readme: ---
  tags:
  - autonlp
  - question-answering
  language: unk
  widget:
  - text: "Who loves AutoNLP?"
    context: "Everyone loves AutoNLP"
  datasets:
  - teacookies/autonlp-data-more_fine_tune_24465520
  co2_eq_emissions: 124.66009281731397
  ---
  
  # Model Trained Using AutoNLP
  
  - Problem type: Extractive Question Answering
  - Model ID: 26265899
  - CO2 Emissions (in grams): 124.66009281731397
  
  ## Validation Metrics
  
  - Loss: 0.7011443972587585
  
  ## Usage
  
  You can use cURL to access this model:
  
  ```
  $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"question": "Who loves AutoNLP?", "context": "Everyone loves AutoNLP"}' https://api-inference.huggingface.co/models/teacookies/autonlp-more_fine_tune_24465520-26265899
  ```
  
  Or Python API:
  
  ```
  import torch
  
  from transformers import AutoModelForQuestionAnswering, AutoTokenizer
  
  model = AutoModelForQuestionAnswering.from_pretrained("teacookies/autonlp-more_fine_tune_24465520-26265899", use_auth_token=True)
  
  tokenizer = AutoTokenizer.from_pretrained("teacookies/autonlp-more_fine_tune_24465520-26265899", use_auth_token=True)
  
  from transformers import BertTokenizer, BertForQuestionAnswering
  
  question, text = "Who loves AutoNLP?", "Everyone loves AutoNLP"
  
  inputs = tokenizer(question, text, return_tensors='pt')
  
  start_positions = torch.tensor([1])
  
  end_positions = torch.tensor([3])
  
  outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
  
  loss = outputs.loss
  
  start_scores = outputs.start_logits
  
  end_scores = outputs.end_logits
  ```