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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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Model size
109M params
Tensor type
I64
·
F32
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Evaluation results

  • Validation Accuracy on ade_corpus_v2Ade_corpus_v2_classification
    self-reported
    92.980
  • Validation F1 on ade_corpus_v2Ade_corpus_v2_classification
    self-reported
    82.730