---
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