README.md

metadata

tags: autotrain
language: en
widget:
  - text: I love AutoTrain 🤗
datasets:
  - lewtun/autotrain-data-my-eval-project-615
co2_eq_emissions: 172.04481351504182
model-index:
  - name: bhadresh-savani/distilbert-base-uncased-emotion
    results:
      - task:
          name: Multi-class Classification
          type: text-classification
        dataset:
          type: emotion
          name: Emotion
          config: default
          split: test
        metrics:
          - name: Loss
            type: loss
            value: 0.17404702305793762
          - name: Accuracy
            type: accuracy
            value: 0.927
          - name: Macro F1
            type: macro_f1
            value: 0.8825061528287809
          - name: Recall
            type: micro_f1
            value: 0.927
          - name: Weighted F1
            type: weighted_f1
            value: 0.926876082854655
          - name: Macro Precision
            type: macro_precision
            value: 0.8880230732280744
          - name: Micro Precision
            type: micro_precision
            value: 0.927
          - name: Weighted Precision
            type: weighted_precision
            value: 0.9272902840835793
          - name: Macro Recall
            type: macro_recall
            value: 0.8790126653780703
          - name: Micro Recall
            type: micro_recall
            value: 0.927
          - name: Weighted Recall
            type: weighted_recall
            value: 0.927

Model Trained Using AutoTrain

• Problem type: Binary Classification
• Model ID: 5694363
• CO2 Emissions (in grams): 172.04481351504182

Validation Metrics

• Loss: 0.2228243350982666
• Accuracy: 0.9298
• Precision: 0.9434585224927775
• Recall: 0.9144
• AUC: 0.9566112000000001
• F1: 0.9287020109689214

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/lewtun/autotrain-my-eval-project-615-5694363

Or Python API:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("lewtun/autotrain-my-eval-project-615-5694363", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("lewtun/autotrain-my-eval-project-615-5694363", use_auth_token=True)

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

outputs = model(**inputs)