readme: ---
  language: en
  widget:
  - text: "I got a rash from taking acetaminophen"
  tags:
  - sagemaker
  - bert-base-uncased
  - text classification
  license: apache-2.0
  datasets:
  - adecorpusv2
  model-index:
  - name: BERT-ade_corpus
    results:
    - task: 
        name: Text Classification
        type: text-classification
      dataset:
        name: "ade_corpus_v2Ade_corpus_v2_classification" 
        type: ade_corpus
      metrics:
         - name: Validation Accuracy
           type: accuracy
           value: 92.98
         - name: Validation F1
           type: f1
           value: 82.73
    
  ---
  
  ## bert-base-uncased
  This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.
  
  - Problem type: Text Classification(adverse drug effects detection).
  
  ## Hyperparameters
  ```json
  {
      "do_eval": true,
      "do_train": true,
      "fp16": true,
      "load_best_model_at_end": true,
      "model_name": "bert-base-uncased",
      "num_train_epochs": 10,
      "per_device_eval_batch_size": 16,
      "per_device_train_batch_size": 16,
      "learning_rate":5e-5
  
  }
  ```
  ## Validation Metrics
  | key | value |
  | --- | ----- |
  | eval_accuracy  | 0.9298021697511167 |
  | eval_auc | 0.8902672664394546 |
  | eval_f1 | 0.827315541601256 |
  | eval_loss | 0.17835010588169098 |
  | eval_recall | 0.8234375 |
  | eval_precision | 0.831230283911672 |
  
  ## 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 got a rash from taking acetaminophen"}' https://api-inference.huggingface.co/models/Jorgeutd/bert-base-uncased-ade-Ade-corpus-v2
  ```
  
  
  """