---
base_model: haryoaw/scenario-TCR-NER_data-univner_half
library_name: transformers
license: mit
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: scenario-non-kd-po-ner-full-mdeberta_data-univner_half66
  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-po-ner-full-mdeberta_data-univner_half66

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: 0.1199
- Precision: 0.8560
- Recall: 0.8660
- F1: 0.8609
- Accuracy: 0.9848

## 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: 66
- 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 |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0064        | 0.5828  | 500   | 0.0946          | 0.8530    | 0.8696 | 0.8612 | 0.9847   |
| 0.0072        | 1.1655  | 1000  | 0.0935          | 0.8563    | 0.8531 | 0.8547 | 0.9844   |
| 0.0058        | 1.7483  | 1500  | 0.0977          | 0.8394    | 0.8530 | 0.8461 | 0.9836   |
| 0.005         | 2.3310  | 2000  | 0.1050          | 0.8492    | 0.8609 | 0.8550 | 0.9840   |
| 0.0054        | 2.9138  | 2500  | 0.1081          | 0.8503    | 0.8422 | 0.8462 | 0.9834   |
| 0.0043        | 3.4965  | 3000  | 0.1210          | 0.8273    | 0.8775 | 0.8516 | 0.9830   |
| 0.0049        | 4.0793  | 3500  | 0.1118          | 0.8413    | 0.8590 | 0.8501 | 0.9836   |
| 0.0035        | 4.6620  | 4000  | 0.1137          | 0.8465    | 0.8647 | 0.8555 | 0.9837   |
| 0.0031        | 5.2448  | 4500  | 0.1150          | 0.8430    | 0.8551 | 0.8490 | 0.9832   |
| 0.0027        | 5.8275  | 5000  | 0.1169          | 0.8401    | 0.8590 | 0.8495 | 0.9836   |
| 0.0027        | 6.4103  | 5500  | 0.1147          | 0.8517    | 0.8678 | 0.8597 | 0.9847   |
| 0.0034        | 6.9930  | 6000  | 0.1163          | 0.8457    | 0.8651 | 0.8553 | 0.9842   |
| 0.0024        | 7.5758  | 6500  | 0.1133          | 0.8523    | 0.8652 | 0.8587 | 0.9846   |
| 0.0031        | 8.1585  | 7000  | 0.1170          | 0.8399    | 0.8577 | 0.8487 | 0.9834   |
| 0.0019        | 8.7413  | 7500  | 0.1243          | 0.8413    | 0.8673 | 0.8541 | 0.9840   |
| 0.0019        | 9.3240  | 8000  | 0.1230          | 0.8393    | 0.8726 | 0.8556 | 0.9841   |
| 0.002         | 9.9068  | 8500  | 0.1218          | 0.8444    | 0.8549 | 0.8496 | 0.9839   |
| 0.002         | 10.4895 | 9000  | 0.1205          | 0.8518    | 0.8651 | 0.8584 | 0.9846   |
| 0.0017        | 11.0723 | 9500  | 0.1184          | 0.8643    | 0.8553 | 0.8598 | 0.9846   |
| 0.0014        | 11.6550 | 10000 | 0.1316          | 0.8363    | 0.8717 | 0.8536 | 0.9838   |
| 0.0016        | 12.2378 | 10500 | 0.1199          | 0.8560    | 0.8660 | 0.8609 | 0.9848   |


### Framework versions

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