---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- new_dataset
metrics:
- accuracy
model-index:
- name: sentiment-analysis-twitter
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: new_dataset
      type: new_dataset
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7965
---

<!-- 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. -->

# sentiment-analysis-twitter

This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the new_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4579
- Accuracy: 0.7965

## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5315        | 1.0   | 157  | 0.4517          | 0.788    |
| 0.388         | 2.0   | 314  | 0.4416          | 0.8      |
| 0.3307        | 3.0   | 471  | 0.4579          | 0.7965   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1