---
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-mdeberta_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-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: 61.4326
- Precision: 0.7814
- Recall: 0.7843
- F1: 0.7828
- Accuracy: 0.9782

## 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 |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 134.73        | 0.5828 | 500  | 105.2633        | 0.6123    | 0.4383 | 0.5109 | 0.9484   |
| 96.7289       | 1.1655 | 1000 | 90.9748         | 0.6784    | 0.7067 | 0.6922 | 0.9701   |
| 84.9234       | 1.7483 | 1500 | 84.4287         | 0.7249    | 0.7498 | 0.7372 | 0.9740   |
| 78.4497       | 2.3310 | 2000 | 79.9555         | 0.7503    | 0.7396 | 0.7449 | 0.9748   |
| 73.644        | 2.9138 | 2500 | 76.3575         | 0.7372    | 0.7624 | 0.7496 | 0.9760   |
| 69.6629       | 3.4965 | 3000 | 73.5820         | 0.7391    | 0.7533 | 0.7461 | 0.9755   |
| 66.745        | 4.0793 | 3500 | 70.9258         | 0.7720    | 0.7482 | 0.7599 | 0.9767   |
| 63.9726       | 4.6620 | 4000 | 68.9640         | 0.7699    | 0.7423 | 0.7558 | 0.9763   |
| 61.778        | 5.2448 | 4500 | 67.0742         | 0.7621    | 0.7782 | 0.7701 | 0.9769   |
| 60.0151       | 5.8275 | 5000 | 65.6493         | 0.7804    | 0.7687 | 0.7745 | 0.9769   |
| 58.5554       | 6.4103 | 5500 | 64.3968         | 0.7767    | 0.7814 | 0.7791 | 0.9779   |
| 57.4554       | 6.9930 | 6000 | 63.7199         | 0.7844    | 0.7664 | 0.7753 | 0.9775   |
| 56.436        | 7.5758 | 6500 | 62.7811         | 0.7705    | 0.7847 | 0.7776 | 0.9777   |
| 55.6938       | 8.1585 | 7000 | 62.2272         | 0.7806    | 0.7791 | 0.7798 | 0.9781   |
| 55.105        | 8.7413 | 7500 | 61.7833         | 0.7791    | 0.7839 | 0.7815 | 0.9780   |
| 54.7106       | 9.3240 | 8000 | 61.5408         | 0.7820    | 0.7895 | 0.7858 | 0.9782   |
| 54.5482       | 9.9068 | 8500 | 61.4326         | 0.7814    | 0.7843 | 0.7828 | 0.9782   |


### Framework versions

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