
## 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
```


"""
{"language": "en", "license": "apache-2.0", "tags": ["sagemaker", "bert-base-uncased", "text classification"], "datasets": ["adecorpusv2"], "widget": [{"text": "I got a rash from taking acetaminophen"}], "model-index": [{"name": "BERT-ade_corpus", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "ade_corpus_v2Ade_corpus_v2_classification", "type": "ade_corpus"}, "metrics": [{"type": "accuracy", "value": 92.98, "name": "Validation Accuracy"}, {"type": "f1", "value": 82.73, "name": "Validation F1"}]}]}]}
transformers pytorch safetensors bert text-classification sagemaker bert-base-uncased text classification en dataset:adecorpusv2 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #text-classification #sagemaker #bert-base-uncased #text classification #en #dataset-adecorpusv2 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

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
---------------


Validation Metrics
------------------



Usage
-----


You can use cURL to access this model:


"""



TAGS
#transformers #pytorch #safetensors #bert #text-classification #sagemaker #bert-base-uncased #text classification #en #dataset-adecorpusv2 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #safetensors #bert #text-classification #sagemaker #bert-base-uncased #text classification #en #dataset-adecorpusv2 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
