---
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-non-kd-po-ner-full-xlmr_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-xlmr_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: 0.1331
- Precision: 0.8406
- Recall: 0.8491
- F1: 0.8448
- Accuracy: 0.9836

## 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 |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0102        | 0.5828  | 500   | 0.0979          | 0.8297    | 0.8550 | 0.8422 | 0.9832   |
| 0.0109        | 1.1655  | 1000  | 0.0889          | 0.8346    | 0.8466 | 0.8406 | 0.9835   |
| 0.0084        | 1.7483  | 1500  | 0.0932          | 0.8491    | 0.8462 | 0.8477 | 0.9839   |
| 0.0075        | 2.3310  | 2000  | 0.0919          | 0.8434    | 0.8437 | 0.8436 | 0.9835   |
| 0.0072        | 2.9138  | 2500  | 0.1043          | 0.8278    | 0.8380 | 0.8329 | 0.9826   |
| 0.0058        | 3.4965  | 3000  | 0.1020          | 0.8370    | 0.8468 | 0.8419 | 0.9832   |
| 0.0059        | 4.0793  | 3500  | 0.1030          | 0.8467    | 0.8458 | 0.8463 | 0.9839   |
| 0.005         | 4.6620  | 4000  | 0.1182          | 0.8492    | 0.8326 | 0.8408 | 0.9830   |
| 0.005         | 5.2448  | 4500  | 0.1141          | 0.8235    | 0.8510 | 0.8370 | 0.9821   |
| 0.0044        | 5.8275  | 5000  | 0.1184          | 0.8273    | 0.8572 | 0.8420 | 0.9828   |
| 0.0045        | 6.4103  | 5500  | 0.1213          | 0.8417    | 0.8494 | 0.8455 | 0.9833   |
| 0.0048        | 6.9930  | 6000  | 0.1126          | 0.8413    | 0.8413 | 0.8413 | 0.9835   |
| 0.0043        | 7.5758  | 6500  | 0.1240          | 0.8363    | 0.8472 | 0.8417 | 0.9830   |
| 0.0036        | 8.1585  | 7000  | 0.1185          | 0.8470    | 0.8450 | 0.8460 | 0.9838   |
| 0.0032        | 8.7413  | 7500  | 0.1249          | 0.8338    | 0.8391 | 0.8365 | 0.9828   |
| 0.0022        | 9.3240  | 8000  | 0.1260          | 0.8351    | 0.8499 | 0.8425 | 0.9835   |
| 0.003         | 9.9068  | 8500  | 0.1208          | 0.8273    | 0.8420 | 0.8346 | 0.9827   |
| 0.0024        | 10.4895 | 9000  | 0.1216          | 0.8451    | 0.8463 | 0.8457 | 0.9836   |
| 0.0027        | 11.0723 | 9500  | 0.1214          | 0.8410    | 0.8390 | 0.8400 | 0.9832   |
| 0.0019        | 11.6550 | 10000 | 0.1234          | 0.8419    | 0.8458 | 0.8438 | 0.9833   |
| 0.0023        | 12.2378 | 10500 | 0.1289          | 0.8339    | 0.8502 | 0.8420 | 0.9830   |
| 0.0019        | 12.8205 | 11000 | 0.1286          | 0.8400    | 0.8401 | 0.8401 | 0.9832   |
| 0.002         | 13.4033 | 11500 | 0.1331          | 0.8406    | 0.8491 | 0.8448 | 0.9836   |


### Framework versions

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