End of training

README.md

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+---
+base_model: bert-base-chinese
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: bert-base-chinese-climate-related-prediction-v2
+  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. -->
+
+# bert-base-chinese-climate-related-prediction-v2
+
+This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5613
+- Accuracy: 0.96
+
+## 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: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| No log        | 1.0   | 175  | 0.4095          | 0.97     |
+| No log        | 2.0   | 350  | 0.4708          | 0.96     |
+| 0.0           | 3.0   | 525  | 0.5164          | 0.96     |
+| 0.0           | 4.0   | 700  | 0.5271          | 0.96     |
+| 0.0           | 5.0   | 875  | 0.5314          | 0.96     |
+| 0.0           | 6.0   | 1050 | 0.5414          | 0.96     |
+| 0.0           | 7.0   | 1225 | 0.5507          | 0.96     |
+| 0.0           | 8.0   | 1400 | 0.5575          | 0.96     |
+| 0.0           | 9.0   | 1575 | 0.5597          | 0.96     |
+| 0.0           | 10.0  | 1750 | 0.5613          | 0.96     |
+
+
+### Framework versions
+
+- Transformers 4.42.4
+- Pytorch 2.3.1+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

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