README.md

metadata

tags:
  - autotrain
  - text-classification
language:
  - en
widget:
  - text: I love AutoTrain
datasets:
  - kafikani/autotrain-data-dynex-bert-10.2023
co2_eq_emissions:
  emissions: 2.484897786762931

Model Trained Using AutoTrain

• Problem type: Multi-class Classification
• Model ID: 98307147045
• CO2 Emissions (in grams): 2.4849

Validation Metrics

• Loss: 0.413
• Accuracy: 0.846
• Macro F1: 0.719
• Micro F1: 0.846
• Weighted F1: 0.839
• Macro Precision: 0.752
• Micro Precision: 0.846
• Weighted Precision: 0.838
• Macro Recall: 0.704
• Micro Recall: 0.846
• Weighted Recall: 0.846

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/kafikani/autotrain-dynex-bert-10.2023-98307147045

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("kafikani/autotrain-dynex-bert-10.2023-98307147045", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("kafikani/autotrain-dynex-bert-10.2023-98307147045", use_auth_token=True)

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

outputs = model(**inputs)