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doctorlan
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autonlp-JD-bert-653619233

Text Classification
Transformers
PyTorch
Enawené-Nawé
bert
Trained with AutoTrain
Inference Endpoints
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autonlp-JD-bert-653619233 / README.md
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metadata
tags: autonlp
language: unk
widget:
  - text: I love AutoNLP 🤗
datasets:
  - doctorlan/autonlp-data-JD-bert
co2_eq_emissions: 5.919372931976555

Model Trained Using AutoNLP

  • Problem type: Binary Classification
  • Model ID: 653619233
  • CO2 Emissions (in grams): 5.919372931976555

Validation Metrics

  • Loss: 0.15083155035972595
  • Accuracy: 0.952650883627876
  • Precision: 0.9631399317406143
  • Recall: 0.9412941961307538
  • AUC: 0.9828776962419389
  • F1: 0.9520917678812415

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 AutoNLP"}' https://api-inference.huggingface.co/models/doctorlan/autonlp-JD-bert-653619233

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("doctorlan/autonlp-JD-bert-653619233", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("doctorlan/autonlp-JD-bert-653619233", use_auth_token=True)

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

outputs = model(**inputs)