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mpalaval
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assignment2_attempt11

Token Classification
Transformers
PyTorch
bert
Generated from Trainer
Model card Files
xet
Community
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End of training

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  1. README.md +20 -20
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -20,11 +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-cased](https://huggingface.co/bert-base-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5518
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- - Precision: 0.2708
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- - Recall: 0.1102
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- - F1: 0.1566
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- - Accuracy: 0.9379
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  ## Model description
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@@ -55,21 +55,21 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | No log | 1.0 | 128 | 0.3036 | 0.2553 | 0.1017 | 0.1455 | 0.9411 |
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- | No log | 2.0 | 256 | 0.3801 | 0.2553 | 0.1017 | 0.1455 | 0.9391 |
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- | No log | 3.0 | 384 | 0.3556 | 0.13 | 0.1102 | 0.1193 | 0.9337 |
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- | 0.0586 | 4.0 | 512 | 0.3874 | 0.1806 | 0.1102 | 0.1368 | 0.9368 |
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- | 0.0586 | 5.0 | 640 | 0.4519 | 0.2545 | 0.1186 | 0.1618 | 0.9387 |
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- | 0.0586 | 6.0 | 768 | 0.4581 | 0.2593 | 0.1186 | 0.1628 | 0.9382 |
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- | 0.0586 | 7.0 | 896 | 0.4750 | 0.2222 | 0.1356 | 0.1684 | 0.9368 |
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- | 0.0088 | 8.0 | 1024 | 0.5352 | 0.2889 | 0.1102 | 0.1595 | 0.9397 |
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- | 0.0088 | 9.0 | 1152 | 0.5362 | 0.2889 | 0.1102 | 0.1595 | 0.9397 |
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- | 0.0088 | 10.0 | 1280 | 0.5526 | 0.2826 | 0.1102 | 0.1585 | 0.9385 |
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- | 0.0088 | 11.0 | 1408 | 0.5330 | 0.2778 | 0.1271 | 0.1744 | 0.9379 |
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- | 0.0022 | 12.0 | 1536 | 0.5490 | 0.2917 | 0.1186 | 0.1687 | 0.9382 |
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- | 0.0022 | 13.0 | 1664 | 0.5442 | 0.28 | 0.1186 | 0.1667 | 0.9383 |
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- | 0.0022 | 14.0 | 1792 | 0.5502 | 0.2653 | 0.1102 | 0.1557 | 0.9376 |
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- | 0.0022 | 15.0 | 1920 | 0.5518 | 0.2708 | 0.1102 | 0.1566 | 0.9379 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5801
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+ - Precision: 0.25
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+ - Recall: 0.1271
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+ - F1: 0.1685
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+ - Accuracy: 0.9361
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 1.0 | 128 | 0.3153 | 0.0 | 0.0 | 0.0 | 0.9388 |
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+ | No log | 2.0 | 256 | 0.2743 | 0.1452 | 0.0763 | 0.1000 | 0.9362 |
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+ | No log | 3.0 | 384 | 0.3585 | 0.1636 | 0.0763 | 0.1040 | 0.9331 |
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+ | 0.2077 | 4.0 | 512 | 0.4103 | 0.2973 | 0.0932 | 0.1419 | 0.9391 |
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+ | 0.2077 | 5.0 | 640 | 0.4270 | 0.3111 | 0.1186 | 0.1718 | 0.9404 |
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+ | 0.2077 | 6.0 | 768 | 0.4653 | 0.2414 | 0.1186 | 0.1591 | 0.9371 |
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+ | 0.2077 | 7.0 | 896 | 0.4569 | 0.14 | 0.1186 | 0.1284 | 0.9316 |
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+ | 0.023 | 8.0 | 1024 | 0.4619 | 0.1647 | 0.1186 | 0.1379 | 0.9333 |
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+ | 0.023 | 9.0 | 1152 | 0.5165 | 0.2456 | 0.1186 | 0.16 | 0.9372 |
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+ | 0.023 | 10.0 | 1280 | 0.5495 | 0.2459 | 0.1271 | 0.1676 | 0.9361 |
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+ | 0.023 | 11.0 | 1408 | 0.5714 | 0.2778 | 0.1271 | 0.1744 | 0.9378 |
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+ | 0.0047 | 12.0 | 1536 | 0.5712 | 0.2586 | 0.1271 | 0.1705 | 0.9368 |
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+ | 0.0047 | 13.0 | 1664 | 0.5776 | 0.2542 | 0.1271 | 0.1695 | 0.9363 |
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+ | 0.0047 | 14.0 | 1792 | 0.5785 | 0.2632 | 0.1271 | 0.1714 | 0.9365 |
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+ | 0.0047 | 15.0 | 1920 | 0.5801 | 0.25 | 0.1271 | 0.1685 | 0.9361 |
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  ### Framework versions
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