readme: ---
  tags: autonlp
  language: ar
  widget:
  - text: "I love AutoNLP 🤗"
  datasets:
  - adelgasmi/autonlp-data-kpmg_nlp
  co2_eq_emissions: 64.58945483765274
  ---
  
  # Model Trained Using AutoNLP
  
  - Problem type: Multi-class Classification
  - Model ID: 18833547
  - CO2 Emissions (in grams): 64.58945483765274
  
  ## Validation Metrics
  
  - Loss: 0.14247722923755646
  - Accuracy: 0.9586074193404036
  - Macro F1: 0.9468339778730883
  - Micro F1: 0.9586074193404036
  - Weighted F1: 0.9585551117678807
  - Macro Precision: 0.9445436604001405
  - Micro Precision: 0.9586074193404036
  - Weighted Precision: 0.9591405429662925
  - Macro Recall: 0.9499427161888565
  - Micro Recall: 0.9586074193404036
  - Weighted Recall: 0.9586074193404036
  
  
  ## 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/adelgasmi/autonlp-kpmg_nlp-18833547
  ```
  
  Or Python API:
  
  ```
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  
  model = AutoModelForSequenceClassification.from_pretrained("adelgasmi/autonlp-kpmg_nlp-18833547", use_auth_token=True)
  
  tokenizer = AutoTokenizer.from_pretrained("adelgasmi/autonlp-kpmg_nlp-18833547", use_auth_token=True)
  
  inputs = tokenizer("I love AutoNLP", return_tensors="pt")
  
  outputs = model(**inputs)
  ```