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---
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language: es
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tags:
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- sagemaker
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- ruperta
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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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model-index:
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name: RuPERTa_base_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: 0.881866
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- name: F1 Score,
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type: f1,
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value: 0.008272
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- name: Precision,
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type: precision,
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value: 0.858605
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- name: Recall,
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type: recall,
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value: 0.920062
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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 `RuPERTa_base_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 **RuPERTa-base (uncased)** which is a RoBERTa model trained on a uncased version of big Spanish corpus.
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It was trained by mrm8488, Manuel Romero.[Link to base model](https://huggingface.co/mrm8488/RuPERTa-base)
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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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## 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": "\"mrm8488/RuPERTa-base\"",
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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 = 0.8629333333333333
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F1 Score = 0.8648790746582545
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Precision = 0.8479381443298969
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Recall = 0.8825107296137339
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## Test results
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Accuracy = 0.8066666666666666
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F1 Score = 0.8057862309134743
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Precision = 0.7928307854507116
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Recall = 0.8191721132897604
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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/RuPERTa_base_sentiment_analysis_es")
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model = AutoModelForSequenceClassification.from_pretrained("edumunozsala/RuPERTa_base_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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