---
library_name: transformers
license: mit
base_model: haryoaw/scenario-TCR-NER_data-univner_half
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: scenario-kd-scr-ner-full-mdeberta_data-univner_half55
  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-full-mdeberta_data-univner_half55

This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_half](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_half) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 369.5634
- Precision: 0.3741
- Recall: 0.4149
- F1: 0.3935
- Accuracy: 0.9238

## 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 |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 638.1208      | 0.5828 | 500  | 570.1356        | 1.0       | 0.0004 | 0.0009 | 0.9241   |
| 540.7434      | 1.1655 | 1000 | 524.2383        | 0.2886    | 0.0521 | 0.0882 | 0.9253   |
| 490.1496      | 1.7483 | 1500 | 493.6944        | 0.2927    | 0.1682 | 0.2137 | 0.9297   |
| 454.2343      | 2.3310 | 2000 | 471.6737        | 0.3338    | 0.2411 | 0.2800 | 0.9293   |
| 429.0546      | 2.9138 | 2500 | 450.0691        | 0.4515    | 0.2450 | 0.3176 | 0.9362   |
| 407.3853      | 3.4965 | 3000 | 436.8550        | 0.3570    | 0.2877 | 0.3186 | 0.9304   |
| 390.9232      | 4.0793 | 3500 | 424.0557        | 0.3848    | 0.3245 | 0.3521 | 0.9313   |
| 376.2085      | 4.6620 | 4000 | 417.5171        | 0.3349    | 0.3800 | 0.3561 | 0.9216   |
| 364.3876      | 5.2448 | 4500 | 404.9495        | 0.3708    | 0.3766 | 0.3737 | 0.9276   |
| 354.1139      | 5.8275 | 5000 | 398.9413        | 0.3534    | 0.3877 | 0.3697 | 0.9226   |
| 344.7845      | 6.4103 | 5500 | 394.6783        | 0.3273    | 0.4193 | 0.3676 | 0.9143   |
| 337.8201      | 6.9930 | 6000 | 382.1873        | 0.3881    | 0.3900 | 0.3891 | 0.9289   |
| 330.94        | 7.5758 | 6500 | 381.2287        | 0.3480    | 0.4074 | 0.3754 | 0.9188   |
| 326.2092      | 8.1585 | 7000 | 372.6259        | 0.4087    | 0.3877 | 0.3979 | 0.9306   |
| 322.0348      | 8.7413 | 7500 | 374.2613        | 0.3530    | 0.4144 | 0.3812 | 0.9184   |
| 319.0297      | 9.3240 | 8000 | 370.3267        | 0.3774    | 0.4131 | 0.3944 | 0.9239   |
| 317.8106      | 9.9068 | 8500 | 369.5634        | 0.3741    | 0.4149 | 0.3935 | 0.9238   |


### Framework versions

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