---
base_model: haryoaw/scenario-TCR-NER_data-univner_en
library_name: transformers
license: mit
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: scenario-kd-scr-ner-full_data-univner_full55
  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_data-univner_full55

This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_en](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.9405

## 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: 32
- eval_batch_size: 32
- seed: 55
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Precision | Recall | F1  | Accuracy |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:---:|:--------:|
| 5.3686        | 1.2755  | 500   | nan             | 0.0       | 0.0    | 0.0 | 0.9405   |
| 0.0           | 2.5510  | 1000  | nan             | 0.0       | 0.0    | 0.0 | 0.9405   |
| 0.0           | 3.8265  | 1500  | nan             | 0.0       | 0.0    | 0.0 | 0.9405   |
| 0.0           | 5.1020  | 2000  | nan             | 0.0       | 0.0    | 0.0 | 0.9405   |
| 0.0           | 6.3776  | 2500  | nan             | 0.0       | 0.0    | 0.0 | 0.9405   |
| 0.0           | 7.6531  | 3000  | nan             | 0.0       | 0.0    | 0.0 | 0.9405   |
| 0.0           | 8.9286  | 3500  | nan             | 0.0       | 0.0    | 0.0 | 0.9405   |
| 0.0           | 10.2041 | 4000  | nan             | 0.0       | 0.0    | 0.0 | 0.9405   |
| 0.0           | 11.4796 | 4500  | nan             | 0.0       | 0.0    | 0.0 | 0.9405   |
| 0.0           | 12.7551 | 5000  | nan             | 0.0       | 0.0    | 0.0 | 0.9405   |
| 0.0           | 14.0306 | 5500  | nan             | 0.0       | 0.0    | 0.0 | 0.9405   |
| 0.0           | 15.3061 | 6000  | nan             | 0.0       | 0.0    | 0.0 | 0.9405   |
| 0.0           | 16.5816 | 6500  | nan             | 0.0       | 0.0    | 0.0 | 0.9405   |
| 0.0           | 17.8571 | 7000  | nan             | 0.0       | 0.0    | 0.0 | 0.9405   |
| 0.0           | 19.1327 | 7500  | nan             | 0.0       | 0.0    | 0.0 | 0.9405   |
| 0.0           | 20.4082 | 8000  | nan             | 0.0       | 0.0    | 0.0 | 0.9405   |
| 0.0           | 21.6837 | 8500  | nan             | 0.0       | 0.0    | 0.0 | 0.9405   |
| 0.0           | 22.9592 | 9000  | nan             | 0.0       | 0.0    | 0.0 | 0.9405   |
| 0.0           | 24.2347 | 9500  | nan             | 0.0       | 0.0    | 0.0 | 0.9405   |
| 0.0           | 25.5102 | 10000 | nan             | 0.0       | 0.0    | 0.0 | 0.9405   |
| 0.0           | 26.7857 | 10500 | nan             | 0.0       | 0.0    | 0.0 | 0.9405   |


### Framework versions

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