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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-kd-scr-ner-half-xlmr_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-scr-ner-half-xlmr_data-univner_full66
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: 121.6095
- Precision: 0.4270
- Recall: 0.3784
- F1: 0.4013
- Accuracy: 0.9476
## 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: 8
- eval_batch_size: 32
- seed: 66
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 259.2613 | 0.2911 | 500 | 189.6813 | 0.0 | 0.0 | 0.0 | 0.9241 |
| 179.4566 | 0.5822 | 1000 | 173.2544 | 0.4545 | 0.0137 | 0.0266 | 0.9246 |
| 167.777 | 0.8732 | 1500 | 164.4018 | 0.3561 | 0.0361 | 0.0655 | 0.9256 |
| 160.7257 | 1.1643 | 2000 | 159.5333 | 0.0954 | 0.0042 | 0.0080 | 0.9243 |
| 155.3029 | 1.4554 | 2500 | 156.0974 | 0.2908 | 0.0283 | 0.0515 | 0.9254 |
| 152.3202 | 1.7465 | 3000 | 151.0953 | 0.2614 | 0.0521 | 0.0869 | 0.9266 |
| 148.4375 | 2.0375 | 3500 | 149.1951 | 0.4117 | 0.0326 | 0.0604 | 0.9256 |
| 144.8759 | 2.3286 | 4000 | 144.9364 | 0.2888 | 0.1091 | 0.1583 | 0.9284 |
| 141.9512 | 2.6197 | 4500 | 142.7550 | 0.3105 | 0.1097 | 0.1621 | 0.9288 |
| 139.8162 | 2.9108 | 5000 | 140.1311 | 0.3438 | 0.0981 | 0.1527 | 0.9289 |
| 136.3472 | 3.2019 | 5500 | 138.3547 | 0.2671 | 0.1955 | 0.2258 | 0.9327 |
| 134.5775 | 3.4929 | 6000 | 135.6351 | 0.2823 | 0.1747 | 0.2158 | 0.9340 |
| 132.1038 | 3.7840 | 6500 | 134.0777 | 0.2685 | 0.1773 | 0.2136 | 0.9349 |
| 130.6851 | 4.0751 | 7000 | 132.9280 | 0.2858 | 0.1840 | 0.2238 | 0.9359 |
| 128.5001 | 4.3662 | 7500 | 131.8978 | 0.3058 | 0.2001 | 0.2419 | 0.9369 |
| 127.3796 | 4.6573 | 8000 | 130.3655 | 0.3250 | 0.2151 | 0.2589 | 0.9378 |
| 126.5618 | 4.9483 | 8500 | 129.1083 | 0.3273 | 0.2332 | 0.2723 | 0.9383 |
| 125.2975 | 5.2394 | 9000 | 128.4492 | 0.3147 | 0.2560 | 0.2823 | 0.9396 |
| 123.5341 | 5.5305 | 9500 | 127.2300 | 0.3418 | 0.2580 | 0.2940 | 0.9405 |
| 122.698 | 5.8216 | 10000 | 126.8739 | 0.3390 | 0.2811 | 0.3073 | 0.9402 |
| 121.6237 | 6.1126 | 10500 | 125.7438 | 0.3739 | 0.3011 | 0.3336 | 0.9434 |
| 120.6456 | 6.4037 | 11000 | 125.2620 | 0.3606 | 0.3011 | 0.3282 | 0.9430 |
| 120.2335 | 6.6948 | 11500 | 124.5899 | 0.3759 | 0.3466 | 0.3606 | 0.9447 |
| 119.8109 | 6.9859 | 12000 | 123.9922 | 0.3920 | 0.3213 | 0.3532 | 0.9442 |
| 118.4398 | 7.2770 | 12500 | 123.5926 | 0.3971 | 0.3497 | 0.3719 | 0.9455 |
| 117.945 | 7.5680 | 13000 | 123.2072 | 0.4014 | 0.3308 | 0.3627 | 0.9453 |
| 117.9631 | 7.8591 | 13500 | 122.8442 | 0.4017 | 0.3556 | 0.3773 | 0.9458 |
| 117.3963 | 8.1502 | 14000 | 122.6162 | 0.3940 | 0.3769 | 0.3852 | 0.9464 |
| 116.5054 | 8.4413 | 14500 | 122.1343 | 0.4079 | 0.3718 | 0.3890 | 0.9471 |
| 116.5259 | 8.7324 | 15000 | 121.9603 | 0.4158 | 0.3528 | 0.3817 | 0.9470 |
| 116.4213 | 9.0234 | 15500 | 121.8525 | 0.4173 | 0.3718 | 0.3933 | 0.9470 |
| 115.7738 | 9.3145 | 16000 | 121.6751 | 0.4247 | 0.3767 | 0.3993 | 0.9476 |
| 115.8023 | 9.6056 | 16500 | 121.5823 | 0.4306 | 0.3855 | 0.4068 | 0.9479 |
| 115.8227 | 9.8967 | 17000 | 121.6095 | 0.4270 | 0.3784 | 0.4013 | 0.9476 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1
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