---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-sentiment
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: tweet_eval
      type: tweet_eval
      config: sentiment
      split: validation
      args: sentiment
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7285
    - name: F1
      type: f1
      value: 0.7289390753190282
---

<!-- 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. -->

# distilbert-base-uncased-finetuned-sentiment

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6208
- Accuracy: 0.7285
- F1: 0.7289

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6922        | 1.0   | 713  | 0.6267          | 0.7195   | 0.7208 |
| 0.5571        | 2.0   | 1426 | 0.6208          | 0.7285   | 0.7289 |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3