readme: ---
  tags: autonlp
  language: en
  widget:
  - text: "I love AutoNLP 🤗"
  datasets:
  - sbrandeis/autonlp-data-emotion-classification-pre
  co2_eq_emissions: 23.4692320403666
  ---
  
  # Model Trained Using AutoNLP
  
  - Problem type: Multi-class Classification
  - Model ID: 3252433
  - CO2 Emissions (in grams): 23.4692320403666
  
  ## Validation Metrics
  
  - Loss: 0.15040820837020874
  - Accuracy: 0.9438026849828286
  - Macro F1: 0.9093924156122387
  - Micro F1: 0.9438026849828286
  - Weighted F1: 0.9423168992167734
  - Macro Precision: 0.9482796335288181
  - Micro Precision: 0.9438026849828286
  - Weighted Precision: 0.9466095426853992
  - Macro Recall: 0.8842649120281764
  - Micro Recall: 0.9438026849828286
  - Weighted Recall: 0.9438026849828286
  
  
  ## 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/sbrandeis/autonlp-emotion-classification-pre-3252433
  ```
  
  Or Python API:
  
  ```
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  
  model = AutoModelForSequenceClassification.from_pretrained("sbrandeis/autonlp-emotion-classification-pre-3252433", use_auth_token=True)
  
  tokenizer = AutoTokenizer.from_pretrained("sbrandeis/autonlp-emotion-classification-pre-3252433", use_auth_token=True)
  
  inputs = tokenizer("I love AutoNLP", return_tensors="pt")
  
  outputs = model(**inputs)
  ```
  