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--- |
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language: |
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- nl |
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license: apache-2.0 |
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base_model: bert-base-uncased |
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tags: |
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- abc |
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- generated_from_trainer |
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datasets: |
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- stsb_multi_mt |
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model-index: |
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- name: bert-base-uncased-FinedTuned |
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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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# bert-base-uncased-FinedTuned |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the stsb_multi_mt dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.7341 |
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- Pearson: 0.2384 |
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- Mse: 2.7341 |
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- Custom Accuracy: 0.2567 |
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- Dataset Accuracy: 0.1762 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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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: 1000 |
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- training_steps: 12000 |
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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 | Pearson | Mse | Custom Accuracy | Dataset Accuracy | |
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|:-------------:|:-------:|:-----:|:---------------:|:-------:|:------:|:---------------:|:----------------:| |
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| 0.0188 | 5.5556 | 1000 | 2.9224 | 0.2311 | 2.9224 | 0.2429 | 0.1762 | |
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| 0.0367 | 11.1111 | 2000 | 2.8363 | 0.2219 | 2.8363 | 0.2524 | 0.1762 | |
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| 0.0151 | 16.6667 | 3000 | 2.8033 | 0.2131 | 2.8033 | 0.2509 | 0.1762 | |
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| 0.0377 | 22.2222 | 4000 | 2.9081 | 0.2205 | 2.9081 | 0.2582 | 0.1762 | |
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| 0.0458 | 27.7778 | 5000 | 2.8001 | 0.2360 | 2.8001 | 0.2611 | 0.1762 | |
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| 0.0324 | 33.3333 | 6000 | 2.7521 | 0.2377 | 2.7521 | 0.2567 | 0.1762 | |
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| 0.0479 | 38.8889 | 7000 | 2.7011 | 0.2441 | 2.7011 | 0.2618 | 0.1762 | |
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| 0.0685 | 44.4444 | 8000 | 2.7119 | 0.2431 | 2.7119 | 0.2611 | 0.1762 | |
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| 0.0463 | 50.0 | 9000 | 2.7674 | 0.2287 | 2.7674 | 0.2603 | 0.1762 | |
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| 0.0879 | 55.5556 | 10000 | 2.7357 | 0.2434 | 2.7357 | 0.2676 | 0.1762 | |
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| 0.0733 | 61.1111 | 11000 | 2.7392 | 0.2374 | 2.7392 | 0.2567 | 0.1762 | |
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| 0.1541 | 66.6667 | 12000 | 2.7341 | 0.2384 | 2.7341 | 0.2567 | 0.1762 | |
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### Framework versions |
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- Transformers 4.42.3 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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