| 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 | |
| ``` | |
| """ |