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--- |
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license: mit |
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base_model: MoritzLaurer/bge-m3-zeroshot-v2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: nli-finetuning-laurer-immigration-classification |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# nli-finetuning-laurer-immigration-classification |
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This model is a fine-tuned version of [MoritzLaurer/bge-m3-zeroshot-v2.0](https://huggingface.co/MoritzLaurer/bge-m3-zeroshot-v2.0) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5578 |
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- Accuracy: 0.9032 |
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- F1 Macro: 0.8969 |
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- Accuracy Balanced: 0.8913 |
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- F1 Micro: 0.9032 |
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- Precision Macro: 0.9048 |
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- Recall Macro: 0.8913 |
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- Precision Micro: 0.9032 |
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- Recall Micro: 0.9032 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 80 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.25 |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Accuracy Balanced | F1 Micro | Precision Macro | Recall Macro | Precision Micro | Recall Micro | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:| |
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| No log | 1.0 | 151 | 0.3342 | 0.8763 | 0.8679 | 0.8619 | 0.8763 | 0.8769 | 0.8619 | 0.8763 | 0.8763 | |
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| No log | 2.0 | 302 | 0.4733 | 0.8710 | 0.8680 | 0.8793 | 0.8710 | 0.8644 | 0.8793 | 0.8710 | 0.8710 | |
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| No log | 3.0 | 453 | 0.5168 | 0.8978 | 0.8895 | 0.8796 | 0.8978 | 0.9073 | 0.8796 | 0.8978 | 0.8978 | |
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| 0.4084 | 4.0 | 604 | 0.5300 | 0.8871 | 0.8813 | 0.8804 | 0.8871 | 0.8823 | 0.8804 | 0.8871 | 0.8871 | |
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| 0.4084 | 5.0 | 755 | 0.5578 | 0.9032 | 0.8969 | 0.8913 | 0.9032 | 0.9048 | 0.8913 | 0.9032 | 0.9032 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.5.0+cu121 |
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- Datasets 2.14.7 |
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- Tokenizers 0.13.3 |
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