---
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-xlmr-halfen_data-univner_en66
  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-kd-po-ner-full-xlmr-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: 51.2856
- Precision: 0.7594
- Recall: 0.7774
- F1: 0.7683
- Accuracy: 0.9817

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 84.3552       | 1.28  | 500  | 68.3345         | 0.7409    | 0.7164 | 0.7284 | 0.9800   |
| 61.0034       | 2.55  | 1000 | 61.0553         | 0.7614    | 0.7267 | 0.7436 | 0.9808   |
| 55.416        | 3.83  | 1500 | 57.0152         | 0.7573    | 0.7557 | 0.7565 | 0.9811   |
| 52.1923       | 5.1   | 2000 | 54.4194         | 0.7455    | 0.7671 | 0.7561 | 0.9813   |
| 50.0905       | 6.38  | 2500 | 52.9010         | 0.7549    | 0.7557 | 0.7553 | 0.9814   |
| 48.6873       | 7.65  | 3000 | 51.8337         | 0.7452    | 0.7598 | 0.7524 | 0.9815   |
| 47.8926       | 8.93  | 3500 | 51.2856         | 0.7594    | 0.7774 | 0.7683 | 0.9817   |


### Framework versions

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