---
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-non-kd-scr-ner-half_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-non-kd-scr-ner-half_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: 0.1084
- Precision: 0.8506
- Recall: 0.8628
- F1: 0.8567
- Accuracy: 0.9845

## 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 |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0097        | 0.2910 | 500   | 0.0794          | 0.8535    | 0.8668 | 0.8601 | 0.9850   |
| 0.0108        | 0.5821 | 1000  | 0.0802          | 0.8457    | 0.8634 | 0.8544 | 0.9846   |
| 0.0115        | 0.8731 | 1500  | 0.0724          | 0.8486    | 0.8693 | 0.8588 | 0.9850   |
| 0.0087        | 1.1641 | 2000  | 0.0877          | 0.8448    | 0.8680 | 0.8562 | 0.9841   |
| 0.008         | 1.4552 | 2500  | 0.0810          | 0.8576    | 0.8645 | 0.8610 | 0.9850   |
| 0.0084        | 1.7462 | 3000  | 0.0844          | 0.8508    | 0.8661 | 0.8584 | 0.9846   |
| 0.0081        | 2.0373 | 3500  | 0.0859          | 0.8524    | 0.8634 | 0.8579 | 0.9846   |
| 0.0066        | 2.3283 | 4000  | 0.0920          | 0.8386    | 0.8778 | 0.8577 | 0.9845   |
| 0.0065        | 2.6193 | 4500  | 0.0907          | 0.8480    | 0.8624 | 0.8551 | 0.9841   |
| 0.0074        | 2.9104 | 5000  | 0.0873          | 0.8342    | 0.8665 | 0.8500 | 0.9838   |
| 0.0056        | 3.2014 | 5500  | 0.0997          | 0.8490    | 0.8602 | 0.8546 | 0.9842   |
| 0.0056        | 3.4924 | 6000  | 0.0908          | 0.8451    | 0.8696 | 0.8571 | 0.9847   |
| 0.0049        | 3.7835 | 6500  | 0.0991          | 0.8534    | 0.8621 | 0.8577 | 0.9849   |
| 0.0053        | 4.0745 | 7000  | 0.1107          | 0.8444    | 0.8641 | 0.8541 | 0.9844   |
| 0.0041        | 4.3655 | 7500  | 0.0994          | 0.8414    | 0.8658 | 0.8534 | 0.9839   |
| 0.0047        | 4.6566 | 8000  | 0.1016          | 0.8498    | 0.8664 | 0.8580 | 0.9839   |
| 0.0052        | 4.9476 | 8500  | 0.1000          | 0.8369    | 0.8746 | 0.8554 | 0.9844   |
| 0.0039        | 5.2386 | 9000  | 0.1097          | 0.8292    | 0.8777 | 0.8527 | 0.9835   |
| 0.0042        | 5.5297 | 9500  | 0.1000          | 0.8451    | 0.8703 | 0.8575 | 0.9844   |
| 0.0042        | 5.8207 | 10000 | 0.1087          | 0.8468    | 0.8612 | 0.8539 | 0.9841   |
| 0.0042        | 6.1118 | 10500 | 0.1120          | 0.8312    | 0.8753 | 0.8527 | 0.9839   |
| 0.0032        | 6.4028 | 11000 | 0.1144          | 0.8510    | 0.8540 | 0.8525 | 0.9842   |
| 0.0039        | 6.6938 | 11500 | 0.1075          | 0.8330    | 0.8689 | 0.8506 | 0.9837   |
| 0.0038        | 6.9849 | 12000 | 0.1112          | 0.8295    | 0.8677 | 0.8482 | 0.9833   |
| 0.0031        | 7.2759 | 12500 | 0.1084          | 0.8506    | 0.8628 | 0.8567 | 0.9845   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1