---
license: mit
base_model: haryoaw/scenario-TCR-NER_data-univner_full
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: scenario-kd-po-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-po-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: 119.1294
- Precision: 0.4508
- Recall: 0.4147
- F1: 0.4320
- Accuracy: 0.9488

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 284.146       | 0.29  | 500   | 212.3445        | 0.0       | 0.0    | 0.0    | 0.9241   |
| 195.0927      | 0.58  | 1000  | 183.6382        | 0.1678    | 0.0104 | 0.0196 | 0.9248   |
| 175.6188      | 0.87  | 1500  | 169.2953        | 0.2481    | 0.0462 | 0.0778 | 0.9266   |
| 164.5343      | 1.16  | 2000  | 160.0481        | 0.25      | 0.0763 | 0.1169 | 0.9295   |
| 155.7977      | 1.46  | 2500  | 153.3116        | 0.2572    | 0.1597 | 0.1971 | 0.9334   |
| 150.8056      | 1.75  | 3000  | 147.9380        | 0.2919    | 0.1860 | 0.2272 | 0.9354   |
| 145.886       | 2.04  | 3500  | 144.0042        | 0.3123    | 0.2203 | 0.2584 | 0.9363   |
| 141.6606      | 2.33  | 4000  | 140.6623        | 0.3362    | 0.2148 | 0.2621 | 0.9378   |
| 138.9315      | 2.62  | 4500  | 137.7953        | 0.3395    | 0.2557 | 0.2917 | 0.9390   |
| 136.3904      | 2.91  | 5000  | 135.3107        | 0.3432    | 0.2940 | 0.3167 | 0.9407   |
| 133.4631      | 3.2   | 5500  | 133.3555        | 0.3602    | 0.3239 | 0.3411 | 0.9419   |
| 131.8994      | 3.49  | 6000  | 131.5568        | 0.3743    | 0.3372 | 0.3548 | 0.9421   |
| 130.0259      | 3.78  | 6500  | 130.1601        | 0.3590    | 0.3480 | 0.3534 | 0.9425   |
| 128.8084      | 4.08  | 7000  | 128.5461        | 0.3878    | 0.3197 | 0.3505 | 0.9431   |
| 126.9491      | 4.37  | 7500  | 127.3171        | 0.4126    | 0.3611 | 0.3852 | 0.9444   |
| 125.7889      | 4.66  | 8000  | 126.2976        | 0.3964    | 0.3598 | 0.3773 | 0.9449   |
| 125.1757      | 4.95  | 8500  | 125.3498        | 0.3985    | 0.3718 | 0.3847 | 0.9448   |
| 124.1594      | 5.24  | 9000  | 124.6697        | 0.4158    | 0.3617 | 0.3869 | 0.9457   |
| 122.9069      | 5.53  | 9500  | 123.8015        | 0.4246    | 0.3735 | 0.3975 | 0.9457   |
| 121.9428      | 5.82  | 10000 | 123.1179        | 0.4038    | 0.3846 | 0.3940 | 0.9461   |
| 121.4957      | 6.11  | 10500 | 122.5535        | 0.4256    | 0.3835 | 0.4035 | 0.9468   |
| 120.8021      | 6.4   | 11000 | 121.9703        | 0.4315    | 0.3887 | 0.4090 | 0.9467   |
| 120.6592      | 6.69  | 11500 | 121.5849        | 0.4290    | 0.4178 | 0.4233 | 0.9478   |
| 119.7714      | 6.99  | 12000 | 121.0852        | 0.4380    | 0.4115 | 0.4243 | 0.9479   |
| 119.6204      | 7.28  | 12500 | 120.7726        | 0.4477    | 0.3894 | 0.4165 | 0.9478   |
| 118.8015      | 7.57  | 13000 | 120.4320        | 0.4449    | 0.4093 | 0.4264 | 0.9480   |
| 118.8596      | 7.86  | 13500 | 120.1600        | 0.4454    | 0.4053 | 0.4244 | 0.9481   |
| 118.376       | 8.15  | 14000 | 119.9419        | 0.4406    | 0.4047 | 0.4219 | 0.9484   |
| 118.1562      | 8.44  | 14500 | 119.6902        | 0.4427    | 0.4099 | 0.4257 | 0.9483   |
| 117.8396      | 8.73  | 15000 | 119.5728        | 0.4410    | 0.4155 | 0.4279 | 0.9484   |
| 117.496       | 9.02  | 15500 | 119.4226        | 0.4450    | 0.4188 | 0.4315 | 0.9486   |
| 117.5852      | 9.31  | 16000 | 119.3681        | 0.4498    | 0.4096 | 0.4288 | 0.9486   |
| 117.6006      | 9.61  | 16500 | 119.2253        | 0.4467    | 0.4048 | 0.4247 | 0.9487   |
| 117.3602      | 9.9   | 17000 | 119.1294        | 0.4508    | 0.4147 | 0.4320 | 0.9488   |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3