---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-tweets-sentiment
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: tweet_eval
      type: tweet_eval
      args: sentiment
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7295
    - name: F1
      type: f1
      value: 0.7303196028048928
---

<!-- 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-tweets-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: 0.8192
- Accuracy: 0.7295
- F1: 0.7303

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7126        | 1.0   | 713  | 0.6578          | 0.7185   | 0.7181 |
| 0.5514        | 2.0   | 1426 | 0.6249          | 0.7005   | 0.7046 |
| 0.4406        | 3.0   | 2139 | 0.7053          | 0.731    | 0.7296 |
| 0.3511        | 4.0   | 2852 | 0.7580          | 0.718    | 0.7180 |
| 0.2809        | 5.0   | 3565 | 0.8192          | 0.7295   | 0.7303 |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.10.0
- Datasets 1.16.1
- Tokenizers 0.10.3