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---
base_model: haryoaw/scenario-TCR-NER_data-univner_half
library_name: transformers
license: mit
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: scenario-non-kd-po-ner-full-mdeberta_data-univner_half44
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_half44
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: 0.1473
- Precision: 0.8609
- Recall: 0.8706
- F1: 0.8657
- Accuracy: 0.9852
## 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: 44
- 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 |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0061 | 0.5828 | 500 | 0.0903 | 0.8587 | 0.8507 | 0.8547 | 0.9842 |
| 0.0071 | 1.1655 | 1000 | 0.1028 | 0.8407 | 0.8732 | 0.8566 | 0.9845 |
| 0.0061 | 1.7483 | 1500 | 0.0990 | 0.8455 | 0.8495 | 0.8475 | 0.9833 |
| 0.0055 | 2.3310 | 2000 | 0.1022 | 0.8571 | 0.8517 | 0.8544 | 0.9842 |
| 0.0047 | 2.9138 | 2500 | 0.0968 | 0.8396 | 0.8668 | 0.8530 | 0.9840 |
| 0.0037 | 3.4965 | 3000 | 0.1138 | 0.8275 | 0.8707 | 0.8486 | 0.9832 |
| 0.0035 | 4.0793 | 3500 | 0.1145 | 0.8490 | 0.8588 | 0.8538 | 0.9837 |
| 0.0036 | 4.6620 | 4000 | 0.1066 | 0.8388 | 0.8687 | 0.8535 | 0.9840 |
| 0.0032 | 5.2448 | 4500 | 0.1206 | 0.8495 | 0.8575 | 0.8535 | 0.9840 |
| 0.0032 | 5.8275 | 5000 | 0.1192 | 0.8502 | 0.8564 | 0.8533 | 0.9840 |
| 0.0027 | 6.4103 | 5500 | 0.1153 | 0.8463 | 0.8683 | 0.8571 | 0.9844 |
| 0.0033 | 6.9930 | 6000 | 0.1199 | 0.8437 | 0.8722 | 0.8577 | 0.9843 |
| 0.003 | 7.5758 | 6500 | 0.1121 | 0.8533 | 0.8525 | 0.8529 | 0.9843 |
| 0.0023 | 8.1585 | 7000 | 0.1152 | 0.8508 | 0.8435 | 0.8471 | 0.9831 |
| 0.0028 | 8.7413 | 7500 | 0.1131 | 0.8368 | 0.8536 | 0.8451 | 0.9835 |
| 0.0026 | 9.3240 | 8000 | 0.1213 | 0.8415 | 0.8670 | 0.8540 | 0.9834 |
| 0.0021 | 9.9068 | 8500 | 0.1207 | 0.8313 | 0.8716 | 0.8510 | 0.9833 |
| 0.0015 | 10.4895 | 9000 | 0.1268 | 0.8566 | 0.8609 | 0.8587 | 0.9844 |
| 0.0016 | 11.0723 | 9500 | 0.1223 | 0.8525 | 0.8528 | 0.8527 | 0.9839 |
| 0.0021 | 11.6550 | 10000 | 0.1262 | 0.8402 | 0.8602 | 0.8501 | 0.9834 |
| 0.0012 | 12.2378 | 10500 | 0.1237 | 0.8575 | 0.8453 | 0.8514 | 0.9837 |
| 0.0014 | 12.8205 | 11000 | 0.1319 | 0.8537 | 0.8551 | 0.8544 | 0.9840 |
| 0.0013 | 13.4033 | 11500 | 0.1308 | 0.8316 | 0.8619 | 0.8465 | 0.9830 |
| 0.0014 | 13.9860 | 12000 | 0.1237 | 0.8615 | 0.8536 | 0.8575 | 0.9843 |
| 0.0008 | 14.5688 | 12500 | 0.1361 | 0.8436 | 0.8638 | 0.8536 | 0.9839 |
| 0.0013 | 15.1515 | 13000 | 0.1383 | 0.8466 | 0.8576 | 0.8521 | 0.9839 |
| 0.0011 | 15.7343 | 13500 | 0.1356 | 0.8595 | 0.8527 | 0.8561 | 0.9843 |
| 0.0007 | 16.3170 | 14000 | 0.1366 | 0.8533 | 0.8618 | 0.8575 | 0.9843 |
| 0.0008 | 16.8998 | 14500 | 0.1334 | 0.8608 | 0.8625 | 0.8616 | 0.9846 |
| 0.0007 | 17.4825 | 15000 | 0.1382 | 0.8549 | 0.8644 | 0.8596 | 0.9847 |
| 0.0007 | 18.0653 | 15500 | 0.1432 | 0.8682 | 0.8488 | 0.8584 | 0.9845 |
| 0.0006 | 18.6480 | 16000 | 0.1408 | 0.8551 | 0.8613 | 0.8582 | 0.9844 |
| 0.0009 | 19.2308 | 16500 | 0.1366 | 0.8551 | 0.8567 | 0.8559 | 0.9844 |
| 0.0007 | 19.8135 | 17000 | 0.1320 | 0.8518 | 0.8641 | 0.8579 | 0.9845 |
| 0.0005 | 20.3963 | 17500 | 0.1399 | 0.8506 | 0.8655 | 0.8580 | 0.9844 |
| 0.0005 | 20.9790 | 18000 | 0.1399 | 0.8510 | 0.8631 | 0.8570 | 0.9845 |
| 0.0005 | 21.5618 | 18500 | 0.1430 | 0.8633 | 0.8622 | 0.8628 | 0.9849 |
| 0.0005 | 22.1445 | 19000 | 0.1368 | 0.8549 | 0.8673 | 0.8611 | 0.9848 |
| 0.0004 | 22.7273 | 19500 | 0.1364 | 0.8548 | 0.8678 | 0.8613 | 0.9848 |
| 0.0005 | 23.3100 | 20000 | 0.1400 | 0.8616 | 0.8650 | 0.8633 | 0.9848 |
| 0.0003 | 23.8928 | 20500 | 0.1452 | 0.8458 | 0.8723 | 0.8589 | 0.9843 |
| 0.0002 | 24.4755 | 21000 | 0.1398 | 0.8552 | 0.8665 | 0.8608 | 0.9849 |
| 0.0003 | 25.0583 | 21500 | 0.1405 | 0.8578 | 0.8710 | 0.8643 | 0.9849 |
| 0.0002 | 25.6410 | 22000 | 0.1408 | 0.8601 | 0.8691 | 0.8646 | 0.9851 |
| 0.0002 | 26.2238 | 22500 | 0.1453 | 0.8578 | 0.8730 | 0.8654 | 0.9852 |
| 0.0002 | 26.8065 | 23000 | 0.1463 | 0.8574 | 0.8736 | 0.8654 | 0.9851 |
| 0.0002 | 27.3893 | 23500 | 0.1458 | 0.8637 | 0.8714 | 0.8676 | 0.9854 |
| 0.0001 | 27.9720 | 24000 | 0.1465 | 0.8565 | 0.8732 | 0.8648 | 0.9851 |
| 0.0001 | 28.5548 | 24500 | 0.1464 | 0.8602 | 0.8727 | 0.8664 | 0.9852 |
| 0.0002 | 29.1375 | 25000 | 0.1474 | 0.8598 | 0.8714 | 0.8656 | 0.9852 |
| 0.0001 | 29.7203 | 25500 | 0.1473 | 0.8609 | 0.8706 | 0.8657 | 0.9852 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1
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