metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: conjunction-classification-finetuned
results: []
conjunction-classification-finetuned
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3628
- Precision: 0.9722
- Recall: 0.9630
- F1-score: 0.9659
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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1-score |
|---|---|---|---|---|---|---|
| 1.0373 | 1.0 | 59 | 1.0341 | 0.1154 | 0.3333 | 0.1714 |
| 1.0096 | 2.0 | 118 | 0.8995 | 0.4697 | 0.5556 | 0.4602 |
| 0.8291 | 3.0 | 177 | 0.7374 | 0.4833 | 0.6667 | 0.5402 |
| 0.6212 | 4.0 | 236 | 0.5642 | 0.8246 | 0.6970 | 0.6032 |
| 0.3968 | 5.0 | 295 | 0.3628 | 0.9722 | 0.9630 | 0.9659 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0