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--- |
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language: es |
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tags: |
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- sagemaker |
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- beto |
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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: beto_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: 0.9101333333333333 |
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- name: F1 Score |
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type: f1 |
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value: 0.9088450094671354 |
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- name: Precision |
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type: precision |
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value: 0.9105691056910569 |
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- name: Recall |
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type: recall |
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value: 0.9071274298056156 |
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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 beto_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 **BETO** which is a BERT-base model pre-trained on a spanish corpus. BETO is of size similar to a BERT-Base and was trained with the Whole Word Masking technique. |
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**BETO Citation** |
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[Spanish Pre-Trained BERT Model and Evaluation Data](https://users.dcc.uchile.cl/~jperez/papers/pml4dc2020.pdf) |
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``` |
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@inproceedings{CaneteCFP2020, |
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title={Spanish Pre-Trained BERT Model and Evaluation Data}, |
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author={Cañete, José and Chaperon, Gabriel and Fuentes, Rodrigo and Ho, Jou-Hui and Kang, Hojin and Pérez, Jorge}, |
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booktitle={PML4DC at ICLR 2020}, |
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year={2020} |
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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": "\"dccuchile/bert-base-spanish-wwm-uncased\"", |
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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 = 0.9101333333333333 |
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- F1 Score = 0.9088450094671354 |
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- Precision = 0.9105691056910569 |
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- Recall = 0.9071274298056156 |
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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/beto_sentiment_analysis_es") |
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model = AutoModelForSequenceClassification.from_pretrained("edumunozsala/beto_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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