End of training
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README.md
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base_model: distilbert-base-uncased-finetuned-sst-2-english
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tags:
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- generated_from_trainer
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datasets:
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- sembr2023
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metrics:
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- precision
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- recall
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- accuracy
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model-index:
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- name: sembr2023-distilbert-base-uncased-finetuned-sst-2-english
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results:
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- task:
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name: Token Classification
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type: token-classification
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dataset:
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name: sembr2023
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type: sembr2023
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config: sembr2023
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split: test
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args: sembr2023
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metrics:
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- name: Precision
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type: precision
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value: 0.7586922044650481
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- name: Recall
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type: recall
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value: 0.8214238541804253
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- name: F1
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type: f1
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value: 0.7888127853881278
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- name: Accuracy
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type: accuracy
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value: 0.9610840374434667
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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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# sembr2023-distilbert-base-uncased-finetuned-sst-2-english
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-
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Precision: 0.
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- Recall: 0.
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- F1: 0.
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- Iou: 0.
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- Accuracy: 0.
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- Balanced Accuracy: 0.
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- Overall Accuracy: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Iou | Accuracy | Balanced Accuracy | Overall Accuracy |
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### Framework versions
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base_model: distilbert-base-uncased-finetuned-sst-2-english
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tags:
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- generated_from_trainer
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metrics:
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- precision
