transformers pytorch deberta text-classification generated_from_trainer dataset:glue license:mit model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- glue
license: mit
metrics:
- f1
tags:
- generated_from_trainer
model-index:
- name: deberta-finetuned-answer-polarity-7e6
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: glue
      type: glue
      config: answer_pol
      split: validation
      args: answer_pol
    metrics:
    - type: f1
      value: 0.8625097340010413
      name: F1
---

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# deberta-finetuned-answer-polarity-7e6

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: 0.9143
- F1: 0.8625

## 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: 7e-06
- train_batch_size: 16
- eval_batch_size: 16
- 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 | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 1.0   | 214  | 0.6748          | 0.8696 |
| 0.0795        | 2.0   | 428  | 0.8541          | 0.8512 |
| 0.0508        | 3.0   | 642  | 0.9143          | 0.8625 |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
