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---
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library_name: peft
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license: mit
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base_model: FacebookAI/roberta-base
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tags:
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- generated_from_trainer
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model-index:
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- name: dapper-ape-848
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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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# dapper-ape-848 |
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This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5164 |
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- Hamming Loss: 0.1123 |
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- Zero One Loss: 1.0 |
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- Jaccard Score: 1.0 |
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- Hamming Loss Optimised: 0.1123 |
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- Hamming Loss Threshold: 0.5944 |
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- Zero One Loss Optimised: 0.8712 |
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- Zero One Loss Threshold: 0.4290 |
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- Jaccard Score Optimised: 0.8190 |
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- Jaccard Score Threshold: 0.4039 |
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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: 8.506034831608646e-06 |
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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: 11 |
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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 | 0.7056 | 0.567 | 1.0 | 0.8855 | 0.1123 | 0.5944 | 1.0 | 0.9000 | 0.8878 | 0.2889 | |
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| No log | 2.0 | 200 | 0.7020 | 0.4813 | 1.0 | 0.8993 | 0.1123 | 0.5944 | 1.0 | 0.9000 | 0.8878 | 0.2889 | |
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| No log | 3.0 | 300 | 0.6961 | 0.4363 | 1.0 | 0.9071 | 0.1123 | 0.5944 | 1.0 | 0.9000 | 0.8878 | 0.2889 | |
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| No log | 4.0 | 400 | 0.6856 | 0.4355 | 1.0 | 0.907 | 0.1123 | 0.5944 | 1.0 | 0.9000 | 0.8878 | 0.2889 | |
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| 0.6963 | 5.0 | 500 | 0.6619 | 0.2924 | 0.9912 | 0.9281 | 0.1123 | 0.5944 | 1.0 | 0.9000 | 0.8878 | 0.2889 | |
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| 0.6963 | 6.0 | 600 | 0.6033 | 0.1124 | 1.0 | 1.0 | 0.1123 | 0.5944 | 1.0 | 0.9000 | 0.8540 | 0.4530 | |
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| 0.6963 | 7.0 | 700 | 0.5635 | 0.1123 | 1.0 | 1.0 | 0.1123 | 0.5944 | 1.0 | 0.9000 | 0.8212 | 0.4305 | |
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| 0.6963 | 8.0 | 800 | 0.5386 | 0.1123 | 1.0 | 1.0 | 0.1123 | 0.5944 | 1.0 | 0.9000 | 0.8135 | 0.4232 | |
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| 0.6963 | 9.0 | 900 | 0.5250 | 0.1123 | 1.0 | 1.0 | 0.1123 | 0.5944 | 0.895 | 0.4370 | 0.8192 | 0.4047 | |
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| 0.5852 | 10.0 | 1000 | 0.5184 | 0.1123 | 1.0 | 1.0 | 0.1123 | 0.5944 | 0.88 | 0.4306 | 0.8163 | 0.4115 | |
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| 0.5852 | 11.0 | 1100 | 0.5164 | 0.1123 | 1.0 | 1.0 | 0.1123 | 0.5944 | 0.8712 | 0.4290 | 0.8190 | 0.4039 | |
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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 |