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- recall
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- accuracy
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model-index:
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- name: sembr2023-distilbert-base-uncased-finetuned-sst-2-english
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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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# sembr2023-distilbert-base-uncased-finetuned-sst-2-english
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+
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2242
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- Precision: 0.8042
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- Recall: 0.8338
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- F1: 0.8187
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- Iou: 0.6930
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- Accuracy: 0.9660
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- Balanced Accuracy: 0.9066
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- Overall Accuracy: 0.9529
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Iou | Accuracy | Balanced Accuracy | Overall Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:------:|:--------:|:-----------------:|:----------------:|
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| 0.3902 | 0.06 | 10 | 0.3870 | 0 | 0.0 | 0.0 | 0.0 | 0.9080 | 0.5 | 0.9080 |
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+
| 0.3064 | 0.12 | 20 | 0.3030 | 1.0 | 0.0024 | 0.0048 | 0.0024 | 0.9083 | 0.5012 | 0.9083 |
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| 63 |
+
| 0.2489 | 0.18 | 30 | 0.2335 | 0.7510 | 0.6056 | 0.6705 | 0.5043 | 0.9453 | 0.7926 | 0.9288 |
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| 64 |
+
| 0.1931 | 0.24 | 40 | 0.1983 | 0.7924 | 0.6957 | 0.7409 | 0.5884 | 0.9552 | 0.8386 | 0.9385 |
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| 65 |
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| 0.1417 | 0.3 | 50 | 0.1830 | 0.8148 | 0.7208 | 0.7649 | 0.6193 | 0.9593 | 0.8521 | 0.9412 |
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| 66 |
+
| 0.1581 | 0.36 | 60 | 0.1756 | 0.8102 | 0.7475 | 0.7776 | 0.6361 | 0.9607 | 0.8649 | 0.9430 |
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| 67 |
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| 0.1572 | 0.42 | 70 | 0.1681 | 0.7986 | 0.7811 | 0.7898 | 0.6526 | 0.9618 | 0.8806 | 0.9440 |
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| 68 |
+
| 0.1304 | 0.48 | 80 | 0.1617 | 0.7915 | 0.7978 | 0.7946 | 0.6593 | 0.9621 | 0.8883 | 0.9442 |
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| 69 |
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| 0.1203 | 0.55 | 90 | 0.1554 | 0.8233 | 0.7706 | 0.7961 | 0.6612 | 0.9637 | 0.8769 | 0.9471 |
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| 70 |
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| 0.1249 | 0.61 | 100 | 0.1782 | 0.7805 | 0.8080 | 0.7940 | 0.6584 | 0.9614 | 0.8925 | 0.9423 |
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| 71 |
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| 0.1212 | 0.67 | 110 | 0.1502 | 0.8589 | 0.7550 | 0.8036 | 0.6717 | 0.9661 | 0.8712 | 0.9494 |
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| 72 |
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| 0.0883 | 0.73 | 120 | 0.1555 | 0.8076 | 0.8120 | 0.8098 | 0.6803 | 0.9649 | 0.8962 | 0.9457 |
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| 73 |
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| 0.0921 | 0.79 | 130 | 0.1561 | 0.8156 | 0.7936 | 0.8044 | 0.6729 | 0.9645 | 0.8877 | 0.9471 |
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| 74 |
