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Fix Metric Typos (#1)
language: es
- sagemaker
- bertin
- TextClassification
- SentimentAnalysis
license: apache-2.0
- IMDbreviews_es
- accuracy
- name: bertin_base_sentiment_analysis_es
- task:
name: Sentiment Analysis
type: sentiment-analysis
name: "IMDb Reviews in Spanish"
type: IMDbreviews_es
- name: Accuracy
type: accuracy
value: 0.898933
- name: F1 Score
type: f1
value: 0.8989063
- name: Precision
type: precision
value: 0.8771473
- name: Recall
type: recall
value: 0.9217724
- text: "Se trata de una película interesante, con un solido argumento y un gran interpretación de su actor principal"
# Model bertin_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 **Bertin base** which is a RoBERTa-base model pre-trained on the Spanish portion of mC4 using Flax.
It was trained by the Bertin Project.[Link to base model](
Article: BERTIN: Efficient Pre-Training of a Spanish Language Model using Perplexity Sampling
- Author = Javier De la Rosa y Eduardo G. Ponferrada y Manu Romero y Paulo Villegas y Pablo González de Prado Salas y María Grandury,
- journal = Procesamiento del Lenguaje Natural,
- volume = 68, number = 0, year = 2022
- url =
## 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": "\"bertin-project/bertin-roberta-base-spanish\"",
"sagemaker_container_log_level": "20",
"sagemaker_program": "\"\"",
## Evaluation results
- Accuracy = 0.8989333333333334
- F1 Score = 0.8989063750333421
- Precision = 0.877147319104633
- Recall = 0.9217724288840262
## Test results
## Model in action
### Usage for Sentiment Analysis
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("edumunozsala/bertin_base_sentiment_analysis_es")
model = AutoModelForSequenceClassification.from_pretrained("edumunozsala/bertin_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](