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--- |
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license: mit |
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tags: |
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- text-classification |
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- generated_from_trainer |
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datasets: |
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- paws |
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metrics: |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: deberta-v3-large-finetuned-paws-paraphrase-detector |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: paws |
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type: paws |
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args: labeled_final |
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metrics: |
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- name: F1 |
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type: f1 |
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value: 0.9426698284279537 |
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- name: Precision |
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type: precision |
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value: 0.9300853289292595 |
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- name: Recall |
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type: recall |
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value: 0.9555995475113123 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# deberta-v3-large-finetuned-paws-paraphrase-detector |
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Feel free to use for paraphrase detection tasks! |
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This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the paws dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3046 |
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- F1: 0.9427 |
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- Precision: 0.9301 |
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- Recall: 0.9556 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 6e-06 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 50 |
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- num_epochs: 3 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:---------:|:------:| |
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| 0.1492 | 1.0 | 6176 | 0.1650 | 0.9537 | 0.9385 | 0.9695 | |
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| 0.1018 | 2.0 | 12352 | 0.1968 | 0.9544 | 0.9427 | 0.9664 | |
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| 0.0482 | 3.0 | 18528 | 0.2419 | 0.9521 | 0.9388 | 0.9658 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.11.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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