---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- sentiment140
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilbert-base-uncasedv1-finetuned-twitter-sentiment
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: sentiment140
      type: sentiment140
      config: sentiment140
      split: train
      args: sentiment140
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.82475
    - name: F1
      type: f1
      value: 0.8246033480256058
    - name: Precision
      type: precision
      value: 0.825087861584212
    - name: Recall
      type: recall
      value: 0.8016811137378513
---

<!-- 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-uncasedv1-finetuned-twitter-sentiment

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the sentiment140 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3985
- Accuracy: 0.8247
- F1: 0.8246
- Precision: 0.8251
- Recall: 0.8017

## 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     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 1.0   | 500  | 0.4049          | 0.8181   | 0.8178 | 0.8236    | 0.7862 |
| No log        | 2.0   | 1000 | 0.3985          | 0.8247   | 0.8246 | 0.8251    | 0.8017 |


### Framework versions

- Transformers 4.22.2
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.12.1