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---
license: mit
base_model: microsoft/deberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-finetuned-claimdecomp
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. -->
# deberta-finetuned-claimdecomp
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7521
- Accuracy: 0.205
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 30000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.7301 | 50.0 | 5000 | 1.7496 | 0.205 |
| 1.7267 | 100.0 | 10000 | 1.7525 | 0.205 |
| 1.7277 | 150.0 | 15000 | 1.7508 | 0.205 |
| 1.7278 | 200.0 | 20000 | 1.7508 | 0.205 |
| 1.7222 | 250.0 | 25000 | 1.7506 | 0.205 |
| 1.725 | 300.0 | 30000 | 1.7521 | 0.205 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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