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--- |
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license: apache-2.0 |
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base_model: sentence-transformers/all-mpnet-base-v2 |
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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: IKT_classifier_mitigation_best |
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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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# IKT_classifier_mitigation_best |
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This model is a fine-tuned version of [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6517 |
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- Precision Micro: 0.3667 |
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- Precision Weighted: 0.4273 |
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- Precision Samples: 0.4539 |
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- Recall Micro: 0.7543 |
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- Recall Weighted: 0.7543 |
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- Recall Samples: 0.7982 |
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- F1-score: 0.5422 |
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- Accuracy: 0.1654 |
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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: 3.6181464293180716e-05 |
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- train_batch_size: 3 |
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- eval_batch_size: 3 |
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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_steps: 300.0 |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision Micro | Precision Weighted | Precision Samples | Recall Micro | Recall Weighted | Recall Samples | F1-score | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:---------------:|:--------------:|:--------:|:--------:| |
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| No log | 1.0 | 398 | 1.0635 | 0.1718 | 0.2238 | 0.1763 | 0.7714 | 0.7714 | 0.7945 | 0.2794 | 0.0 | |
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| 1.2442 | 2.0 | 796 | 0.8827 | 0.2167 | 0.2522 | 0.2388 | 0.7543 | 0.7543 | 0.7863 | 0.3518 | 0.0 | |
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| 0.9539 | 3.0 | 1194 | 0.7579 | 0.2710 | 0.3279 | 0.2979 | 0.7543 | 0.7543 | 0.7932 | 0.4134 | 0.0150 | |
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| 0.8265 | 4.0 | 1592 | 0.6773 | 0.3377 | 0.3943 | 0.3937 | 0.7429 | 0.7429 | 0.7901 | 0.4961 | 0.0752 | |
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| 0.8265 | 5.0 | 1990 | 0.6517 | 0.3667 | 0.4273 | 0.4539 | 0.7543 | 0.7543 | 0.7982 | 0.5422 | 0.1654 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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