|
--- |
|
license: apache-2.0 |
|
base_model: google-bert/bert-base-uncased |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: BERT_BIOMAT_NER3600_ST_DA |
|
results: [] |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# BERT_BIOMAT_NER3600_ST_DA |
|
|
|
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co./google-bert/bert-base-uncased) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.3385 |
|
- Precision: 0.9673 |
|
- Recall: 0.9631 |
|
- F1: 0.9652 |
|
- Accuracy: 0.9636 |
|
|
|
## 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: 2e-05 |
|
- train_batch_size: 32 |
|
- eval_batch_size: 32 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 10 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.2217 | 1.0 | 601 | 0.2356 | 0.9543 | 0.9508 | 0.9525 | 0.9472 | |
|
| 0.0692 | 2.0 | 1202 | 0.2555 | 0.9571 | 0.9529 | 0.9550 | 0.9525 | |
|
| 0.0406 | 3.0 | 1803 | 0.2416 | 0.9626 | 0.9584 | 0.9605 | 0.9584 | |
|
| 0.0257 | 4.0 | 2404 | 0.2984 | 0.9644 | 0.9594 | 0.9619 | 0.9598 | |
|
| 0.011 | 5.0 | 3005 | 0.2851 | 0.9663 | 0.9622 | 0.9643 | 0.9627 | |
|
| 0.0079 | 6.0 | 3606 | 0.3022 | 0.9665 | 0.9622 | 0.9644 | 0.9627 | |
|
| 0.0056 | 7.0 | 4207 | 0.3214 | 0.9668 | 0.9619 | 0.9644 | 0.9625 | |
|
| 0.0043 | 8.0 | 4808 | 0.3227 | 0.9673 | 0.9631 | 0.9652 | 0.9636 | |
|
| 0.0036 | 9.0 | 5409 | 0.3405 | 0.9672 | 0.9629 | 0.9650 | 0.9634 | |
|
| 0.0025 | 10.0 | 6010 | 0.3385 | 0.9673 | 0.9631 | 0.9652 | 0.9636 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.42.4 |
|
- Pytorch 2.4.0+cu121 |
|
- Datasets 2.21.0 |
|
- Tokenizers 0.19.1 |
|
|
