---
library_name: transformers
license: mit
base_model: haryoaw/scenario-TCR-NER_data-univner_full
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: scenario-kd-scr-ner-full-mdeberta_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-mdeberta_data-univner_full55

This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_full](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_full) 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: 0.9241

## 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: 55
- 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 |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:---:|:--------:|
| 72900804.608  | 0.2911 | 500   | nan             | 0.0       | 0.0    | 0.0 | 0.9241   |
| 0.0           | 0.5822 | 1000  | nan             | 0.0       | 0.0    | 0.0 | 0.9241   |
| 0.0           | 0.8732 | 1500  | nan             | 0.0       | 0.0    | 0.0 | 0.9241   |
| 0.0           | 1.1643 | 2000  | nan             | 0.0       | 0.0    | 0.0 | 0.9241   |
| 0.0           | 1.4554 | 2500  | nan             | 0.0       | 0.0    | 0.0 | 0.9241   |
| 0.0           | 1.7465 | 3000  | nan             | 0.0       | 0.0    | 0.0 | 0.9241   |
| 0.0           | 2.0375 | 3500  | nan             | 0.0       | 0.0    | 0.0 | 0.9241   |
| 0.0           | 2.3286 | 4000  | nan             | 0.0       | 0.0    | 0.0 | 0.9241   |
| 0.0           | 2.6197 | 4500  | nan             | 0.0       | 0.0    | 0.0 | 0.9241   |
| 0.0           | 2.9108 | 5000  | nan             | 0.0       | 0.0    | 0.0 | 0.9241   |
| 0.0           | 3.2019 | 5500  | nan             | 0.0       | 0.0    | 0.0 | 0.9241   |
| 0.0           | 3.4929 | 6000  | nan             | 0.0       | 0.0    | 0.0 | 0.9241   |
| 0.0           | 3.7840 | 6500  | nan             | 0.0       | 0.0    | 0.0 | 0.9241   |
| 0.0           | 4.0751 | 7000  | nan             | 0.0       | 0.0    | 0.0 | 0.9241   |
| 0.0           | 4.3662 | 7500  | nan             | 0.0       | 0.0    | 0.0 | 0.9241   |
| 0.0           | 4.6573 | 8000  | nan             | 0.0       | 0.0    | 0.0 | 0.9241   |
| 0.0           | 4.9483 | 8500  | nan             | 0.0       | 0.0    | 0.0 | 0.9241   |
| 0.0           | 5.2394 | 9000  | nan             | 0.0       | 0.0    | 0.0 | 0.9241   |
| 0.0           | 5.5305 | 9500  | nan             | 0.0       | 0.0    | 0.0 | 0.9241   |
| 0.0           | 5.8216 | 10000 | nan             | 0.0       | 0.0    | 0.0 | 0.9241   |
| 0.0           | 6.1126 | 10500 | nan             | 0.0       | 0.0    | 0.0 | 0.9241   |


### Framework versions

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