pytorch tensorboard deberta-v2 text-classification dataset:tweet_eval transformers generated_from_trainer license:mit model-index
---
license: mit
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- accuracy
- f1
model-index:
- name: tweet_eval-sentiment-finetuned
  results: 
  - task:
      name: Sentiment Analysis
      type: sentiment-analysis
    dataset:
      name: tweeteval
      type: tweeteval
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7099
    - name: f1
      type: f1
      value: 0.7097
---

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# 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: 0.6532
- Accuracy: 0.744
- F1: 0.7437

## 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: 3e-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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7491        | 1.0   | 357  | 0.6089          | 0.7345   | 0.7314 |
| 0.5516        | 2.0   | 714  | 0.5958          | 0.751    | 0.7516 |
| 0.4618        | 3.0   | 1071 | 0.6131          | 0.748    | 0.7487 |
| 0.4066        | 4.0   | 1428 | 0.6532          | 0.744    | 0.7437 |


### Framework versions

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