metadata
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
{
"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
"""