transformers pytorch tensorboard deberta text-classification generated_from_trainer dataset:glue license:mit model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- glue
license: mit
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: deberta-large-finetuned-mrpc
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: glue
      type: glue
      config: mrpc
      split: validation
      args: mrpc
    metrics:
    - type: accuracy
      value: 0.9044117647058824
      name: Accuracy
    - type: f1
      value: 0.9307282415630549
      name: F1
---

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# deberta-large-finetuned-mrpc

This model is a fine-tuned version of [microsoft/deberta-large](https://huggingface.co/microsoft/deberta-large) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5845
- Accuracy: 0.9044
- F1: 0.9307

## 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: 16
- eval_batch_size: 16
- seed: 87
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 230  | 0.3173          | 0.8848   | 0.9180 |
| No log        | 2.0   | 460  | 0.3501          | 0.8799   | 0.9127 |
| 0.3071        | 3.0   | 690  | 0.5214          | 0.8946   | 0.9239 |
| 0.3071        | 4.0   | 920  | 0.5542          | 0.9118   | 0.9366 |
| 0.0619        | 5.0   | 1150 | 0.5845          | 0.9044   | 0.9307 |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.13.3
