procesamiento del Lenguaje Natural}, |
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volume = {68}, |
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number = {0}, |
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year = {2022}, |
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issn = {1989-7553}, |
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url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6405}, |
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pages = {39--60} |
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} |
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``` |
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## Dataset |
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The dataset is a collection of movie reviews in Spanish, about 50,000 reviews. The dataset is balanced and provides every review in english, in spanish and the label in both languages. |
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Sizes of datasets: |
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- Train dataset: 42,500 |
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- Validation dataset: 3,750 |
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- Test dataset: 3,750 |
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## Intended uses & limitations |
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This model is intented for Sentiment Analysis for spanish corpus and finetuned specially for movie reviews but it can be applied to other kind of reviews. |
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## Hyperparameters |
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{ |
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"epochs": "4", |
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"train_batch_size": "32", |
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"eval_batch_size": "8", |
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"fp16": "true", |
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"learning_rate": "3e-05", |
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"model_name": "\"PlanTL-GOB-ES/roberta-base-bne\"", |
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"sagemaker_container_log_level": "20", |
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"sagemaker_program": "\"train.py\"", |
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} |
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## Evaluation results |
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- Accuracy = 0.9106666666666666 |
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- F1 Score = 0.9090909090909091 |
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- Precision = 0.9063852813852814 |
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- Recall = 0.9118127381600436 |
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## Test results |
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## Model in action |
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### Usage for Sentiment Analysis |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("edumunozsala/roberta_bne_sentiment_analysis_es") |
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model = AutoModelForSequenceClassification.from_pretrained("edumunozsala/roberta_bne_sentiment_analysis_es") |
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text ="Se trata de una película interesante, con un solido argumento y un gran interpretación de su actor principal" |
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input_ids = torch.tensor(tokenizer.encode(text)).unsqueeze(0) |
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outputs = model(input_ids) |
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output = outputs.logits.argmax(1) |
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``` |
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Created by [Eduardo Muñoz/@edumunozsala](https://github.com/edumunozsala) |
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