transformers tensorboard safetensors distilbert text-classification generated_from_trainer dataset:twitter-sentiment-analysis base_model:distilbert/distilbert-base-uncased base_model:finetune:distilbert/distilbert-base-uncased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: distilbert-base-uncased
datasets:
- twitter-sentiment-analysis
license: apache-2.0
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: twitter-sentiment-analysis-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: twitter-sentiment-analysis
      type: twitter-sentiment-analysis
      config: default
      split: test
      args: default
    metrics:
    - type: accuracy
      value: 0.8366721507145392
      name: Accuracy
    - type: f1
      value: 0.8366721507145392
      name: F1
---

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# twitter-sentiment-analysis-v2

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the twitter-sentiment-analysis dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3771
- Accuracy: 0.8367
- F1: 0.8367

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: reduce_lr_on_plateau
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.3957        | 0.13  | 1000  | 0.4273          | 0.8075   | 0.8005 |
| 0.4086        | 0.27  | 2000  | 0.4081          | 0.8211   | 0.8139 |
| 0.4085        | 0.4   | 3000  | 0.3971          | 0.8274   | 0.8237 |
| 0.3936        | 0.53  | 4000  | 0.3857          | 0.8304   | 0.8307 |
| 0.3783        | 0.67  | 5000  | 0.3978          | 0.8317   | 0.8300 |
| 0.3858        | 0.8   | 6000  | 0.3887          | 0.8281   | 0.8182 |
| 0.3779        | 0.93  | 7000  | 0.3771          | 0.8367   | 0.8367 |
| 0.2971        | 1.07  | 8000  | 0.4023          | 0.8352   | 0.8310 |
| 0.2994        | 1.2   | 9000  | 0.3865          | 0.8326   | 0.8342 |
| 0.293         | 1.33  | 10000 | 0.4454          | 0.8299   | 0.8197 |
| 0.3053        | 1.47  | 11000 | 0.3929          | 0.8364   | 0.8349 |
| 0.3125        | 1.6   | 12000 | 0.4141          | 0.8366   | 0.8314 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
