readme: ---
  tags: autotrain
  language: unk
  widget:
  - text: "I love AutoTrain 🤗"
  datasets:
  - kakashi210/autotrain-data-tweet-sentiment-classifier
  co2_eq_emissions: 17.43982800509071
  ---
  
  # Model Trained Using AutoTrain
  
  - Problem type: Multi-class Classification
  - Model ID: 1055036381
  - CO2 Emissions (in grams): 17.43982800509071
  
  ## Validation Metrics
  
  - Loss: 0.6177256107330322
  - Accuracy: 0.7306006137658921
  - Macro F1: 0.719534854339415
  - Micro F1: 0.730600613765892
  - Weighted F1: 0.7302204676842725
  - Macro Precision: 0.714938066281146
  - Micro Precision: 0.7306006137658921
  - Weighted Precision: 0.7316651970219867
  - Macro Recall: 0.7258484087500343
  - Micro Recall: 0.7306006137658921
  - Weighted Recall: 0.7306006137658921
  
  
  ## 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/kakashi210/autotrain-tweet-sentiment-classifier-1055036381
  ```
  
  Or Python API:
  
  ```
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  
  model = AutoModelForSequenceClassification.from_pretrained("kakashi210/autotrain-tweet-sentiment-classifier-1055036381", use_auth_token=True)
  
  tokenizer = AutoTokenizer.from_pretrained("kakashi210/autotrain-tweet-sentiment-classifier-1055036381", use_auth_token=True)
  
  inputs = tokenizer("I love AutoTrain", return_tensors="pt")
  
  outputs = model(**inputs)
  ```