readme: ---
  tags: autotrain
  language: en
  widget:
  - text: "I love AutoTrain 🤗"
  datasets:
  - ktangri/autotrain-data-financial-sentiment
  co2_eq_emissions: 0.007501354635994803
  ---
  
  # Model Trained Using AutoTrain
  
  - Problem type: Multi-class Classification
  - Model ID: 765323474
  - CO2 Emissions (in grams): 0.007501354635994803
  
  ## Validation Metrics
  
  - Loss: 0.0447433702647686
  - Accuracy: 0.9823788546255506
  - Macro F1: 0.974405452470854
  - Micro F1: 0.9823788546255506
  - Weighted F1: 0.9823043153179869
  - Macro Precision: 0.978208375548801
  - Micro Precision: 0.9823788546255506
  - Weighted Precision: 0.9823204968555985
  - Macro Recall: 0.9707159078140736
  - Micro Recall: 0.9823788546255506
  - Weighted Recall: 0.9823788546255506
  
  
  ## 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/ktangri/autotrain-financial-sentiment-765323474
  ```
  
  Or Python API:
  
  ```
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  
  model = AutoModelForSequenceClassification.from_pretrained("ktangri/autotrain-financial-sentiment-765323474", use_auth_token=True)
  
  tokenizer = AutoTokenizer.from_pretrained("ktangri/autotrain-financial-sentiment-765323474", use_auth_token=True)
  
  inputs = tokenizer("I love AutoTrain", return_tensors="pt")
  
  outputs = model(**inputs)
  ```