File size: 6,455 Bytes
ef02fc7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 |
---
library_name: transformers
license: mit
base_model: haryoaw/scenario-TCR-NER_data-univner_half
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: scenario-non-kd-po-ner-full-mdeberta_data-univner_half55
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# scenario-non-kd-po-ner-full-mdeberta_data-univner_half55
This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_half](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_half) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1503
- Precision: 0.8528
- Recall: 0.8701
- F1: 0.8614
- Accuracy: 0.9848
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 55
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0066 | 0.5828 | 500 | 0.1023 | 0.8339 | 0.8461 | 0.8399 | 0.9829 |
| 0.0066 | 1.1655 | 1000 | 0.0991 | 0.8499 | 0.8478 | 0.8488 | 0.9838 |
| 0.0063 | 1.7483 | 1500 | 0.1052 | 0.8388 | 0.8562 | 0.8474 | 0.9834 |
| 0.005 | 2.3310 | 2000 | 0.1075 | 0.8267 | 0.8645 | 0.8452 | 0.9830 |
| 0.0058 | 2.9138 | 2500 | 0.1067 | 0.8431 | 0.8673 | 0.8550 | 0.9838 |
| 0.0044 | 3.4965 | 3000 | 0.1132 | 0.8353 | 0.8605 | 0.8477 | 0.9835 |
| 0.0042 | 4.0793 | 3500 | 0.1059 | 0.8429 | 0.8689 | 0.8557 | 0.9842 |
| 0.0038 | 4.6620 | 4000 | 0.1047 | 0.8468 | 0.8671 | 0.8569 | 0.9845 |
| 0.0033 | 5.2448 | 4500 | 0.1195 | 0.8323 | 0.8557 | 0.8439 | 0.9832 |
| 0.0037 | 5.8275 | 5000 | 0.1231 | 0.8376 | 0.8618 | 0.8495 | 0.9831 |
| 0.0023 | 6.4103 | 5500 | 0.1207 | 0.8427 | 0.8639 | 0.8532 | 0.9839 |
| 0.0026 | 6.9930 | 6000 | 0.1162 | 0.8544 | 0.8566 | 0.8555 | 0.9841 |
| 0.0029 | 7.5758 | 6500 | 0.1149 | 0.8418 | 0.8691 | 0.8553 | 0.9841 |
| 0.0022 | 8.1585 | 7000 | 0.1342 | 0.8600 | 0.8543 | 0.8571 | 0.9842 |
| 0.0025 | 8.7413 | 7500 | 0.1159 | 0.8538 | 0.8566 | 0.8552 | 0.9842 |
| 0.0023 | 9.3240 | 8000 | 0.1253 | 0.8468 | 0.8512 | 0.8490 | 0.9837 |
| 0.0018 | 9.9068 | 8500 | 0.1288 | 0.8536 | 0.8546 | 0.8541 | 0.9839 |
| 0.0018 | 10.4895 | 9000 | 0.1243 | 0.8439 | 0.8523 | 0.8480 | 0.9837 |
| 0.0015 | 11.0723 | 9500 | 0.1278 | 0.8546 | 0.8540 | 0.8543 | 0.9839 |
| 0.0015 | 11.6550 | 10000 | 0.1337 | 0.8587 | 0.8564 | 0.8576 | 0.9840 |
| 0.0018 | 12.2378 | 10500 | 0.1324 | 0.8339 | 0.8562 | 0.8449 | 0.9836 |
| 0.0017 | 12.8205 | 11000 | 0.1284 | 0.845 | 0.8534 | 0.8492 | 0.9837 |
| 0.0013 | 13.4033 | 11500 | 0.1304 | 0.8581 | 0.8602 | 0.8591 | 0.9846 |
| 0.0015 | 13.9860 | 12000 | 0.1310 | 0.8335 | 0.8593 | 0.8462 | 0.9836 |
| 0.0013 | 14.5688 | 12500 | 0.1356 | 0.8422 | 0.8563 | 0.8492 | 0.9836 |
| 0.0009 | 15.1515 | 13000 | 0.1374 | 0.8559 | 0.8713 | 0.8635 | 0.9849 |
| 0.0013 | 15.7343 | 13500 | 0.1321 | 0.8399 | 0.8696 | 0.8545 | 0.9844 |
| 0.0009 | 16.3170 | 14000 | 0.1412 | 0.8431 | 0.8628 | 0.8528 | 0.9838 |
| 0.0011 | 16.8998 | 14500 | 0.1337 | 0.8527 | 0.8645 | 0.8586 | 0.9845 |
| 0.0006 | 17.4825 | 15000 | 0.1438 | 0.8620 | 0.8641 | 0.8630 | 0.9847 |
| 0.001 | 18.0653 | 15500 | 0.1346 | 0.8529 | 0.8683 | 0.8605 | 0.9847 |
| 0.0005 | 18.6480 | 16000 | 0.1356 | 0.8620 | 0.8667 | 0.8643 | 0.9850 |
| 0.0007 | 19.2308 | 16500 | 0.1426 | 0.8612 | 0.8622 | 0.8617 | 0.9845 |
| 0.0005 | 19.8135 | 17000 | 0.1415 | 0.8543 | 0.8588 | 0.8565 | 0.9844 |
| 0.0007 | 20.3963 | 17500 | 0.1385 | 0.8590 | 0.8589 | 0.8590 | 0.9844 |
| 0.0007 | 20.9790 | 18000 | 0.1405 | 0.8439 | 0.8696 | 0.8565 | 0.9840 |
| 0.0006 | 21.5618 | 18500 | 0.1414 | 0.8579 | 0.8648 | 0.8613 | 0.9846 |
| 0.0005 | 22.1445 | 19000 | 0.1386 | 0.8579 | 0.8612 | 0.8595 | 0.9845 |
| 0.0003 | 22.7273 | 19500 | 0.1447 | 0.8466 | 0.8735 | 0.8598 | 0.9843 |
| 0.0004 | 23.3100 | 20000 | 0.1413 | 0.8529 | 0.8626 | 0.8578 | 0.9845 |
| 0.0003 | 23.8928 | 20500 | 0.1404 | 0.8580 | 0.8655 | 0.8617 | 0.9850 |
| 0.0003 | 24.4755 | 21000 | 0.1466 | 0.8549 | 0.8681 | 0.8615 | 0.9847 |
| 0.0002 | 25.0583 | 21500 | 0.1467 | 0.8549 | 0.8645 | 0.8597 | 0.9846 |
| 0.0001 | 25.6410 | 22000 | 0.1495 | 0.8497 | 0.8746 | 0.8620 | 0.9847 |
| 0.0002 | 26.2238 | 22500 | 0.1479 | 0.8526 | 0.8701 | 0.8613 | 0.9848 |
| 0.0001 | 26.8065 | 23000 | 0.1485 | 0.8535 | 0.8655 | 0.8595 | 0.9846 |
| 0.0002 | 27.3893 | 23500 | 0.1482 | 0.8535 | 0.8691 | 0.8612 | 0.9847 |
| 0.0001 | 27.9720 | 24000 | 0.1501 | 0.8502 | 0.8683 | 0.8592 | 0.9845 |
| 0.0001 | 28.5548 | 24500 | 0.1490 | 0.8521 | 0.8670 | 0.8595 | 0.9847 |
| 0.0001 | 29.1375 | 25000 | 0.1506 | 0.8503 | 0.8713 | 0.8607 | 0.9847 |
| 0.0001 | 29.7203 | 25500 | 0.1503 | 0.8528 | 0.8701 | 0.8614 | 0.9848 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1
|