---
license: mit
tags:
- text-classification
- generated_from_trainer
datasets:
- paws
metrics:
- f1
- precision
- recall
model-index:
- name: deberta-v3-large-finetuned-paws-paraphrase-detector
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: paws
      type: paws
      args: labeled_final
    metrics:
    - name: F1
      type: f1
      value: 0.9426698284279537
    - name: Precision
      type: precision
      value: 0.9300853289292595
    - name: Recall
      type: recall
      value: 0.9555995475113123
---

<!-- 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-v3-large-finetuned-paws-paraphrase-detector

Feel free to use for paraphrase detection tasks!

This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the paws dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3046
- F1: 0.9427
- Precision: 0.9301
- Recall: 0.9556

## 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: 6e-06
- 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
- lr_scheduler_warmup_steps: 50
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:---------:|:------:|
| 0.1492        | 1.0   | 6176  | 0.1650          | 0.9537 | 0.9385    | 0.9695 |
| 0.1018        | 2.0   | 12352 | 0.1968          | 0.9544 | 0.9427    | 0.9664 |
| 0.0482        | 3.0   | 18528 | 0.2419          | 0.9521 | 0.9388    | 0.9658 |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1