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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Evaluation results
- Validation Accuracy on ade_corpus_v2Ade_corpus_v2_classificationself-reported92.980
- Validation F1 on ade_corpus_v2Ade_corpus_v2_classificationself-reported82.730