metadata
license: apache-2.0
base_model: bert-large-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-large
results: []
bert-large
This model is a fine-tuned version of bert-large-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2253
- Precision: 0.6260
- Recall: 0.6749
- F1: 0.6495
- Accuracy: 0.9376
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: 4
- 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 |
---|---|---|---|---|---|---|---|
0.4658 | 1.0 | 746 | 0.2568 | 0.5354 | 0.5597 | 0.5473 | 0.9154 |
0.245 | 2.0 | 1492 | 0.2295 | 0.6059 | 0.6708 | 0.6367 | 0.9297 |
0.0948 | 3.0 | 2238 | 0.2253 | 0.6260 | 0.6749 | 0.6495 | 0.9376 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1