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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-po-ner-full-mdeberta_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-mdeberta_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: 46.8943
- Precision: 0.8216
- Recall: 0.8305
- F1: 0.8260
- Accuracy: 0.9824
## 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 |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 135.9225 | 0.2911 | 500 | 108.8223 | 0.6029 | 0.4263 | 0.4995 | 0.9530 |
| 101.0224 | 0.5822 | 1000 | 94.7779 | 0.7231 | 0.7083 | 0.7156 | 0.9727 |
| 91.1928 | 0.8732 | 1500 | 88.1561 | 0.7488 | 0.7565 | 0.7526 | 0.9760 |
| 85.2395 | 1.1643 | 2000 | 83.2027 | 0.7757 | 0.7680 | 0.7718 | 0.9777 |
| 80.2907 | 1.4554 | 2500 | 79.1339 | 0.7824 | 0.7925 | 0.7874 | 0.9788 |
| 76.5231 | 1.7465 | 3000 | 75.6774 | 0.7942 | 0.7846 | 0.7894 | 0.9793 |
| 73.1464 | 2.0375 | 3500 | 72.6010 | 0.8048 | 0.7973 | 0.8010 | 0.9801 |
| 69.6437 | 2.3286 | 4000 | 69.6290 | 0.7955 | 0.8179 | 0.8066 | 0.9803 |
| 67.0793 | 2.6197 | 4500 | 67.2226 | 0.8016 | 0.8085 | 0.8051 | 0.9808 |
| 64.9103 | 2.9108 | 5000 | 65.1388 | 0.8012 | 0.8176 | 0.8093 | 0.9807 |
| 62.5177 | 3.2019 | 5500 | 63.1765 | 0.8105 | 0.8160 | 0.8133 | 0.9813 |
| 60.6079 | 3.4929 | 6000 | 61.4149 | 0.8158 | 0.8129 | 0.8143 | 0.9811 |
| 58.9252 | 3.7840 | 6500 | 59.9050 | 0.8118 | 0.8212 | 0.8165 | 0.9810 |
| 57.4544 | 4.0751 | 7000 | 58.3757 | 0.8063 | 0.8260 | 0.8161 | 0.9813 |
| 55.9212 | 4.3662 | 7500 | 57.1185 | 0.8129 | 0.8254 | 0.8191 | 0.9815 |
| 54.706 | 4.6573 | 8000 | 55.9905 | 0.8208 | 0.8197 | 0.8202 | 0.9818 |
| 53.5567 | 4.9483 | 8500 | 54.9749 | 0.8117 | 0.8259 | 0.8187 | 0.9813 |
| 52.4084 | 5.2394 | 9000 | 53.9236 | 0.8158 | 0.8228 | 0.8193 | 0.9815 |
| 51.3684 | 5.5305 | 9500 | 52.9420 | 0.8148 | 0.8263 | 0.8205 | 0.9817 |
| 50.5374 | 5.8216 | 10000 | 52.1205 | 0.8224 | 0.8209 | 0.8217 | 0.9819 |
| 49.7012 | 6.1126 | 10500 | 51.3587 | 0.8195 | 0.8310 | 0.8252 | 0.9820 |
| 48.8997 | 6.4037 | 11000 | 50.7199 | 0.8205 | 0.8270 | 0.8237 | 0.9819 |
| 48.3307 | 6.6948 | 11500 | 50.0936 | 0.8238 | 0.8215 | 0.8227 | 0.9821 |
| 47.765 | 6.9859 | 12000 | 49.5167 | 0.8177 | 0.8318 | 0.8247 | 0.9819 |
| 47.1176 | 7.2770 | 12500 | 49.0615 | 0.8195 | 0.8305 | 0.8249 | 0.9821 |
| 46.5727 | 7.5680 | 13000 | 48.6345 | 0.8176 | 0.8347 | 0.8260 | 0.9820 |
| 46.2968 | 7.8591 | 13500 | 48.2124 | 0.8193 | 0.8282 | 0.8237 | 0.9821 |
| 45.8193 | 8.1502 | 14000 | 47.8940 | 0.8236 | 0.8285 | 0.8260 | 0.9821 |
| 45.4871 | 8.4413 | 14500 | 47.5967 | 0.8171 | 0.8362 | 0.8266 | 0.9819 |
| 45.2671 | 8.7324 | 15000 | 47.3633 | 0.8252 | 0.8329 | 0.8290 | 0.9824 |
| 45.0471 | 9.0234 | 15500 | 47.1393 | 0.8245 | 0.8280 | 0.8262 | 0.9821 |
| 44.7971 | 9.3145 | 16000 | 47.0470 | 0.8234 | 0.8315 | 0.8274 | 0.9822 |
| 44.7601 | 9.6056 | 16500 | 46.9315 | 0.8223 | 0.8331 | 0.8276 | 0.9825 |
| 44.647 | 9.8967 | 17000 | 46.8943 | 0.8216 | 0.8305 | 0.8260 | 0.9824 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1
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