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--- |
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license: apache-2.0 |
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base_model: sentence-transformers/paraphrase-mpnet-base-v2 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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model-index: |
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- name: paraphrase-mpnet-base-v2_mbti_full |
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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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# paraphrase-mpnet-base-v2_mbti_full |
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This model is a fine-tuned version of [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5933 |
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- F1: 0.5931 |
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- Roc Auc: 0.6834 |
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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: 3e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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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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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| |
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| No log | 1.0 | 325 | 0.5755 | 0.4804 | 0.6197 | |
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| 0.5774 | 2.0 | 651 | 0.5656 | 0.2262 | 0.5484 | |
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| 0.5774 | 3.0 | 976 | 0.5515 | 0.3992 | 0.6041 | |
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| 0.5594 | 4.0 | 1302 | 0.5487 | 0.4959 | 0.6344 | |
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| 0.5352 | 5.0 | 1627 | 0.5429 | 0.5776 | 0.6775 | |
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| 0.5352 | 6.0 | 1953 | 0.5557 | 0.5332 | 0.6560 | |
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| 0.4996 | 7.0 | 2278 | 0.5681 | 0.5895 | 0.6837 | |
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| 0.4531 | 8.0 | 2604 | 0.5793 | 0.5783 | 0.6783 | |
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| 0.4531 | 9.0 | 2929 | 0.5924 | 0.5982 | 0.6882 | |
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| 0.4204 | 9.98 | 3250 | 0.5970 | 0.5786 | 0.6778 | |
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### Framework versions |
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- Transformers 4.39.1 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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