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---
library_name: transformers
license: mit
base_model: haryoaw/scenario-TCR-NER_data-univner_en
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: scenario-kd-po-ner-full_data-univner_full66
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-kd-po-ner-full_data-univner_full66
This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_en](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5267
- Precision: 0.7744
- Recall: 0.7391
- F1: 0.7564
- Accuracy: 0.9807
## 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 |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.8089 | 1.2755 | 500 | 0.7185 | 0.7338 | 0.6791 | 0.7054 | 0.9767 |
| 0.4626 | 2.5510 | 1000 | 0.6447 | 0.7127 | 0.7319 | 0.7222 | 0.9787 |
| 0.3791 | 3.8265 | 1500 | 0.5975 | 0.7349 | 0.7288 | 0.7318 | 0.9794 |
| 0.3262 | 5.1020 | 2000 | 0.5889 | 0.7447 | 0.7277 | 0.7361 | 0.9797 |
| 0.2868 | 6.3776 | 2500 | 0.5714 | 0.7427 | 0.7381 | 0.7404 | 0.9799 |
| 0.2587 | 7.6531 | 3000 | 0.5688 | 0.7703 | 0.7257 | 0.7473 | 0.9807 |
| 0.2389 | 8.9286 | 3500 | 0.5610 | 0.7338 | 0.7246 | 0.7292 | 0.9791 |
| 0.2211 | 10.2041 | 4000 | 0.5571 | 0.7719 | 0.7495 | 0.7605 | 0.9800 |
| 0.2022 | 11.4796 | 4500 | 0.5692 | 0.776 | 0.7029 | 0.7376 | 0.9799 |
| 0.1903 | 12.7551 | 5000 | 0.5554 | 0.7711 | 0.7360 | 0.7532 | 0.9804 |
| 0.179 | 14.0306 | 5500 | 0.5411 | 0.7574 | 0.7371 | 0.7471 | 0.9803 |
| 0.1688 | 15.3061 | 6000 | 0.5353 | 0.7602 | 0.7516 | 0.7559 | 0.9804 |
| 0.1608 | 16.5816 | 6500 | 0.5383 | 0.7748 | 0.7267 | 0.75 | 0.9802 |
| 0.1552 | 17.8571 | 7000 | 0.5223 | 0.7716 | 0.7381 | 0.7545 | 0.9800 |
| 0.1489 | 19.1327 | 7500 | 0.5300 | 0.7721 | 0.7329 | 0.7520 | 0.9801 |
| 0.1439 | 20.4082 | 8000 | 0.5321 | 0.7634 | 0.7246 | 0.7435 | 0.9797 |
| 0.1391 | 21.6837 | 8500 | 0.5204 | 0.7798 | 0.7443 | 0.7617 | 0.9805 |
| 0.1351 | 22.9592 | 9000 | 0.5251 | 0.7489 | 0.7350 | 0.7419 | 0.9800 |
| 0.131 | 24.2347 | 9500 | 0.5164 | 0.7664 | 0.7505 | 0.7584 | 0.9808 |
| 0.1291 | 25.5102 | 10000 | 0.5216 | 0.7614 | 0.7236 | 0.7420 | 0.9798 |
| 0.1276 | 26.7857 | 10500 | 0.5257 | 0.7739 | 0.7371 | 0.7550 | 0.9804 |
| 0.1251 | 28.0612 | 11000 | 0.5156 | 0.7692 | 0.7453 | 0.7571 | 0.9808 |
| 0.1241 | 29.3367 | 11500 | 0.5267 | 0.7744 | 0.7391 | 0.7564 | 0.9807 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1
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