README.md

metadata

tags:
  - autotrain
  - text-classification
language:
  - es
widget:
  - text: I love AutoTrain 🤗
datasets:
  - Taraassss/autotrain-data-taras_es_sentiment_analysis
co2_eq_emissions:
  emissions: 0.24907555356641484

Model Trained Using AutoTrain

• Problem type: Multi-class Classification
• Model ID: 50174120292
• CO2 Emissions (in grams): 0.2491

Validation Metrics

• Loss: 0.816
• Accuracy: 0.647
• Macro F1: 0.637
• Micro F1: 0.647
• Weighted F1: 0.644
• Macro Precision: 0.643
• Micro Precision: 0.647
• Weighted Precision: 0.645
• Macro Recall: 0.635
• Micro Recall: 0.647
• Weighted Recall: 0.647

Usage

You can use cURL to access this model:

$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/Taraassss/autotrain-taras_es_sentiment_analysis-50174120292

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("Taraassss/autotrain-taras_es_sentiment_analysis-50174120292", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("Taraassss/autotrain-taras_es_sentiment_analysis-50174120292", use_auth_token=True)

inputs = tokenizer("I love AutoTrain", return_tensors="pt")

outputs = model(**inputs)