metadata
library_name: transformers
license: apache-2.0
base_model: tohoku-nlp/bert-base-japanese-v3
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: bert_japanese_complexity_finetune
results: []
bert_japanese_complexity_finetune
This model is a fine-tuned version of tohoku-nlp/bert-base-japanese-v3 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7799
- Accuracy: 0.6767
- Precision: 0.6818
- Recall: 0.7212
- F1: 0.6958
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: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 242 | 0.7699 | 0.6725 | 0.6666 | 0.6726 | 0.6679 |
| No log | 2.0 | 484 | 0.7706 | 0.6529 | 0.6590 | 0.7061 | 0.6691 |
| 0.7752 | 3.0 | 726 | 0.7799 | 0.6767 | 0.6818 | 0.7212 | 0.6958 |
Framework versions
- Transformers 4.56.2
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1