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---
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 |