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---
base_model: microsoft/mdeberta-v3-base
library_name: transformers
license: mit
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: scenario-NON-KD-SCR-COPY-CDF-CL-D2_data-cl-cardiff_cl_only44
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-NON-KD-SCR-COPY-CDF-CL-D2_data-cl-cardiff_cl_only44
This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.2603
- Accuracy: 0.3657
- F1: 0.3633
## 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: 44
- 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 | 1.0870 | 250 | 1.3365 | 0.3519 | 0.3479 |
| 0.9017 | 2.1739 | 500 | 1.9777 | 0.3565 | 0.3508 |
| 0.9017 | 3.2609 | 750 | 2.9438 | 0.3542 | 0.3298 |
| 0.3023 | 4.3478 | 1000 | 3.1702 | 0.3611 | 0.3489 |
| 0.3023 | 5.4348 | 1250 | 3.4689 | 0.3534 | 0.3522 |
| 0.1011 | 6.5217 | 1500 | 4.0537 | 0.3627 | 0.3608 |
| 0.1011 | 7.6087 | 1750 | 4.5352 | 0.3573 | 0.3504 |
| 0.0549 | 8.6957 | 2000 | 4.5030 | 0.3495 | 0.3449 |
| 0.0549 | 9.7826 | 2250 | 4.6084 | 0.3519 | 0.3479 |
| 0.0339 | 10.8696 | 2500 | 4.7223 | 0.3565 | 0.3505 |
| 0.0339 | 11.9565 | 2750 | 4.9936 | 0.3565 | 0.3518 |
| 0.0232 | 13.0435 | 3000 | 4.5828 | 0.3449 | 0.3352 |
| 0.0232 | 14.1304 | 3250 | 5.0265 | 0.3565 | 0.3543 |
| 0.0224 | 15.2174 | 3500 | 5.2273 | 0.3627 | 0.3580 |
| 0.0224 | 16.3043 | 3750 | 5.2708 | 0.3611 | 0.3516 |
| 0.0156 | 17.3913 | 4000 | 5.6845 | 0.3511 | 0.3469 |
| 0.0156 | 18.4783 | 4250 | 5.5643 | 0.3603 | 0.3537 |
| 0.0081 | 19.5652 | 4500 | 5.9288 | 0.3519 | 0.3372 |
| 0.0081 | 20.6522 | 4750 | 5.9406 | 0.3611 | 0.3564 |
| 0.0034 | 21.7391 | 5000 | 5.9909 | 0.3534 | 0.3519 |
| 0.0034 | 22.8261 | 5250 | 6.1283 | 0.3611 | 0.3562 |
| 0.0017 | 23.9130 | 5500 | 6.1721 | 0.3688 | 0.3668 |
| 0.0017 | 25.0 | 5750 | 6.2167 | 0.3596 | 0.3581 |
| 0.0019 | 26.0870 | 6000 | 6.2126 | 0.3627 | 0.3596 |
| 0.0019 | 27.1739 | 6250 | 6.2446 | 0.3634 | 0.3616 |
| 0.0014 | 28.2609 | 6500 | 6.2484 | 0.3650 | 0.3624 |
| 0.0014 | 29.3478 | 6750 | 6.2603 | 0.3657 | 0.3633 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1
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