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---
base_model: haryoaw/scenario-TCR-NER_data-univner_full
library_name: transformers
license: mit
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: scenario-non-kd-scr-ner-half_data-univner_full44
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-scr-ner-half_data-univner_full44
This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_full](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_full) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1359
- Precision: 0.8579
- Recall: 0.8661
- F1: 0.8620
- 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: 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 |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0095 | 0.2910 | 500 | 0.0842 | 0.8414 | 0.8590 | 0.8501 | 0.9842 |
| 0.0115 | 0.5821 | 1000 | 0.0851 | 0.8363 | 0.8626 | 0.8493 | 0.9836 |
| 0.0108 | 0.8731 | 1500 | 0.0812 | 0.8414 | 0.8664 | 0.8537 | 0.9844 |
| 0.0086 | 1.1641 | 2000 | 0.0967 | 0.8277 | 0.8729 | 0.8497 | 0.9829 |
| 0.0083 | 1.4552 | 2500 | 0.0794 | 0.8469 | 0.8652 | 0.8560 | 0.9841 |
| 0.0082 | 1.7462 | 3000 | 0.0808 | 0.8391 | 0.8712 | 0.8548 | 0.9843 |
| 0.0089 | 2.0373 | 3500 | 0.0862 | 0.8488 | 0.8645 | 0.8566 | 0.9846 |
| 0.0063 | 2.3283 | 4000 | 0.0889 | 0.8455 | 0.8735 | 0.8593 | 0.9847 |
| 0.0068 | 2.6193 | 4500 | 0.0900 | 0.8463 | 0.8638 | 0.8550 | 0.9842 |
| 0.0071 | 2.9104 | 5000 | 0.0883 | 0.8362 | 0.8691 | 0.8524 | 0.9837 |
| 0.0055 | 3.2014 | 5500 | 0.0945 | 0.8433 | 0.8588 | 0.8510 | 0.9838 |
| 0.0055 | 3.4924 | 6000 | 0.0951 | 0.8428 | 0.8687 | 0.8556 | 0.9840 |
| 0.0059 | 3.7835 | 6500 | 0.0975 | 0.8563 | 0.8551 | 0.8557 | 0.9842 |
| 0.0054 | 4.0745 | 7000 | 0.1009 | 0.8422 | 0.8673 | 0.8546 | 0.9841 |
| 0.0047 | 4.3655 | 7500 | 0.0967 | 0.8556 | 0.8589 | 0.8572 | 0.9848 |
| 0.0048 | 4.6566 | 8000 | 0.0989 | 0.8506 | 0.8694 | 0.8599 | 0.9847 |
| 0.0048 | 4.9476 | 8500 | 0.0959 | 0.8526 | 0.8713 | 0.8619 | 0.9844 |
| 0.0036 | 5.2386 | 9000 | 0.1014 | 0.8476 | 0.8706 | 0.8589 | 0.9841 |
| 0.0042 | 5.5297 | 9500 | 0.1147 | 0.8068 | 0.8613 | 0.8332 | 0.9814 |
| 0.0048 | 5.8207 | 10000 | 0.1065 | 0.8392 | 0.8691 | 0.8539 | 0.9844 |
| 0.0041 | 6.1118 | 10500 | 0.1076 | 0.8417 | 0.8758 | 0.8584 | 0.9843 |
| 0.0035 | 6.4028 | 11000 | 0.1029 | 0.8505 | 0.8732 | 0.8617 | 0.9848 |
| 0.0036 | 6.6938 | 11500 | 0.0929 | 0.8460 | 0.8719 | 0.8587 | 0.9849 |
| 0.0038 | 6.9849 | 12000 | 0.1019 | 0.8494 | 0.8631 | 0.8562 | 0.9846 |
| 0.0031 | 7.2759 | 12500 | 0.1073 | 0.8563 | 0.8575 | 0.8569 | 0.9845 |
| 0.0031 | 7.5669 | 13000 | 0.1013 | 0.8431 | 0.8696 | 0.8561 | 0.9847 |
