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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_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-po-ner-full-mdeberta_data-univner_full55
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: 46.9305
- Precision: 0.8196
- Recall: 0.8292
- F1: 0.8244
- Accuracy: 0.9823
## 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: 55
- 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 |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 135.8523 | 0.2911 | 500 | 108.5680 | 0.5906 | 0.3842 | 0.4656 | 0.9517 |
| 100.5734 | 0.5822 | 1000 | 94.6790 | 0.7351 | 0.6641 | 0.6978 | 0.9712 |
| 91.2154 | 0.8732 | 1500 | 88.0640 | 0.7607 | 0.7570 | 0.7588 | 0.9762 |
| 85.164 | 1.1643 | 2000 | 83.3722 | 0.8082 | 0.7302 | 0.7672 | 0.9767 |
| 80.2474 | 1.4554 | 2500 | 78.9752 | 0.7780 | 0.7925 | 0.7852 | 0.9791 |
| 76.4386 | 1.7465 | 3000 | 75.6330 | 0.8035 | 0.7945 | 0.7990 | 0.9801 |
| 73.0828 | 2.0375 | 3500 | 72.4945 | 0.7997 | 0.7970 | 0.7983 | 0.9804 |
| 69.7284 | 2.3286 | 4000 | 69.7705 | 0.7983 | 0.8048 | 0.8016 | 0.9804 |
| 67.0314 | 2.6197 | 4500 | 67.3742 | 0.8113 | 0.7970 | 0.8041 | 0.9805 |
| 64.9596 | 2.9108 | 5000 | 65.2223 | 0.8108 | 0.8025 | 0.8066 | 0.9805 |
| 62.6221 | 3.2019 | 5500 | 63.1795 | 0.8049 | 0.8169 | 0.8109 | 0.9810 |
| 60.6361 | 3.4929 | 6000 | 61.4200 | 0.8124 | 0.8186 | 0.8155 | 0.9814 |
| 58.8661 | 3.7840 | 6500 | 59.9772 | 0.8102 | 0.8192 | 0.8147 | 0.9815 |
| 57.5058 | 4.0751 | 7000 | 58.4410 | 0.8114 | 0.8168 | 0.8141 | 0.9811 |
| 55.9259 | 4.3662 | 7500 | 57.1486 | 0.8151 | 0.8179 | 0.8165 | 0.9814 |
| 54.6494 | 4.6573 | 8000 | 55.9362 | 0.8206 | 0.8155 | 0.8180 | 0.9814 |
| 53.5407 | 4.9483 | 8500 | 54.8810 | 0.8152 | 0.8205 | 0.8179 | 0.9816 |
| 52.3581 | 5.2394 | 9000 | 53.9021 | 0.8169 | 0.8266 | 0.8217 | 0.9816 |
| 51.3581 | 5.5305 | 9500 | 53.0325 | 0.8200 | 0.8204 | 0.8202 | 0.9816 |
| 50.5535 | 5.8216 | 10000 | 52.1425 | 0.8182 | 0.8282 | 0.8232 | 0.9818 |
| 49.8392 | 6.1126 | 10500 | 51.4247 | 0.8178 | 0.8254 | 0.8216 | 0.9817 |
| 48.9716 | 6.4037 | 11000 | 50.6978 | 0.8191 | 0.8338 | 0.8264 | 0.9823 |
| 48.3296 | 6.6948 | 11500 | 50.1578 | 0.8164 | 0.8290 | 0.8227 | 0.9818 |
| 47.712 | 6.9859 | 12000 | 49.5760 | 0.8234 | 0.8266 | 0.8250 | 0.9824 |
| 47.0545 | 7.2770 | 12500 | 49.0523 | 0.8227 | 0.8354 | 0.8290 | 0.9821 |
| 46.6326 | 7.5680 | 13000 | 48.6282 | 0.8174 | 0.8287 | 0.8230 | 0.9820 |
| 46.2306 | 7.8591 | 13500 | 48.2713 | 0.8208 | 0.8254 | 0.8231 | 0.9819 |
| 45.9118 | 8.1502 | 14000 | 47.9235 | 0.8185 | 0.8259 | 0.8222 | 0.9817 |
| 45.5272 | 8.4413 | 14500 | 47.6086 | 0.8241 | 0.8259 | 0.8250 | 0.9822 |
| 45.2228 | 8.7324 | 15000 | 47.3476 | 0.8250 | 0.8321 | 0.8285 | 0.9822 |
| 44.9978 | 9.0234 | 15500 | 47.1635 | 0.8204 | 0.8263 | 0.8233 | 0.9821 |
| 44.8309 | 9.3145 | 16000 | 47.0839 | 0.8264 | 0.8285 | 0.8274 | 0.9821 |
| 44.6998 | 9.6056 | 16500 | 46.9565 | 0.8228 | 0.8292 | 0.8260 | 0.9824 |
| 44.6759 | 9.8967 | 17000 | 46.9305 | 0.8196 | 0.8292 | 0.8244 | 0.9823 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1
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