---
library_name: transformers
base_model: jay0911/fine-tuned-aemodel
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: ade_biobert_output
  results: []
datasets:
- ade-benchmark-corpus/ade_corpus_v2
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# ade_biobert_output

This model is a fine-tuned version of [jay0911/fine-tuned-aemodel](https://huggingface.co/jay0911/fine-tuned-aemodel) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3619
- Precision: 0.9353
- Recall: 0.9358
- F1: 0.9355
- Recall Positive: 0.8686
- Recall Negative: 0.9613

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Recall Positive | Recall Negative |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:---------------:|:---------------:|
| 0.1921        | 0.2126 | 500  | 0.2565          | 0.9347    | 0.9332 | 0.9337 | 0.9147          | 0.9412          |
| 0.1893        | 0.4252 | 1000 | 0.2461          | 0.9409    | 0.9392 | 0.9397 | 0.9289          | 0.9436          |
| 0.2207        | 0.6378 | 1500 | 0.2583          | 0.9421    | 0.9418 | 0.9419 | 0.9104          | 0.9551          |
| 0.1706        | 0.8503 | 2000 | 0.3926          | 0.9216    | 0.9205 | 0.9183 | 0.7866          | 0.9776          |
| 0.1219        | 1.0629 | 2500 | 0.3413          | 0.9373    | 0.9354 | 0.9359 | 0.9246          | 0.9400          |
| 0.1097        | 1.2755 | 3000 | 0.3073          | 0.9453    | 0.9456 | 0.9453 | 0.8919          | 0.9685          |
| 0.1645        | 1.4881 | 3500 | 0.2700          | 0.9433    | 0.9430 | 0.9431 | 0.9118          | 0.9563          |
| 0.2348        | 1.7007 | 4000 | 0.2449          | 0.9452    | 0.9456 | 0.9452 | 0.8876          | 0.9703          |
| 0.2718        | 1.9133 | 4500 | 0.2304          | 0.9425    | 0.9426 | 0.9425 | 0.8990          | 0.9612          |


### Framework versions

- Transformers 4.55.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4