readme: ---
  tags: autotrain
  language: en
  widget:
  - text: "I love AutoTrain 🤗"
  datasets:
  - ndavid/autotrain-data-trec-fine-bert
  co2_eq_emissions: 0.02238820299105448
  ---
  
  # Model Trained Using AutoTrain
  
  - Problem type: Multi-class Classification
  - Model ID: 739422530
  - CO2 Emissions (in grams): 0.02238820299105448
  
  ## Validation Metrics
  
  - Loss: 0.36623290181159973
  - Accuracy: 0.9321753515301903
  - Macro F1: 0.9066706944656866
  - Micro F1: 0.9321753515301903
  - Weighted F1: 0.9314858667247282
  - Macro Precision: 0.9489233194839841
  - Micro Precision: 0.9321753515301903
  - Weighted Precision: 0.9347346558570125
  - Macro Recall: 0.8842587178845419
  - Micro Recall: 0.9321753515301903
  - Weighted Recall: 0.9321753515301903
  
  
  ## 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/ndavid/autotrain-trec-fine-bert-739422530
  ```
  
  Or Python API:
  
  ```
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  
  model = AutoModelForSequenceClassification.from_pretrained("ndavid/autotrain-trec-fine-bert-739422530", use_auth_token=True)
  
  tokenizer = AutoTokenizer.from_pretrained("ndavid/autotrain-trec-fine-bert-739422530", use_auth_token=True)
  
  inputs = tokenizer("I love AutoTrain", return_tensors="pt")
  
  outputs = model(**inputs)
  ```