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---
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
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