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| 0.112 | 0.85 | 140 | 0.1444 | 0.7773 | 0.8389 | 0.8069 | 0.6763 | 0.9631 | 0.9073 | 0.9485 |
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| 75 |
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| 0.0858 | 0.91 | 150 | 0.1530 | 0.8258 | 0.7806 | 0.8026 | 0.6702 | 0.9647 | 0.8820 | 0.9490 |
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| 76 |
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| 0.076 | 0.97 | 160 | 0.1355 | 0.8478 | 0.7679 | 0.8059 | 0.6749 | 0.9660 | 0.8770 | 0.9537 |
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| 77 |
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| 0.0891 | 1.03 | 170 | 0.1468 | 0.8333 | 0.7996 | 0.8161 | 0.6893 | 0.9669 | 0.8917 | 0.9512 |
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| 78 |
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| 0.0727 | 1.09 | 180 | 0.1394 | 0.8659 | 0.7685 | 0.8143 | 0.6868 | 0.9678 | 0.8782 | 0.9543 |
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| 79 |
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| 0.0707 | 1.15 | 190 | 0.1396 | 0.8585 | 0.7792 | 0.8170 | 0.6906 | 0.9679 | 0.8831 | 0.9546 |
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| 80 |
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| 0.0827 | 1.21 | 200 | 0.1365 | 0.8231 | 0.8098 | 0.8164 | 0.6898 | 0.9665 | 0.8961 | 0.9542 |
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| 81 |
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| 0.0628 | 1.27 | 210 | 0.1629 | 0.8189 | 0.8157 | 0.8173 | 0.6910 | 0.9665 | 0.8987 | 0.9507 |
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| 82 |
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| 0.0544 | 1.33 | 220 | 0.1490 | 0.8179 | 0.8182 | 0.8181 | 0.6921 | 0.9665 | 0.8999 | 0.9536 |
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| 83 |
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| 0.0581 | 1.39 | 230 | 0.1618 | 0.7956 | 0.8346 | 0.8147 | 0.6873 | 0.9651 | 0.9065 | 0.9489 |
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| 84 |
+
| 0.0508 | 1.45 | 240 | 0.1583 | 0.8032 | 0.8191 | 0.8111 | 0.6822 | 0.9649 | 0.8994 | 0.9526 |
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| 85 |
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| 0.0477 | 1.52 | 250 | 0.1524 | 0.8149 | 0.8223 | 0.8186 | 0.6929 | 0.9665 | 0.9017 | 0.9544 |
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| 86 |
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| 0.0493 | 1.58 | 260 | 0.1518 | 0.8422 | 0.7969 | 0.8190 | 0.6934 | 0.9676 | 0.8909 | 0.9551 |
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| 87 |
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| 0.0586 | 1.64 | 270 | 0.1635 | 0.8112 | 0.8194 | 0.8153 | 0.6881 | 0.9658 | 0.9000 | 0.9510 |
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| 88 |
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| 0.0438 | 1.7 | 280 | 0.1819 | 0.7835 | 0.8446 | 0.8129 | 0.6848 | 0.9642 | 0.9105 | 0.9479 |
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| 89 |
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| 0.0544 | 1.76 | 290 | 0.1781 | 0.8208 | 0.8190 | 0.8199 | 0.6947 | 0.9669 | 0.9004 | 0.9505 |
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| 90 |
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| 0.0527 | 1.82 | 300 | 0.1547 | 0.8213 | 0.8157 | 0.8185 | 0.6927 | 0.9667 | 0.8988 | 0.9538 |
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| 91 |
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| 0.0449 | 1.88 | 310 | 0.1603 | 0.8095 | 0.8301 | 0.8197 | 0.6944 | 0.9664 | 0.9051 | 0.9533 |
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| 92 |
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| 0.0556 | 1.94 | 320 | 0.1627 | 0.7995 | 0.8312 | 0.8151 | 0.6879 | 0.9653 | 0.9051 | 0.9519 |
