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bert-base-uncased-FinedTuned

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  1. README.md +16 -16
  2. events.out.tfevents.1719948302.628cf1ad7240.1549.4 +2 -2
  3. model.safetensors +1 -1
README.md CHANGED
@@ -20,10 +20,10 @@ should probably proofread and complete it, then remove this comment. -->
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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: 2.7638
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- - Pearson: 0.2339
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- - Mse: 2.7638
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- - Custom Accuracy: 0.2603
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  - Dataset Accuracy: 0.1762
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  ## Model description
@@ -59,18 +59,18 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Pearson | Mse | Custom Accuracy | Dataset Accuracy |
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  |:-------------:|:-------:|:-----:|:---------------:|:-------:|:------:|:---------------:|:----------------:|
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- | 0.0219 | 5.5556 | 1000 | 2.8140 | 0.2324 | 2.8140 | 0.2437 | 0.1762 |
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- | 0.0195 | 11.1111 | 2000 | 2.9679 | 0.2078 | 2.9679 | 0.2618 | 0.1762 |
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- | 0.0184 | 16.6667 | 3000 | 2.7712 | 0.2476 | 2.7712 | 0.2683 | 0.1762 |
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- | 0.0213 | 22.2222 | 4000 | 2.7564 | 0.2486 | 2.7564 | 0.2661 | 0.1762 |
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- | 0.0222 | 27.7778 | 5000 | 2.8691 | 0.2333 | 2.8691 | 0.2596 | 0.1762 |
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- | 0.0151 | 33.3333 | 6000 | 2.7762 | 0.2451 | 2.7762 | 0.2560 | 0.1762 |
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- | 0.0318 | 38.8889 | 7000 | 2.8121 | 0.2370 | 2.8121 | 0.2647 | 0.1762 |
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- | 0.0616 | 44.4444 | 8000 | 2.8343 | 0.2195 | 2.8343 | 0.2560 | 0.1762 |
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- | 0.0335 | 50.0 | 9000 | 2.8070 | 0.2259 | 2.8070 | 0.2676 | 0.1762 |
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- | 0.0553 | 55.5556 | 10000 | 2.7934 | 0.2330 | 2.7934 | 0.2531 | 0.1762 |
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- | 0.0718 | 61.1111 | 11000 | 2.7822 | 0.2286 | 2.7822 | 0.2603 | 0.1762 |
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- | 0.1741 | 66.6667 | 12000 | 2.7638 | 0.2339 | 2.7638 | 0.2603 | 0.1762 |
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  ### Framework versions
 
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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: 2.7758
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+ - Pearson: 0.2352
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+ - Mse: 2.7758
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+ - Custom Accuracy: 0.2611
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  - Dataset Accuracy: 0.1762
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  ## Model description
 
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  | Training Loss | Epoch | Step | Validation Loss | Pearson | Mse | Custom Accuracy | Dataset Accuracy |
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  |:-------------:|:-------:|:-----:|:---------------:|:-------:|:------:|:---------------:|:----------------:|
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+ | 0.028 | 5.5556 | 1000 | 2.7386 | 0.2467 | 2.7386 | 0.2502 | 0.1762 |
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+ | 0.0269 | 11.1111 | 2000 | 2.8265 | 0.2229 | 2.8265 | 0.2589 | 0.1762 |
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+ | 0.0088 | 16.6667 | 3000 | 2.8485 | 0.2219 | 2.8485 | 0.2654 | 0.1762 |
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+ | 0.0141 | 22.2222 | 4000 | 2.8855 | 0.2086 | 2.8855 | 0.2661 | 0.1762 |
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+ | 0.0099 | 27.7778 | 5000 | 2.8081 | 0.2328 | 2.8081 | 0.2632 | 0.1762 |
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+ | 0.0248 | 33.3333 | 6000 | 2.7765 | 0.2309 | 2.7765 | 0.2625 | 0.1762 |
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+ | 0.0353 | 38.8889 | 7000 | 2.8126 | 0.2296 | 2.8126 | 0.2748 | 0.1762 |
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+ | 0.0892 | 44.4444 | 8000 | 2.8362 | 0.2327 | 2.8362 | 0.2567 | 0.1762 |
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+ | 0.0488 | 50.0 | 9000 | 2.7667 | 0.2363 | 2.7667 | 0.2596 | 0.1762 |
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+ | 0.0538 | 55.5556 | 10000 | 2.7885 | 0.2363 | 2.7885 | 0.2632 | 0.1762 |
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+ | 0.0829 | 61.1111 | 11000 | 2.7837 | 0.2348 | 2.7837 | 0.2647 | 0.1762 |
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+ | 0.1473 | 66.6667 | 12000 | 2.7758 | 0.2352 | 2.7758 | 0.2611 | 0.1762 |
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  ### Framework versions
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