metadata

tags: autotrain
language: en
widget:
  - text: I love AutoTrain 🤗
datasets:
  - lewtun/autotrain-data-acronym-identification
  - acronym_identification
co2_eq_emissions: 10.435358044493652
model-index:
  - name: autotrain-demo
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: acronym_identification
          type: acronym_identification
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9708090976211485

Model Trained Using AutoTrain

Problem type: Entity Extraction
Model ID: 7324788
CO2 Emissions (in grams): 10.435358044493652

Validation Metrics

Loss: 0.08991389721632004
Accuracy: 0.9708090976211485
Precision: 0.8998421675654347
Recall: 0.9309429854401959
F1: 0.9151284109149278

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-acronym-identification-7324788

Or Python API:

from transformers import AutoModelForTokenClassification, AutoTokenizer

model = AutoModelForTokenClassification.from_pretrained("lewtun/autotrain-acronym-identification-7324788", use_auth_token=True)

tokenizer = AutoTokenizer.from_pretrained("lewtun/autotrain-acronym-identification-7324788", use_auth_token=True)

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

outputs = model(**inputs)