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autotrain-xlm-roberta-base-reviews-672119800

Text Classification
Transformers
PyTorch
Enawené-Nawé
xlm-roberta
Trained with AutoTrain
Carbon Emissions
Inference Endpoints
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autotrain-xlm-roberta-base-reviews-672119800 / README.md
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metadata
tags: autotrain
language: unk
widget:
  - text: I love AutoTrain 🤗
datasets:
  - YXHugging/autotrain-data-xlm-roberta-base-reviews
co2_eq_emissions: 2011.6528745969179

Model Trained Using AutoTrain

  • Problem type: Multi-class Classification
  • Model ID: 672119800
  • CO2 Emissions (in grams): 2011.6528745969179

Validation Metrics

  • Loss: 0.9570887088775635
  • Accuracy: 0.5830708333333333
  • Macro F1: 0.5789149828346194
  • Micro F1: 0.5830708333333333
  • Weighted F1: 0.5789149828346193
  • Macro Precision: 0.5808338093704437
  • Micro Precision: 0.5830708333333333
  • Weighted Precision: 0.5808338093704437
  • Macro Recall: 0.5830708333333334
  • Micro Recall: 0.5830708333333333
  • Weighted Recall: 0.5830708333333333

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/YXHugging/autotrain-xlm-roberta-base-reviews-672119800

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("YXHugging/autotrain-xlm-roberta-base-reviews-672119800", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("YXHugging/autotrain-xlm-roberta-base-reviews-672119800", use_auth_token=True)

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

outputs = model(**inputs)