---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
- sentiment_analysis
widget:
- text: "Sold all btc, tethered up before the correction."
datasets:
- ckandemir/bitcoin_tweets_sentiment_kaggle
metrics:
- accuracy
- f1
model-index:
- name: bitcoin_tweet_sentiment_classification
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: ckandemir/bitcoin_tweets_sentiment_kaggle
      type: ckandemir/bitcoin_tweets_sentiment_kaggle
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7150837988826816
    - name: F1
      type: f1
      value: 0.7212944928862212
language:
- en
library_name: transformers
pipeline_tag: text-classification
---

<!-- 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. -->

# bitcoin_tweet_sentiment_classification

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the ckandemir/bitcoin_tweets_sentiment_kaggle dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4542
- Accuracy: 0.7151
- F1: 0.7213

## 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: 5e-06
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 72
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 1000
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8941        | 0.65  | 50   | 0.8733          | 0.5698   | 0.5654 |
| 0.8565        | 1.3   | 100  | 0.8042          | 0.6690   | 0.6031 |
| 0.7896        | 1.96  | 150  | 0.7219          | 0.6802   | 0.5740 |
| 0.7174        | 2.61  | 200  | 0.6379          | 0.7514   | 0.6955 |
| 0.633         | 3.26  | 250  | 0.5745          | 0.7514   | 0.6930 |
| 0.5824        | 3.91  | 300  | 0.5303          | 0.75     | 0.6919 |
| 0.5365        | 4.57  | 350  | 0.4997          | 0.7514   | 0.7014 |
| 0.5089        | 5.22  | 400  | 0.4766          | 0.7458   | 0.6991 |
| 0.4893        | 5.87  | 450  | 0.4596          | 0.7486   | 0.7174 |
| 0.463         | 6.52  | 500  | 0.4446          | 0.7514   | 0.7127 |
| 0.4496        | 7.17  | 550  | 0.4407          | 0.7165   | 0.7048 |
| 0.4357        | 7.83  | 600  | 0.4364          | 0.7277   | 0.7246 |
| 0.4257        | 8.48  | 650  | 0.4324          | 0.7067   | 0.7115 |
| 0.4029        | 9.13  | 700  | 0.4314          | 0.7277   | 0.7180 |
| 0.3955        | 9.78  | 750  | 0.4354          | 0.7151   | 0.7164 |
| 0.3886        | 10.43 | 800  | 0.4396          | 0.7221   | 0.7244 |
| 0.3788        | 11.09 | 850  | 0.4363          | 0.7235   | 0.7194 |
| 0.366         | 11.74 | 900  | 0.4528          | 0.7179   | 0.7215 |
| 0.3298        | 12.39 | 950  | 0.4766          | 0.7053   | 0.7107 |
| 0.3423        | 13.04 | 1000 | 0.4542          | 0.7151   | 0.7213 |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1