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bert-base-uncased-FinedTuned

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  1. README.md +16 -16
  2. events.out.tfevents.1719935722.68005939a7c4.1503.0 +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: 2.7563
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- - Pearson: 0.2355
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- - Mse: 2.7563
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- - Custom Accuracy: 0.2596
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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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- | 0.0232 | 5.5556 | 1000 | 2.7795 | 0.2318 | 2.7795 | 0.2524 | 0.1762 |
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- | 0.0217 | 11.1111 | 2000 | 2.7382 | 0.2231 | 2.7382 | 0.2509 | 0.1762 |
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- | 0.0201 | 16.6667 | 3000 | 2.7976 | 0.2330 | 2.7976 | 0.2545 | 0.1762 |
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- | 0.0262 | 22.2222 | 4000 | 2.7060 | 0.2450 | 2.7060 | 0.2473 | 0.1762 |
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- | 0.0241 | 27.7778 | 5000 | 2.7124 | 0.2431 | 2.7124 | 0.2560 | 0.1762 |
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- | 0.036 | 33.3333 | 6000 | 2.7126 | 0.2503 | 2.7126 | 0.2502 | 0.1762 |
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- | 0.049 | 38.8889 | 7000 | 2.6933 | 0.2425 | 2.6933 | 0.2582 | 0.1762 |
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- | 0.0911 | 44.4444 | 8000 | 2.7766 | 0.2268 | 2.7766 | 0.2516 | 0.1762 |
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- | 0.0399 | 50.0 | 9000 | 2.7265 | 0.2313 | 2.7265 | 0.2487 | 0.1762 |
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- | 0.0764 | 55.5556 | 10000 | 2.7674 | 0.2352 | 2.7674 | 0.2524 | 0.1762 |
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- | 0.0597 | 61.1111 | 11000 | 2.7864 | 0.2311 | 2.7864 | 0.2582 | 0.1762 |
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- | 0.1612 | 66.6667 | 12000 | 2.7563 | 0.2355 | 2.7563 | 0.2596 | 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: 2.7341
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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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  | 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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