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| 93 |
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| 0.0459 | 2.0 | 330 | 0.1525 | 0.8324 | 0.7990 | 0.8153 | 0.6882 | 0.9667 | 0.8913 | 0.9542 |
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| 94 |
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| 0.0401 | 2.06 | 340 | 0.1915 | 0.7856 | 0.8469 | 0.8151 | 0.6879 | 0.9647 | 0.9117 | 0.9480 |
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| 95 |
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| 0.0384 | 2.12 | 350 | 0.1791 | 0.8060 | 0.8282 | 0.8169 | 0.6905 | 0.9659 | 0.9040 | 0.9512 |
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| 96 |
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| 0.0358 | 2.18 | 360 | 0.1831 | 0.8265 | 0.8084 | 0.8174 | 0.6911 | 0.9668 | 0.8956 | 0.9515 |
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| 97 |
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| 0.0263 | 2.24 | 370 | 0.1735 | 0.8188 | 0.8118 | 0.8153 | 0.6882 | 0.9662 | 0.8968 | 0.9515 |
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| 98 |
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| 0.0313 | 2.3 | 380 | 0.1828 | 0.7911 | 0.8408 | 0.8152 | 0.6880 | 0.9649 | 0.9091 | 0.9510 |
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| 99 |
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| 0.0323 | 2.36 | 390 | 0.1760 | 0.8245 | 0.8194 | 0.8219 | 0.6977 | 0.9673 | 0.9008 | 0.9542 |
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| 100 |
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| 0.034 | 2.42 | 400 | 0.1693 | 0.8306 | 0.8032 | 0.8167 | 0.6901 | 0.9668 | 0.8933 | 0.9540 |
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| 101 |
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| 0.0376 | 2.48 | 410 | 0.1928 | 0.7556 | 0.8576 | 0.8033 | 0.6713 | 0.9614 | 0.9147 | 0.9481 |
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| 102 |
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| 0.0312 | 2.55 | 420 | 0.1761 | 0.8197 | 0.8194 | 0.8195 | 0.6942 | 0.9668 | 0.9006 | 0.9537 |
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| 103 |
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| 0.0266 | 2.61 | 430 | 0.1805 | 0.8175 | 0.8148 | 0.8161 | 0.6894 | 0.9662 | 0.8982 | 0.9534 |
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| 104 |
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| 0.0388 | 2.67 | 440 | 0.2017 | 0.7774 | 0.8571 | 0.8153 | 0.6882 | 0.9643 | 0.9161 | 0.9477 |
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| 105 |
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| 0.0337 | 2.73 | 450 | 0.1764 | 0.8145 | 0.8195 | 0.8170 | 0.6906 | 0.9662 | 0.9003 | 0.9530 |
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| 106 |
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| 0.0255 | 2.79 | 460 | 0.1786 | 0.8267 | 0.8039 | 0.8152 | 0.6880 | 0.9665 | 0.8934 | 0.9533 |
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| 107 |
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| 0.0299 | 2.85 | 470 | 0.1844 | 0.8048 | 0.8343 | 0.8193 | 0.6939 | 0.9662 | 0.9069 | 0.9529 |
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| 108 |
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| 0.0264 | 2.91 | 480 | 0.1865 | 0.7909 | 0.8455 | 0.8173 | 0.6910 | 0.9652 | 0.9114 | 0.9526 |
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| 109 |
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| 0.0282 | 2.97 | 490 | 0.1816 | 0.8105 | 0.8206 | 0.8155 | 0.6885 | 0.9659 | 0.9006 | 0.9532 |
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| 110 |
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| 0.0269 | 3.03 | 500 | 0.1969 | 0.8195 | 0.8178 | 0.8187 | 0.6930 | 0.9667 | 0.8998 | 0.9516 |
