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---
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 |