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---
license: mit
base_model: haryoaw/scenario-MDBT-TCR_data-cl-cardiff_cl_only
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: scenario-KD-SCR-CDF-EN-FROM-CL-D2_data-en-cardiff_eng_only55
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-CDF-EN-FROM-CL-D2_data-en-cardiff_eng_only55
This model is a fine-tuned version of [haryoaw/scenario-MDBT-TCR_data-cl-cardiff_cl_only](https://huggingface.co/haryoaw/scenario-MDBT-TCR_data-cl-cardiff_cl_only) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Accuracy: 0.3333
- F1: 0.1667
## 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: 5e-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 | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.72 | 100 | nan | 0.3333 | 0.1667 |
| No log | 3.45 | 200 | nan | 0.3333 | 0.1667 |
| No log | 5.17 | 300 | nan | 0.3333 | 0.1667 |
| No log | 6.9 | 400 | nan | 0.3333 | 0.1667 |
| 1.106 | 8.62 | 500 | nan | 0.3333 | 0.1667 |
| 1.106 | 10.34 | 600 | nan | 0.3333 | 0.1667 |
| 1.106 | 12.07 | 700 | nan | 0.3333 | 0.1667 |
| 1.106 | 13.79 | 800 | nan | 0.3333 | 0.1667 |
| 1.106 | 15.52 | 900 | nan | 0.3333 | 0.1667 |
| 0.0 | 17.24 | 1000 | nan | 0.3333 | 0.1667 |
| 0.0 | 18.97 | 1100 | nan | 0.3333 | 0.1667 |
| 0.0 | 20.69 | 1200 | nan | 0.3333 | 0.1667 |
| 0.0 | 22.41 | 1300 | nan | 0.3333 | 0.1667 |
| 0.0 | 24.14 | 1400 | nan | 0.3333 | 0.1667 |
| 0.0 | 25.86 | 1500 | nan | 0.3333 | 0.1667 |
| 0.0 | 27.59 | 1600 | nan | 0.3333 | 0.1667 |
| 0.0 | 29.31 | 1700 | nan | 0.3333 | 0.1667 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
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