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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-pre-ner-full-mdeberta_data-univner_half66
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-pre-ner-full-mdeberta_data-univner_half66
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: 0.1403
- Precision: 0.8613
- Recall: 0.8638
- F1: 0.8626
- 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: 66
- 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 |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0064 | 0.5828 | 500 | 0.0946 | 0.8529 | 0.8691 | 0.8609 | 0.9847 |
| 0.0076 | 1.1655 | 1000 | 0.0939 | 0.8503 | 0.8562 | 0.8532 | 0.9842 |
| 0.0059 | 1.7483 | 1500 | 0.0980 | 0.8583 | 0.8515 | 0.8549 | 0.9840 |
| 0.005 | 2.3310 | 2000 | 0.1052 | 0.8469 | 0.8618 | 0.8543 | 0.9840 |
| 0.0054 | 2.9138 | 2500 | 0.1025 | 0.8389 | 0.8699 | 0.8541 | 0.9841 |
| 0.0045 | 3.4965 | 3000 | 0.1032 | 0.8371 | 0.8696 | 0.8530 | 0.9836 |
| 0.0042 | 4.0793 | 3500 | 0.1088 | 0.8459 | 0.8642 | 0.8550 | 0.9840 |
| 0.0032 | 4.6620 | 4000 | 0.1182 | 0.8301 | 0.8691 | 0.8492 | 0.9828 |
| 0.0035 | 5.2448 | 4500 | 0.1164 | 0.8486 | 0.8611 | 0.8548 | 0.9841 |
| 0.0031 | 5.8275 | 5000 | 0.1190 | 0.8352 | 0.8602 | 0.8475 | 0.9836 |
| 0.0029 | 6.4103 | 5500 | 0.1197 | 0.8516 | 0.8694 | 0.8604 | 0.9843 |
| 0.0029 | 6.9930 | 6000 | 0.1177 | 0.8282 | 0.8674 | 0.8474 | 0.9833 |
| 0.0024 | 7.5758 | 6500 | 0.1219 | 0.8396 | 0.8680 | 0.8536 | 0.9845 |
| 0.0031 | 8.1585 | 7000 | 0.1160 | 0.8566 | 0.8559 | 0.8562 | 0.9846 |
| 0.002 | 8.7413 | 7500 | 0.1222 | 0.8385 | 0.8624 | 0.8503 | 0.9834 |
| 0.0021 | 9.3240 | 8000 | 0.1217 | 0.8522 | 0.8667 | 0.8594 | 0.9847 |
| 0.0019 | 9.9068 | 8500 | 0.1333 | 0.8222 | 0.8699 | 0.8453 | 0.9835 |
| 0.002 | 10.4895 | 9000 | 0.1210 | 0.8475 | 0.8665 | 0.8569 | 0.9845 |
| 0.0017 | 11.0723 | 9500 | 0.1192 | 0.8571 | 0.8642 | 0.8606 | 0.9849 |
| 0.0013 | 11.6550 | 10000 | 0.1329 | 0.8524 | 0.8716 | 0.8619 | 0.9848 |
| 0.0016 | 12.2378 | 10500 | 0.1337 | 0.8493 | 0.8700 | 0.8595 | 0.9844 |
| 0.0014 | 12.8205 | 11000 | 0.1245 | 0.8635 | 0.8707 | 0.8671 | 0.9854 |
| 0.0014 | 13.4033 | 11500 | 0.1299 | 0.8611 | 0.8595 | 0.8603 | 0.9849 |
| 0.0012 | 13.9860 | 12000 | 0.1229 | 0.8545 | 0.8657 | 0.8600 | 0.9848 |
| 0.0011 | 14.5688 | 12500 | 0.1258 | 0.8585 | 0.8631 | 0.8608 | 0.9849 |
| 0.0008 | 15.1515 | 13000 | 0.1377 | 0.8558 | 0.8658 | 0.8608 | 0.9847 |
| 0.001 | 15.7343 | 13500 | 0.1328 | 0.8576 | 0.8611 | 0.8593 | 0.9846 |
| 0.0008 | 16.3170 | 14000 | 0.1331 | 0.8596 | 0.8660 | 0.8628 | 0.9850 |
| 0.0008 | 16.8998 | 14500 | 0.1292 | 0.8549 | 0.8694 | 0.8621 | 0.9849 |
| 0.0008 | 17.4825 | 15000 | 0.1388 | 0.8496 | 0.8699 | 0.8596 | 0.9846 |
| 0.0008 | 18.0653 | 15500 | 0.1364 | 0.8577 | 0.8629 | 0.8603 | 0.9848 |
| 0.0005 | 18.6480 | 16000 | 0.1419 | 0.8627 | 0.8645 | 0.8636 | 0.9848 |
| 0.0007 | 19.2308 | 16500 | 0.1414 | 0.8569 | 0.8709 | 0.8638 | 0.9850 |
| 0.0005 | 19.8135 | 17000 | 0.1369 | 0.8513 | 0.8700 | 0.8606 | 0.9848 |
| 0.0004 | 20.3963 | 17500 | 0.1419 | 0.8580 | 0.8658 | 0.8619 | 0.9849 |
| 0.0004 | 20.9790 | 18000 | 0.1452 | 0.8598 | 0.8700 | 0.8649 | 0.9849 |
| 0.0005 | 21.5618 | 18500 | 0.1417 | 0.8540 | 0.8673 | 0.8606 | 0.9842 |
| 0.0003 | 22.1445 | 19000 | 0.1419 | 0.8667 | 0.8611 | 0.8639 | 0.9848 |
| 0.0003 | 22.7273 | 19500 | 0.1500 | 0.8588 | 0.8632 | 0.8610 | 0.9845 |
| 0.0004 | 23.3100 | 20000 | 0.1470 | 0.8557 | 0.8717 | 0.8636 | 0.9846 |
| 0.0004 | 23.8928 | 20500 | 0.1387 | 0.8652 | 0.8671 | 0.8662 | 0.9852 |
| 0.0002 | 24.4755 | 21000 | 0.1403 | 0.8613 | 0.8638 | 0.8626 | 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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