Model save

README.md

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+---
+license: apache-2.0
+base_model: tohoku-nlp/bert-base-japanese-v3
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: gigazine-labeling
+  results: []
+---
+
+<!-- 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. -->
+
+# gigazine-labeling
+
+This model is a fine-tuned version of [tohoku-nlp/bert-base-japanese-v3](https://huggingface.co/tohoku-nlp/bert-base-japanese-v3) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.3062
+- Accuracy: 0.623
+
+## 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: 5e-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_ratio: 0.1
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 2.4089        | 1.0   | 125  | 1.5892          | 0.551    |
+| 1.2661        | 2.0   | 250  | 1.3291          | 0.601    |
+| 0.6811        | 3.0   | 375  | 1.3062          | 0.623    |
+
+
+### Framework versions
+
+- Transformers 4.43.4
+- Pytorch 2.4.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1