
# Model beto_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 **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. 

**BETO Citation**

[Spanish Pre-Trained BERT Model and Evaluation Data](https://users.dcc.uchile.cl/~jperez/papers/pml4dc2020.pdf)

```
@inproceedings{CaneteCFP2020,
  title={Spanish Pre-Trained BERT Model and Evaluation Data},
  author={Cañete, José and Chaperon, Gabriel and Fuentes, Rodrigo and Ho, Jou-Hui and Kang, Hojin and Pérez, Jorge},
  booktitle={PML4DC at ICLR 2020},
  year={2020}
}
```

## 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

## Intended uses & limitations

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.

## Hyperparameters
    {
    "epochs": "4",
    "train_batch_size": "32",    
    "eval_batch_size": "8",
    "fp16": "true",
    "learning_rate": "3e-05",
    "model_name": "\"dccuchile/bert-base-spanish-wwm-uncased\"",
    "sagemaker_container_log_level": "20",
    "sagemaker_program": "\"train.py\"",
    }

## Evaluation results

- Accuracy = 0.9101333333333333

- F1 Score = 0.9088450094671354

- Precision = 0.9105691056910569

- Recall = 0.9071274298056156

## Test results

## Model in action

### Usage for Sentiment Analysis

```python
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("edumunozsala/beto_sentiment_analysis_es")
model = AutoModelForSequenceClassification.from_pretrained("edumunozsala/beto_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", "beto", "TextClassification", "SentimentAnalysis"], "datasets": ["IMDbreviews_es"], "metrics": ["accuracy"], "widget": [{"text": "Se trata de una pel\u00edcula interesante, con un solido argumento y un gran interpretaci\u00f3n de su actor principal"}], "model-index": [{"name": "beto_sentiment_analysis_es", "results": [{"task": {"type": "sentiment-analysis", "name": "Sentiment Analysis"}, "dataset": {"name": "IMDb Reviews in Spanish", "type": "IMDbreviews_es"}, "metrics": [{"type": "accuracy", "value": 0.9101333333333333, "name": "Accuracy"}, {"type": "f1", "value": 0.9088450094671354, "name": "F1 Score"}, {"type": "precision", "value": 0.9105691056910569, "name": "Precision"}, {"type": "recall", "value": 0.9071274298056156, "name": "Recall"}]}]}]}
transformers pytorch safetensors bert text-classification sagemaker beto TextClassification SentimentAnalysis es dataset:IMDbreviews_es license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #text-classification #sagemaker #beto #TextClassification #SentimentAnalysis #es #dataset-IMDbreviews_es #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 


# Model beto_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 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. 

BETO Citation

Spanish Pre-Trained BERT Model and Evaluation Data



## 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

## Intended uses & limitations

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.

## Hyperparameters
    {
    "epochs": "4",
    "train_batch_size": "32",    
    "eval_batch_size": "8",
    "fp16": "true",
    "learning_rate": "3e-05",
    "model_name": "\"dccuchile/bert-base-spanish-wwm-uncased\"",
    "sagemaker_container_log_level": "20",
    "sagemaker_program": "\"URL\"",
    }

## Evaluation results

- Accuracy = 0.9101333333333333

- F1 Score = 0.9088450094671354

- Precision = 0.9105691056910569

- Recall = 0.9071274298056156

## Test results

## Model in action

### Usage for Sentiment Analysis



Created by Eduardo Muñoz/@edumunozsala

# Model beto_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 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. 

BETO Citation

Spanish Pre-Trained BERT Model and Evaluation Data ## 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 ## Intended uses & limitations

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. ## Hyperparameters
    {
    "epochs": "4",
    "train_batch_size": "32",    
    "eval_batch_size": "8",
    "fp16": "true",
    "learning_rate": "3e-05",
    "model_name": "\"dccuchile/bert-base-spanish-wwm-uncased\"",
    "sagemaker_container_log_level": "20",
    "sagemaker_program": "\"URL\"",
    } ## Evaluation results

- Accuracy = 0.9101333333333333

- F1 Score = 0.9088450094671354

- Precision = 0.9105691056910569

- Recall = 0.9071274298056156 ## Test results ## Model in action ### Usage for Sentiment Analysis



Created by Eduardo Muñoz/@edumunozsala
TAGS
#transformers #pytorch #safetensors #bert #text-classification #sagemaker #beto #TextClassification #SentimentAnalysis #es #dataset-IMDbreviews_es #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 # Model beto_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 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. 

BETO Citation

Spanish Pre-Trained BERT Model and Evaluation Data ## 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 ## Intended uses & limitations

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. ## Hyperparameters
    {
    "epochs": "4",
    "train_batch_size": "32",    
    "eval_batch_size": "8",
    "fp16": "true",
    "learning_rate": "3e-05",
    "model_name": "\"dccuchile/bert-base-spanish-wwm-uncased\"",
    "sagemaker_container_log_level": "20",
    "sagemaker_program": "\"URL\"",
    } ## Evaluation results

- Accuracy = 0.9101333333333333

- F1 Score = 0.9088450094671354

- Precision = 0.9105691056910569

- Recall = 0.9071274298056156 ## Test results ## Model in action ### Usage for Sentiment Analysis



Created by Eduardo Muñoz/@edumunozsala
passage: TAGS
#transformers #pytorch #safetensors #bert #text-classification #sagemaker #beto #TextClassification #SentimentAnalysis #es #dataset-IMDbreviews_es #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
# Model beto_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 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. 

BETO Citation

Spanish Pre-Trained BERT Model and Evaluation Data## 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## Intended uses & limitations

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.## Hyperparameters
    {
    "epochs": "4",
    "train_batch_size": "32",    
    "eval_batch_size": "8",
    "fp16": "true",
    "learning_rate": "3e-05",
    "model_name": "\"dccuchile/bert-base-spanish-wwm-uncased\"",
    "sagemaker_container_log_level": "20",
    "sagemaker_program": "\"URL\"",
    }## Evaluation results

- Accuracy = 0.9101333333333333

- F1 Score = 0.9088450094671354

- Precision = 0.9105691056910569

- Recall = 0.9071274298056156## Test results## Model in action