Jorgeutd/bert-base-uncased-ade-Ade-corpus-v2

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

{
    "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

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