---
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-po-ner-full-mdeberta-halfen_data-univner_en66
  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-halfen_data-univner_en66

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: 63.9106
- Precision: 0.7647
- Recall: 0.7671
- F1: 0.7659
- Accuracy: 0.9812

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 115.4337      | 1.28  | 500  | 89.9508         | 0.6039    | 0.5114 | 0.5538 | 0.9672   |
| 81.5056       | 2.55  | 1000 | 78.2589         | 0.7137    | 0.7019 | 0.7077 | 0.9781   |
| 73.2299       | 3.83  | 1500 | 72.9013         | 0.7153    | 0.7360 | 0.7255 | 0.9791   |
| 68.3008       | 5.1   | 2000 | 69.0462         | 0.7313    | 0.7692 | 0.7497 | 0.9806   |
| 64.8932       | 6.38  | 2500 | 66.5653         | 0.7296    | 0.7516 | 0.7404 | 0.9796   |
| 62.7315       | 7.65  | 3000 | 64.7790         | 0.7677    | 0.7526 | 0.7601 | 0.9808   |
| 61.2637       | 8.93  | 3500 | 63.9106         | 0.7647    | 0.7671 | 0.7659 | 0.9812   |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3