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| 111 |
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| 0.0258 | 3.09 | 510 | 0.2061 | 0.8070 | 0.8341 | 0.8203 | 0.6954 | 0.9664 | 0.9070 | 0.9513 |
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| 112 |
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| 0.0239 | 3.15 | 520 | 0.2138 | 0.7878 | 0.8392 | 0.8127 | 0.6845 | 0.9644 | 0.9082 | 0.9492 |
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| 113 |
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| 0.0234 | 3.21 | 530 | 0.1984 | 0.8203 | 0.8153 | 0.8178 | 0.6917 | 0.9666 | 0.8986 | 0.9521 |
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| 114 |
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| 0.0188 | 3.27 | 540 | 0.2032 | 0.8081 | 0.8254 | 0.8166 | 0.6901 | 0.9659 | 0.9027 | 0.9513 |
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| 115 |
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| 0.0258 | 3.33 | 550 | 0.2045 | 0.7976 | 0.8355 | 0.8162 | 0.6894 | 0.9654 | 0.9070 | 0.9524 |
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| 116 |
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| 0.0167 | 3.39 | 560 | 0.2052 | 0.7851 | 0.8389 | 0.8111 | 0.6822 | 0.9641 | 0.9078 | 0.9512 |
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| 117 |
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| 0.0203 | 3.45 | 570 | 0.2261 | 0.7912 | 0.8409 | 0.8153 | 0.6882 | 0.9650 | 0.9092 | 0.9489 |
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| 118 |
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| 0.0173 | 3.52 | 580 | 0.2094 | 0.7816 | 0.8503 | 0.8145 | 0.6871 | 0.9644 | 0.9131 | 0.9489 |
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| 119 |
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| 0.0249 | 3.58 | 590 | 0.2101 | 0.7968 | 0.8394 | 0.8175 | 0.6914 | 0.9655 | 0.9088 | 0.9515 |
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| 120 |
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| 0.0198 | 3.64 | 600 | 0.2015 | 0.7947 | 0.8425 | 0.8179 | 0.6919 | 0.9655 | 0.9102 | 0.9516 |
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| 121 |
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| 0.0194 | 3.7 | 610 | 0.2160 | 0.7895 | 0.8494 | 0.8184 | 0.6926 | 0.9653 | 0.9132 | 0.9505 |
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| 122 |
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| 0.0176 | 3.76 | 620 | 0.2121 | 0.7893 | 0.8434 | 0.8155 | 0.6885 | 0.9649 | 0.9103 | 0.9506 |
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| 123 |
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| 0.021 | 3.82 | 630 | 0.2020 | 0.8127 | 0.8282 | 0.8204 | 0.6954 | 0.9666 | 0.9044 | 0.9525 |
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| 124 |
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| 0.019 | 3.88 | 640 | 0.2133 | 0.8157 | 0.8266 | 0.8211 | 0.6965 | 0.9669 | 0.9039 | 0.9523 |
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| 125 |
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| 0.0184 | 3.94 | 650 | 0.2015 | 0.8056 | 0.8303 | 0.8178 | 0.6917 | 0.9660 | 0.9050 | 0.9536 |
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| 126 |
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| 0.0202 | 4.0 | 660 | 0.2126 | 0.8106 | 0.8201 | 0.8153 | 0.6883 | 0.9658 | 0.9004 | 0.9513 |
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| 127 |
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| 0.0182 | 4.06 | 670 | 0.2114 | 0.8027 | 0.8320 | 0.8171 | 0.6907 | 0.9657 | 0.9056 | 0.9528 |
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| 128 |
+
| 0.0174 | 4.12 | 680 | 0.2246 | 0.7973 | 0.8375 | 0.8169 | 0.6905 | 0.9655 | 0.9079 | 0.9511 |
|
| 129 |
+
| 0.0149 | 4.18 | 690 | 0.2140 | 0.8123 | 0.8259 | 0.8190 | 0.6935 | 0.9664 | 0.9033 | 0.9533 |
