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rodrigogelacio
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autonlp-department-classification-534915130

Text Classification
Transformers
PyTorch
Enawené-Nawé
bert
Trained with AutoTrain
Inference Endpoints
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autonlp-department-classification-534915130 / README.md
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metadata
tags: autonlp
language: unk
widget:
  - text: I love AutoNLP 🤗
datasets:
  - rodrigogelacio/autonlp-data-department-classification
co2_eq_emissions: 1.4862856774320061

Model Trained Using AutoNLP

  • Problem type: Multi-class Classification
  • Model ID: 534915130
  • CO2 Emissions (in grams): 1.4862856774320061

Validation Metrics

  • Loss: 0.37066277861595154
  • Accuracy: 0.9204545454545454
  • Macro F1: 0.9103715740678612
  • Micro F1: 0.9204545454545455
  • Weighted F1: 0.9196871607509906
  • Macro Precision: 0.9207759152612094
  • Micro Precision: 0.9204545454545454
  • Weighted Precision: 0.922177301864802
  • Macro Recall: 0.9055002187355129
  • Micro Recall: 0.9204545454545454
  • Weighted Recall: 0.9204545454545454

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/rodrigogelacio/autonlp-department-classification-534915130

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("rodrigogelacio/autonlp-department-classification-534915130", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("rodrigogelacio/autonlp-department-classification-534915130", use_auth_token=True)

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

outputs = model(**inputs)