---
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-scr-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-scr-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: 250.6721
- Precision: 0.4368
- Recall: 0.2754
- F1: 0.3378
- Accuracy: 0.9532

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 426.5331      | 1.28  | 500  | 343.1131        | 0.4717    | 0.0259 | 0.0491 | 0.9413   |
| 317.8317      | 2.55  | 1000 | 306.4857        | 0.3815    | 0.1066 | 0.1667 | 0.9449   |
| 288.4479      | 3.83  | 1500 | 285.8158        | 0.4429    | 0.1284 | 0.1990 | 0.9461   |
| 270.6268      | 5.1   | 2000 | 271.5317        | 0.3921    | 0.2050 | 0.2692 | 0.9494   |
| 257.3109      | 6.38  | 2500 | 260.7265        | 0.3853    | 0.2381 | 0.2943 | 0.9517   |
| 248.4864      | 7.65  | 3000 | 253.9278        | 0.3950    | 0.2940 | 0.3371 | 0.9527   |
| 242.8456      | 8.93  | 3500 | 250.6721        | 0.4368    | 0.2754 | 0.3378 | 0.9532   |


### Framework versions

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