metadata
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-DT
results: []
bert-finetuned-DT
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6697
- Precision: 0.2381
- Recall: 0.0321
- F1: 0.0565
- Accuracy: 0.8179
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 99 | 0.7505 | 0.0 | 0.0 | 0.0 | 0.8196 |
| No log | 2.0 | 198 | 0.7033 | 0.0 | 0.0 | 0.0 | 0.8196 |
| No log | 3.0 | 297 | 0.6697 | 0.2381 | 0.0321 | 0.0565 | 0.8179 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2