---
license: mit
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- accuracy
- f1
model-index:
- name: tweet_eval-sentiment-finetuned
  results: 
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: sentiment
      type: sentiment
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.70
    - name: f1
      type: f1
      value: 0.70
---

<!-- 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. -->

# tweet_eval-sentiment-finetuned

This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8369
- Accuracy: 0.7305
- F1: 0.7297

## 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: 8e-05
- train_batch_size: 128
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7269        | 1.0   | 357  | 0.6057          | 0.733    | 0.7323 |
| 0.522         | 2.0   | 714  | 0.6115          | 0.7415   | 0.7416 |
| 0.359         | 3.0   | 1071 | 0.6970          | 0.744    | 0.7445 |
| 0.2386        | 4.0   | 1428 | 0.8369          | 0.7305   | 0.7297 |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.9.1
- Datasets 2.1.0
- Tokenizers 0.12.1