Model RuPERTa_base_sentiment_analysis_es
A finetuned model for Sentiment analysis in Spanish
This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container, The base model is RuPERTa-base (uncased) which is a RoBERTa model trained on a uncased version of big Spanish corpus. It was trained by mrm8488, Manuel Romero.Link to base model
Dataset
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.
Sizes of datasets:
- Train dataset: 42,500
- Validation dataset: 3,750
- Test dataset: 3,750
Hyperparameters
{
"epochs": "4",
"train_batch_size": "32",
"eval_batch_size": "8",
"fp16": "true",
"learning_rate": "3e-05",
"model_name": "\"mrm8488/RuPERTa-base\"",
"sagemaker_container_log_level": "20",
"sagemaker_program": "\"train.py\"",
}
Evaluation results
Accuracy = 0.8629333333333333 F1 Score = 0.8648790746582545 Precision = 0.8479381443298969 Recall = 0.8825107296137339
Test results
Accuracy = 0.8066666666666666 F1 Score = 0.8057862309134743 Precision = 0.7928307854507116 Recall = 0.8191721132897604
Model in action
Usage for Sentiment Analysis
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("edumunozsala/RuPERTa_base_sentiment_analysis_es")
model = AutoModelForSequenceClassification.from_pretrained("edumunozsala/RuPERTa_base_sentiment_analysis_es")
text ="Se trata de una película interesante, con un solido argumento y un gran interpretación de su actor principal"
input_ids = torch.tensor(tokenizer.encode(text)).unsqueeze(0)
outputs = model(input_ids)
output = outputs.logits.argmax(1)
Created by Eduardo Muñoz/@edumunozsala
