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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: 1.0515 |
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- Precision Micro: 0.2570 |
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- Precision Weighted: 0.2809 |
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- Precision Samples: 0.2896 |
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- Recall Micro: 0.6815 |
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- Recall Weighted: 0.6815 |
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- Recall Samples: 0.7119 |
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- F1-score: 0.3907 |
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- Accuracy: 0.0095 |
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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 | 313 | 1.2909 | 0.1858 | 0.2078 | 0.1957 | 0.7185 | 0.7185 | 0.7222 | 0.2977 | 0.0 | |
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| 1.262 | 2.0 | 626 | 1.0875 | 0.2099 | 0.2605 | 0.2295 | 0.7852 | 0.7852 | 0.8071 | 0.3431 | 0.0 | |
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| 1.262 | 3.0 | 939 | 1.0171 | 0.2284 | 0.2612 | 0.2539 | 0.7630 | 0.7630 | 0.7746 | 0.3643 | 0.0095 | |
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| 1.0059 | 4.0 | 1252 | 1.0510 | 0.2519 | 0.2764 | 0.2914 | 0.7259 | 0.7259 | 0.7563 | 0.4013 | 0.0095 | |
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| 0.8421 | 5.0 | 1565 | 1.0515 | 0.2570 | 0.2809 | 0.2896 | 0.6815 | 0.6815 | 0.7119 | 0.3907 | 0.0095 | |
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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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