
## 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](https://huggingface.co/mrm8488/RuPERTa-base) 

## 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

```python
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](https://github.com/edumunozsala)

{"language": "es", "license": "apache-2.0", "tags": ["sagemaker", "ruperta", "TextClassification", "SentimentAnalysis"], "datasets": ["IMDbreviews_es"], "name": "RuPERTa_base_sentiment_analysis_es", "results": [{"task": {"name": "Sentiment Analysis", "type": "sentiment-analysis"}}, {"dataset": {"name": "IMDb Reviews in Spanish", "type": "IMDbreviews_es"}}, {"metrics": [{"name": "Accuracy,", "type": "accuracy,", "value": 0.881866}, {"name": "F1 Score,", "type": "f1,", "value": 0.008272}, {"name": "Precision,", "type": "precision,", "value": 0.858605}, {"name": "Recall,", "type": "recall,", "value": 0.920062}]}], "widget": [{"text": "Se trata de una pel\u00edcula interesante, con un solido argumento y un gran interpretaci\u00f3n de su actor principal"}]}
transformers pytorch roberta text-classification sagemaker ruperta TextClassification SentimentAnalysis es dataset:IMDbreviews_es license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #sagemaker #ruperta #TextClassification #SentimentAnalysis #es #dataset-IMDbreviews_es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


## 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": "\"URL\"",
    }

## 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



Created by Eduardo Muñoz/@edumunozsala

## 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": "\"URL\"",
    } ## 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



Created by Eduardo Muñoz/@edumunozsala
TAGS
#transformers #pytorch #roberta #text-classification #sagemaker #ruperta #TextClassification #SentimentAnalysis #es #dataset-IMDbreviews_es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ## 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": "\"URL\"",
    } ## 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



Created by Eduardo Muñoz/@edumunozsala
passage: TAGS
#transformers #pytorch #roberta #text-classification #sagemaker #ruperta #TextClassification #SentimentAnalysis #es #dataset-IMDbreviews_es #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
## 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": "\"URL\"",
    }## 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



Created by Eduardo Muñoz/@edumunozsala