|
| 130 |
+
| 0.0135 | 4.24 | 700 | 0.2187 | 0.8029 | 0.8329 | 0.8176 | 0.6915 | 0.9658 | 0.9061 | 0.9523 |
|
| 131 |
+
| 0.0202 | 4.3 | 710 | 0.2165 | 0.8118 | 0.8250 | 0.8183 | 0.6925 | 0.9663 | 0.9028 | 0.9530 |
|
| 132 |
+
| 0.0139 | 4.36 | 720 | 0.2203 | 0.8007 | 0.8332 | 0.8167 | 0.6901 | 0.9656 | 0.9061 | 0.9519 |
|
| 133 |
+
| 0.0153 | 4.42 | 730 | 0.2297 | 0.7920 | 0.8429 | 0.8167 | 0.6901 | 0.9652 | 0.9103 | 0.9511 |
|
| 134 |
+
| 0.0144 | 4.48 | 740 | 0.2241 | 0.8090 | 0.8330 | 0.8208 | 0.6961 | 0.9666 | 0.9065 | 0.9527 |
|
| 135 |
+
| 0.0113 | 4.55 | 750 | 0.2218 | 0.8015 | 0.8352 | 0.8180 | 0.6920 | 0.9658 | 0.9071 | 0.9530 |
|
| 136 |
+
| 0.0192 | 4.61 | 760 | 0.2236 | 0.8021 | 0.8376 | 0.8195 | 0.6942 | 0.9661 | 0.9083 | 0.9523 |
|
| 137 |
+
| 0.0162 | 4.67 | 770 | 0.2226 | 0.7928 | 0.8434 | 0.8174 | 0.6911 | 0.9653 | 0.9106 | 0.9521 |
|
| 138 |
+
| 0.0144 | 4.73 | 780 | 0.2188 | 0.8054 | 0.8361 | 0.8204 | 0.6955 | 0.9663 | 0.9078 | 0.9531 |
|
| 139 |
+
| 0.0159 | 4.79 | 790 | 0.2234 | 0.8022 | 0.8359 | 0.8187 | 0.6931 | 0.9660 | 0.9075 | 0.9525 |
|
| 140 |
+
| 0.0136 | 4.85 | 800 | 0.2230 | 0.8033 | 0.8334 | 0.8181 | 0.6921 | 0.9659 | 0.9064 | 0.9529 |
|
| 141 |
+
| 0.0197 | 4.91 | 810 | 0.2239 | 0.8020 | 0.8363 | 0.8188 | 0.6932 | 0.9660 | 0.9077 | 0.9525 |
|
| 142 |
+
| 0.0165 | 4.97 | 820 | 0.2212 | 0.8048 | 0.8339 | 0.8191 | 0.6936 | 0.9661 | 0.9067 | 0.9524 |
|
| 143 |
+
| 0.0146 | 5.03 | 830 | 0.2228 | 0.8071 | 0.8308 | 0.8188 | 0.6932 | 0.9662 | 0.9054 | 0.9528 |
|
| 144 |
+
| 0.0109 | 5.09 | 840 | 0.2255 | 0.8079 | 0.8311 | 0.8193 | 0.6940 | 0.9663 | 0.9055 | 0.9530 |
|
| 145 |
+
| 0.0104 | 5.15 | 850 | 0.2235 | 0.8066 | 0.8316 | 0.8189 | 0.6934 | 0.9662 | 0.9057 | 0.9534 |
|
| 146 |
+
| 0.0152 | 5.21 | 860 | 0.2239 | 0.8051 | 0.8331 | 0.8189 | 0.6933 | 0.9661 | 0.9063 | 0.9532 |
|
| 147 |
+
| 0.0118 | 5.27 | 870 | 0.2242 | 0.8002 | 0.8389 | 0.8191 | 0.6936 | 0.9659 | 0.9088 | 0.9526 |
|
| 148 |
+
| 0.0106 | 5.33 | 880 | 0.2225 | 0.8047 | 0.8334 | 0.8188 | 0.6932 | 0.9661 | 0.9064 | 0.9527 |
|
| 149 |
+
| 0.0127 | 5.39 | 890 | 0.2232 | 0.8017 | 0.8349 | 0.8180 | 0.6920 | 0.9658 | 0.9070 | 0.9526 |
|
| 150 |
+
| 0.0126 | 5.45 | 900 | 0.2246 | 0.8026 | 0.8343 | 0.8181 | 0.6922 | 0.9659 | 0.9067 | 0.9527 |
|
| 151 |
+
| 0.0159 | 5.52 | 910 | 0.2241 | 0.8041 | 0.8343 | 0.8189 | 0.6933 | 0.9661 | 0.9068 | 0.9529 |
|
| 152 |
+
| 0.0182 | 5.58 | 920 | 0.2245 | 0.8060 | 0.8327 | 0.8192 | 0.6937 | 0.9662 | 0.9062 | 0.9529 |
|
| 153 |
+
| 0.0154 | 5.64 | 930 | 0.2251 | 0.8041 | 0.8331 | 0.8184 | 0.6926 | 0.9660 | 0.9063 | 0.9527 |
|
| 154 |
+
| 0.012 | 5.7 | 940 | 0.2245 | 0.8036 | 0.8343 | 0.8186 | 0.6929 | 0.9660 | 0.9068 | 0.9529 |
|
| 155 |
+
| 0.0177 | 5.76 | 950 | 0.2246 | 0.8035 | 0.8344 | 0.8186 | 0.6930 | 0.9660 | 0.9069 | 0.9528 |
|
| 156 |
+
| 0.0162 | 5.82 | 960 | 0.2245 | 0.8040 | 0.8340 | 0.8187 | 0.6931 | 0.9660 | 0.9067 | 0.9529 |
|
| 157 |
+
| 0.0177 | 5.88 | 970 | 0.2243 | 0.8040 | 0.8338 | 0.8186 | 0.6929 | 0.9660 | 0.9066 | 0.9529 |
|
| 158 |
+
| 0.0147 | 5.94 | 980 | 0.2242 | 0.8041 | 0.8336 | 0.8186 | 0.6929 | 0.9660 | 0.9065 | 0.9529 |
|
| 159 |
+
| 0.0123 | 6.0 | 990 | 0.2242 | 0.8042 | 0.8338 | 0.8187 | 0.6930 | 0.9660 | 0.9066 | 0.9529 |
|
| 160 |
+
| 0.0123 | 6.06 | 1000 | 0.2242 | 0.8042 | 0.8338 | 0.8187 | 0.6930 | 0.9660 | 0.9066 | 0.9529 |
|
| 161 |
|
| 162 |
|
| 163 |
### Framework versions
|
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 265520165
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:0fa69df9681c32b99b8eead25b723e91c554ae53639328d32ede86f0fb6844cf
|
| 3 |
size 265520165
|
training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 4283
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3f798d3616fbc0d2ba69b9cc82de9ece969255ffd12cb626c1c7dfe715ec417a
|
| 3 |
size 4283
|