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---
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library_name: peft
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license: apache-2.0
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base_model: google-bert/bert-base-cased
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tags:
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- generated_from_trainer
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model-index:
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- name: grandiose-horse-172
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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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# grandiose-horse-172 |
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This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co./google-bert/bert-base-cased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6509 |
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- Hamming Loss: 0.3414 |
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- Zero One Loss: 1.0 |
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- Jaccard Score: 0.8678 |
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- Hamming Loss Optimised: 0.1121 |
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- Hamming Loss Threshold: 0.7504 |
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- Zero One Loss Optimised: 0.8812 |
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- Zero One Loss Threshold: 0.6730 |
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- Jaccard Score Optimised: 0.8449 |
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- Jaccard Score Threshold: 0.6539 |
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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: 1.510606094120106e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 2024 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Hamming Loss | Zero One Loss | Jaccard Score | Hamming Loss Optimised | Hamming Loss Threshold | Zero One Loss Optimised | Zero One Loss Threshold | Jaccard Score Optimised | Jaccard Score Threshold | |
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|:-------------:|:-----:|:----:|:---------------:|:------------:|:-------------:|:-------------:|:----------------------:|:----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:-----------------------:| |
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| No log | 1.0 | 100 | 0.7202 | 0.4325 | 1.0 | 0.8586 | 0.1123 | 0.7924 | 0.8712 | 0.7112 | 0.8203 | 0.5766 | |
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| No log | 2.0 | 200 | 0.6922 | 0.3761 | 1.0 | 0.8520 | 0.1123 | 0.7829 | 0.8812 | 0.6982 | 0.8546 | 0.5904 | |
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| No log | 3.0 | 300 | 0.6696 | 0.349 | 1.0 | 0.8606 | 0.1123 | 0.7641 | 0.885 | 0.6857 | 0.8436 | 0.6634 | |
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| No log | 4.0 | 400 | 0.6555 | 0.3432 | 1.0 | 0.8662 | 0.1121 | 0.7518 | 0.8825 | 0.6757 | 0.8455 | 0.6604 | |
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| 0.6931 | 5.0 | 500 | 0.6509 | 0.3414 | 1.0 | 0.8678 | 0.1121 | 0.7504 | 0.8812 | 0.6730 | 0.8449 | 0.6539 | |
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### Framework versions |
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- PEFT 0.13.2 |
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- Transformers 4.47.0 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.1.0 |
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- Tokenizers 0.21.0 |
