|
--- |
|
library_name: transformers |
|
license: apache-2.0 |
|
base_model: google-bert/bert-large-uncased |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: CS221-bert-large-uncased-finetuned-semeval |
|
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. --> |
|
|
|
# CS221-bert-large-uncased-finetuned-semeval |
|
|
|
This model is a fine-tuned version of [google-bert/bert-large-uncased](https://huggingface.co/google-bert/bert-large-uncased) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.3493 |
|
- F1: 0.7668 |
|
- Roc Auc: 0.8210 |
|
- Accuracy: 0.4765 |
|
|
|
## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
|
- lr_scheduler_type: cosine |
|
- lr_scheduler_warmup_steps: 100 |
|
- num_epochs: 20 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| |
|
| 0.55 | 1.0 | 70 | 0.5378 | 0.4156 | 0.6228 | 0.1625 | |
|
| 0.3931 | 2.0 | 140 | 0.4018 | 0.6857 | 0.7636 | 0.3989 | |
|
| 0.2768 | 3.0 | 210 | 0.3776 | 0.7337 | 0.7972 | 0.4422 | |
|
| 0.2033 | 4.0 | 280 | 0.3493 | 0.7668 | 0.8210 | 0.4765 | |
|
| 0.1157 | 5.0 | 350 | 0.3954 | 0.7648 | 0.8254 | 0.4675 | |
|
| 0.0746 | 6.0 | 420 | 0.4089 | 0.7660 | 0.8235 | 0.4747 | |
|
| 0.0539 | 7.0 | 490 | 0.4444 | 0.7597 | 0.8170 | 0.4567 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.47.1 |
|
- Pytorch 2.5.1+cu121 |
|
- Datasets 3.2.0 |
|
- Tokenizers 0.21.0 |
|
|
