Downloads · 30 days
13
15% of all-time downloads
finbert-japan-advanced-v2-fix
dgawghbuidw/finbert-japan-advanced-v2-fix
finbert-japan-advanced-v2-fix is a machine learning model from dgawghbuidw. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
All-time downloads
86
Public
Parameters
111M
4 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors445 MB · 100%
At a glance
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Dec 30, 2025
- Updated
- Dec 30, 2025
- SHA
- 7145f357
Base models
Model card
From the Hugging Face model README
<!-- 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. -->
finbert-japan-advanced-v2-fix
This model is a fine-tuned version of ProsusAI/finbert on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2322
- Accuracy: 0.86
- F1: 0.8602
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 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 | F1 |
|---|---|---|---|---|---|
| 0.1136 | 1.0 | 733 | 0.2425 | 0.792 | 0.7929 |
| 0.062 | 2.0 | 1466 | 0.2756 | 0.818 | 0.8189 |
| 0.0256 | 3.0 | 2199 | 0.3002 | 0.834 | 0.8343 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
- Library
- transformers
- Type
- bert
- Created
- Dec 30, 2025
- Updated
- Dec 30, 2025