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ckandemir
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crypto_sentiment

Text Classification
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Safetensors
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bert
Generated from Trainer
sentiment_analysis
Eval Results
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metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
  - generated_from_trainer
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

bitcoin_tweet_sentiment_classification

This model is a fine-tuned version of 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