readme: ---
  tags: 
  - autonlp
  language: en
  widget:
  - text: "I love AutoNLP 🤗"
  datasets:
  - nihaldsouza1/autonlp-data-yelp-rating-classification
  co2_eq_emissions: 15.62335109262394
  ---
  
  # Custom-trained user model
  
  - Problem type: Multi-class Classification
  - Model ID: 545015430
  - CO2 Emissions (in grams): 15.62335109262394
  
  ## Validation Metrics
  
  - Loss: 0.7870086431503296
  - Accuracy: 0.6631428571428571
  - Macro F1: 0.6613073053700258
  - Micro F1: 0.6631428571428571
  - Weighted F1: 0.661157273964887
  - Macro Precision: 0.6626911151999393
  - Micro Precision: 0.6631428571428571
  - Weighted Precision: 0.662191421927851
  - Macro Recall: 0.6629735627465572
  - Micro Recall: 0.6631428571428571
  - Weighted Recall: 0.6631428571428571
  
  
  ## 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/nihaldsouza1/autonlp-yelp-rating-classification-545015430
  ```
  
  Or Python API:
  
  ```
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  
  model = AutoModelForSequenceClassification.from_pretrained("nihaldsouza1/autonlp-yelp-rating-classification-545015430", use_auth_token=True)
  
  tokenizer = AutoTokenizer.from_pretrained("nihaldsouza1/autonlp-yelp-rating-classification-545015430", use_auth_token=True)
  
  inputs = tokenizer("I love AutoNLP", return_tensors="pt")
  
  outputs = model(**inputs)
  ```