---
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- f1
model-index:
- name: debertav3-finetuned-answer-polarity-2e6-newdata
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: glue
      type: glue
      config: answer_pol
      split: validation
      args: answer_pol
    metrics:
    - name: F1
      type: f1
      value: 0.8526968320709598
---

<!-- 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. -->

# debertav3-finetuned-answer-polarity-2e6-newdata

This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4009
- F1: 0.8527

## 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-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: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 1.0   | 221  | 1.1820          | 0.2111 |
| 1.1195        | 2.0   | 442  | 0.7073          | 0.7068 |
| 0.4953        | 3.0   | 663  | 0.5068          | 0.8311 |
| 0.4953        | 4.0   | 884  | 0.4326          | 0.8498 |
| 0.2767        | 5.0   | 1105 | 0.4155          | 0.8553 |
| 0.2147        | 6.0   | 1326 | 0.4009          | 0.8527 |


### Framework versions

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