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bert-base-uncased-FinedTuned

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  1. README.md +19 -20
  2. events.out.tfevents.1719925271.5933cb923c9f.1119.0 +2 -2
  3. model.safetensors +1 -1
README.md CHANGED
@@ -8,8 +8,6 @@ tags:
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  - generated_from_trainer
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  datasets:
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  - stsb_multi_mt
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- metrics:
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- - accuracy
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  model-index:
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  - name: bert-base-uncased-FinedTuned
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  results: []
@@ -22,10 +20,11 @@ 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: 2.6982
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- - Pearson: 0.2449
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- - Mse: 2.6982
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- - Accuracy: 0.2574
 
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  ## Model description
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@@ -58,20 +57,20 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Pearson | Mse | Accuracy |
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- |:-------------:|:-------:|:-----:|:---------------:|:-------:|:------:|:--------:|
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- | 0.0221 | 5.5556 | 1000 | 2.7065 | 0.2549 | 2.7065 | 0.2553 |
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- | 0.0511 | 11.1111 | 2000 | 2.7408 | 0.2264 | 2.7408 | 0.2509 |
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- | 0.0223 | 16.6667 | 3000 | 2.7352 | 0.2356 | 2.7352 | 0.2473 |
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- | 0.026 | 22.2222 | 4000 | 2.7210 | 0.2294 | 2.7210 | 0.2451 |
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- | 0.011 | 27.7778 | 5000 | 2.6722 | 0.2438 | 2.6722 | 0.2545 |
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- | 0.0222 | 33.3333 | 6000 | 2.6768 | 0.2369 | 2.6768 | 0.2538 |
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- | 0.0466 | 38.8889 | 7000 | 2.6867 | 0.2414 | 2.6867 | 0.2589 |
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- | 0.0355 | 44.4444 | 8000 | 2.7838 | 0.2298 | 2.7838 | 0.2538 |
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- | 0.0349 | 50.0 | 9000 | 2.7550 | 0.2272 | 2.7550 | 0.2495 |
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- | 0.0813 | 55.5556 | 10000 | 2.7215 | 0.2473 | 2.7215 | 0.2567 |
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- | 0.0592 | 61.1111 | 11000 | 2.7262 | 0.2412 | 2.7262 | 0.2589 |
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- | 0.1523 | 66.6667 | 12000 | 2.6982 | 0.2449 | 2.6982 | 0.2574 |
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  ### Framework versions
 
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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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  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
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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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+ | 0.0232 | 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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