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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-kd-scr-ner-full_data-univner_full55
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-scr-ner-full_data-univner_full55
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: 1.6332
- Precision: 0.4469
- Recall: 0.3758
- F1: 0.4083
- Accuracy: 0.9390
## 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 |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 2.9172 | 0.5828 | 500 | 2.8507 | 0.2157 | 0.0452 | 0.0747 | 0.9231 |
| 2.2157 | 1.1655 | 1000 | 2.5207 | 0.2360 | 0.1134 | 0.1532 | 0.9230 |
| 1.9564 | 1.7483 | 1500 | 2.5233 | 0.1706 | 0.1749 | 0.1727 | 0.9128 |
| 1.7543 | 2.3310 | 2000 | 2.4414 | 0.2175 | 0.2244 | 0.2209 | 0.9157 |
| 1.6286 | 2.9138 | 2500 | 2.2528 | 0.2500 | 0.2336 | 0.2415 | 0.9223 |
| 1.4766 | 3.4965 | 3000 | 2.0896 | 0.2944 | 0.2241 | 0.2545 | 0.9279 |
| 1.398 | 4.0793 | 3500 | 2.0471 | 0.3335 | 0.2441 | 0.2819 | 0.9303 |
| 1.2907 | 4.6620 | 4000 | 1.9739 | 0.2985 | 0.2568 | 0.2761 | 0.9294 |
| 1.2065 | 5.2448 | 4500 | 1.8564 | 0.3685 | 0.2424 | 0.2924 | 0.9344 |
| 1.1392 | 5.8275 | 5000 | 2.1380 | 0.2515 | 0.3037 | 0.2751 | 0.9172 |
| 1.0459 | 6.4103 | 5500 | 1.9090 | 0.3426 | 0.2819 | 0.3093 | 0.9320 |
| 0.9973 | 6.9930 | 6000 | 1.8167 | 0.3556 | 0.3015 | 0.3263 | 0.9350 |
| 0.9106 | 7.5758 | 6500 | 1.8701 | 0.3736 | 0.2884 | 0.3255 | 0.9326 |
| 0.8843 | 8.1585 | 7000 | 1.8193 | 0.3618 | 0.3219 | 0.3407 | 0.9345 |
| 0.8329 | 8.7413 | 7500 | 1.8722 | 0.3634 | 0.3378 | 0.3501 | 0.9305 |
| 0.784 | 9.3240 | 8000 | 1.7434 | 0.4139 | 0.3140 | 0.3571 | 0.9381 |
| 0.7606 | 9.9068 | 8500 | 1.7787 | 0.4143 | 0.3147 | 0.3577 | 0.9363 |
| 0.7111 | 10.4895 | 9000 | 1.8461 | 0.3518 | 0.3292 | 0.3401 | 0.9315 |
| 0.6894 | 11.0723 | 9500 | 1.7537 | 0.3635 | 0.3327 | 0.3474 | 0.9351 |
| 0.6543 | 11.6550 | 10000 | 1.7565 | 0.3779 | 0.3506 | 0.3637 | 0.9347 |
| 0.6429 | 12.2378 | 10500 | 1.8134 | 0.3769 | 0.3496 | 0.3627 | 0.9323 |
| 0.6084 | 12.8205 | 11000 | 1.8020 | 0.3757 | 0.3740 | 0.3748 | 0.9320 |
| 0.5799 | 13.4033 | 11500 | 1.7080 | 0.4119 | 0.3447 | 0.3753 | 0.9374 |
| 0.5742 | 13.9860 | 12000 | 1.7454 | 0.3963 | 0.3668 | 0.3809 | 0.9356 |
| 0.5467 | 14.5688 | 12500 | 1.8019 | 0.3832 | 0.3748 | 0.3790 | 0.9322 |
| 0.5327 | 15.1515 | 13000 | 1.8784 | 0.3599 | 0.3774 | 0.3685 | 0.9275 |
| 0.5207 | 15.7343 | 13500 | 1.7905 | 0.3977 | 0.3760 | 0.3865 | 0.9336 |
| 0.5047 | 16.3170 | 14000 | 1.6909 | 0.4336 | 0.3606 | 0.3937 | 0.9377 |
| 0.4911 | 16.8998 | 14500 | 1.7464 | 0.3951 | 0.3780 | 0.3864 | 0.9342 |
| 0.4802 | 17.4825 | 15000 | 1.7247 | 0.4230 | 0.3738 | 0.3969 | 0.9365 |
| 0.4729 | 18.0653 | 15500 | 1.6929 | 0.4307 | 0.3639 | 0.3945 | 0.9379 |
| 0.4607 | 18.6480 | 16000 | 1.6395 | 0.4493 | 0.3503 | 0.3937 | 0.9404 |
| 0.449 | 19.2308 | 16500 | 1.7051 | 0.4149 | 0.3766 | 0.3948 | 0.9362 |
| 0.4402 | 19.8135 | 17000 | 1.7664 | 0.4024 | 0.3779 | 0.3898 | 0.9318 |
| 0.4337 | 20.3963 | 17500 | 1.6884 | 0.4475 | 0.3689 | 0.4044 | 0.9386 |
| 0.4272 | 20.9790 | 18000 | 1.6995 | 0.4209 | 0.3841 | 0.4017 | 0.9360 |
| 0.4162 | 21.5618 | 18500 | 1.6522 | 0.4428 | 0.3668 | 0.4012 | 0.9387 |
| 0.4114 | 22.1445 | 19000 | 1.6957 | 0.4082 | 0.3797 | 0.3935 | 0.9356 |
| 0.4087 | 22.7273 | 19500 | 1.6728 | 0.4323 | 0.3656 | 0.3962 | 0.9377 |
| 0.4008 | 23.3100 | 20000 | 1.6749 | 0.4287 | 0.3598 | 0.3913 | 0.9368 |
| 0.394 | 23.8928 | 20500 | 1.6745 | 0.4266 | 0.3640 | 0.3928 | 0.9373 |
| 0.3887 | 24.4755 | 21000 | 1.6553 | 0.4358 | 0.3666 | 0.3982 | 0.9386 |
| 0.3876 | 25.0583 | 21500 | 1.6904 | 0.4190 | 0.3841 | 0.4008 | 0.9363 |
| 0.3819 | 25.6410 | 22000 | 1.6581 | 0.4360 | 0.3761 | 0.4039 | 0.9372 |
| 0.3776 | 26.2238 | 22500 | 1.6192 | 0.4595 | 0.3620 | 0.4050 | 0.9401 |
| 0.3767 | 26.8065 | 23000 | 1.6383 | 0.4453 | 0.3796 | 0.4098 | 0.9386 |
| 0.3738 | 27.3893 | 23500 | 1.6327 | 0.4517 | 0.3745 | 0.4095 | 0.9396 |
| 0.3671 | 27.9720 | 24000 | 1.6605 | 0.4399 | 0.3763 | 0.4056 | 0.9378 |
| 0.3694 | 28.5548 | 24500 | 1.6160 | 0.4554 | 0.3744 | 0.4110 | 0.9402 |
| 0.3659 | 29.1375 | 25000 | 1.6376 | 0.4419 | 0.3734 | 0.4048 | 0.9383 |
| 0.3637 | 29.7203 | 25500 | 1.6332 | 0.4469 | 0.3758 | 0.4083 | 0.9390 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1
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