---
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-large-ReqORNot
  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-large-ReqORNot

This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5297
- Accuracy: 0.9135
- Weighted precision: 0.9135
- Weighted recall: 0.9135
- Weighted f1: 0.9134
- Macro precision: 0.9135
- Macro recall: 0.9128
- Macro f1: 0.9131

## 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: 2e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted precision | Weighted recall | Weighted f1 | Macro precision | Macro recall | Macro f1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------------------:|:---------------:|:-----------:|:---------------:|:------------:|:--------:|
| 0.4826        | 1.0   | 1896 | 0.4286          | 0.9020   | 0.9020             | 0.9020          | 0.9019      | 0.9018          | 0.9014       | 0.9016   |
| 0.3429        | 2.0   | 3792 | 0.4274          | 0.9077   | 0.9091             | 0.9077          | 0.9078      | 0.9076          | 0.9089       | 0.9076   |
| 0.1299        | 3.0   | 5688 | 0.5297          | 0.9135   | 0.9135             | 0.9135          | 0.9134      | 0.9135          | 0.9128       | 0.9131   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2