---
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-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
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# scenario-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: 53.5934
- Precision: 0.7914
- Recall: 0.7922
- F1: 0.7918
- Accuracy: 0.9789

## 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 |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 93.9811       | 0.5828 | 500  | 77.1719         | 0.7802    | 0.7262 | 0.7522 | 0.9755   |
| 69.0206       | 1.1655 | 1000 | 70.2429         | 0.7554    | 0.7718 | 0.7635 | 0.9766   |
| 62.467        | 1.7483 | 1500 | 66.3316         | 0.7886    | 0.7455 | 0.7664 | 0.9767   |
| 58.5858       | 2.3310 | 2000 | 63.5450         | 0.7970    | 0.7396 | 0.7672 | 0.9768   |
| 55.7072       | 2.9138 | 2500 | 61.1857         | 0.7871    | 0.7772 | 0.7821 | 0.9783   |
| 53.5041       | 3.4965 | 3000 | 59.5353         | 0.7816    | 0.7843 | 0.7829 | 0.9783   |
| 51.8153       | 4.0793 | 3500 | 58.3157         | 0.7938    | 0.7863 | 0.7900 | 0.9786   |
| 50.327        | 4.6620 | 4000 | 57.1124         | 0.7914    | 0.7905 | 0.7910 | 0.9788   |
| 49.2402       | 5.2448 | 4500 | 56.3184         | 0.7844    | 0.7986 | 0.7914 | 0.9789   |
| 48.2334       | 5.8275 | 5000 | 55.7867         | 0.7922    | 0.7862 | 0.7892 | 0.9787   |
| 47.4646       | 6.4103 | 5500 | 55.2770         | 0.7955    | 0.7818 | 0.7886 | 0.9785   |
| 46.8764       | 6.9930 | 6000 | 54.6109         | 0.7958    | 0.7826 | 0.7891 | 0.9788   |
| 46.3099       | 7.5758 | 6500 | 54.2702         | 0.8051    | 0.7830 | 0.7939 | 0.9792   |
| 45.8877       | 8.1585 | 7000 | 53.9679         | 0.7953    | 0.7917 | 0.7935 | 0.9792   |
| 45.5735       | 8.7413 | 7500 | 53.7160         | 0.7935    | 0.7907 | 0.7921 | 0.9787   |
| 45.3573       | 9.3240 | 8000 | 53.6114         | 0.7886    | 0.7919 | 0.7903 | 0.9791   |
| 45.2644       | 9.9068 | 8500 | 53.5934         | 0.7914    | 0.7922 | 0.7918 | 0.9789   |


### Framework versions

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