| 0.0034 | 7.8580 | 13500 | 0.1058 | 0.8533 | 0.8596 | 0.8565 | 0.9845 |
| 0.0027 | 8.1490 | 14000 | 0.1154 | 0.8431 | 0.8719 | 0.8572 | 0.9845 |
| 0.0027 | 8.4400 | 14500 | 0.1030 | 0.8404 | 0.8785 | 0.8591 | 0.9845 |
| 0.0028 | 8.7311 | 15000 | 0.1132 | 0.8559 | 0.8510 | 0.8534 | 0.9846 |
| 0.003 | 9.0221 | 15500 | 0.1106 | 0.8514 | 0.8648 | 0.8581 | 0.9848 |
| 0.0022 | 9.3132 | 16000 | 0.1136 | 0.8586 | 0.8657 | 0.8621 | 0.9852 |
| 0.0025 | 9.6042 | 16500 | 0.1128 | 0.8494 | 0.8697 | 0.8594 | 0.9848 |
| 0.002 | 9.8952 | 17000 | 0.1139 | 0.8453 | 0.8600 | 0.8526 | 0.9841 |
| 0.0024 | 10.1863 | 17500 | 0.1124 | 0.8541 | 0.8658 | 0.8599 | 0.9849 |
| 0.002 | 10.4773 | 18000 | 0.1154 | 0.8368 | 0.8663 | 0.8513 | 0.9842 |
| 0.0019 | 10.7683 | 18500 | 0.1182 | 0.8457 | 0.8629 | 0.8542 | 0.9844 |
| 0.0023 | 11.0594 | 19000 | 0.1140 | 0.8531 | 0.8596 | 0.8563 | 0.9846 |
| 0.0018 | 11.3504 | 19500 | 0.1194 | 0.8526 | 0.8683 | 0.8604 | 0.9851 |
| 0.0016 | 11.6414 | 20000 | 0.1198 | 0.8527 | 0.8652 | 0.8589 | 0.9848 |
| 0.0021 | 11.9325 | 20500 | 0.1169 | 0.8592 | 0.8654 | 0.8623 | 0.9852 |
| 0.0016 | 12.2235 | 21000 | 0.1229 | 0.8605 | 0.8626 | 0.8616 | 0.9848 |
| 0.0021 | 12.5146 | 21500 | 0.1198 | 0.8484 | 0.8697 | 0.8589 | 0.9846 |
| 0.0019 | 12.8056 | 22000 | 0.1177 | 0.8535 | 0.8600 | 0.8568 | 0.9844 |
| 0.0013 | 13.0966 | 22500 | 0.1190 | 0.8436 | 0.8716 | 0.8574 | 0.9844 |
| 0.0016 | 13.3877 | 23000 | 0.1227 | 0.8475 | 0.8665 | 0.8569 | 0.9847 |
| 0.0012 | 13.6787 | 23500 | 0.1237 | 0.8513 | 0.8676 | 0.8594 | 0.9848 |
| 0.0015 | 13.9697 | 24000 | 0.1198 | 0.8407 | 0.8709 | 0.8555 | 0.9843 |
| 0.0012 | 14.2608 | 24500 | 0.1239 | 0.8516 | 0.8689 | 0.8602 | 0.9850 |
| 0.0014 | 14.5518 | 25000 | 0.1261 | 0.8432 | 0.8634 | 0.8532 | 0.9843 |
| 0.0015 | 14.8428 | 25500 | 0.1220 | 0.8451 | 0.8716 | 0.8582 | 0.9849 |
| 0.0013 | 15.1339 | 26000 | 0.1209 | 0.8608 | 0.8598 | 0.8603 | 0.9847 |
| 0.0011 | 15.4249 | 26500 | 0.1261 | 0.8457 | 0.8637 | 0.8546 | 0.9847 |
| 0.0011 | 15.7159 | 27000 | 0.1273 | 0.8510 | 0.8616 | 0.8563 | 0.9846 |
| 0.0022 | 16.0070 | 27500 | 0.1282 | 0.8431 | 0.8738 | 0.8582 | 0.9847 |
| 0.001 | 16.2980 | 28000 | 0.1357 | 0.8451 | 0.8628 | 0.8539 | 0.9842 |
| 0.0013 | 16.5891 | 28500 | 0.1301 | 0.8465 | 0.8658 | 0.8561 | 0.9843 |
| 0.0008 | 16.8801 | 29000 | 0.1335 | 0.8533 | 0.8678 | 0.8605 | 0.9845 |
| 0.0011 | 17.1711 | 29500 | 0.1338 | 0.8572 | 0.8654 | 0.8613 | 0.9846 |
| 0.0006 | 17.4622 | 30000 | 0.1368 | 0.8561 | 0.8628 | 0.8594 | 0.9847 |
| 0.0008 | 17.7532 | 30500 | 0.1359 | 0.8579 | 0.8661 | 0.8620 | 0.9848 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1
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