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--- |
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license: apache-2.0 |
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base_model: bert-base-uncased |
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tags: |
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- generated_from_trainer |
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- sentiment_analysis |
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datasets: |
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- ckandemir/bitcoin_tweets_sentiment_kaggle |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: bitcoin_tweet_sentiment_classification |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: ckandemir/bitcoin_tweets_sentiment_kaggle |
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type: ckandemir/bitcoin_tweets_sentiment_kaggle |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.7150837988826816 |
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- name: F1 |
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type: f1 |
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value: 0.7212944928862212 |
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widget: |
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- text: "Sold all btc, tethered up before the correction." |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: text-classification |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bitcoin_tweet_sentiment_classification |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4542 |
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- Accuracy: 0.7151 |
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- F1: 0.7213 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-06 |
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- train_batch_size: 24 |
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- eval_batch_size: 24 |
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- seed: 42 |
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- gradient_accumulation_steps: 3 |
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- total_train_batch_size: 72 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine_with_restarts |
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- lr_scheduler_warmup_steps: 1000 |
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- training_steps: 1000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| 0.8941 | 0.65 | 50 | 0.8733 | 0.5698 | 0.5654 | |
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| 0.8565 | 1.3 | 100 | 0.8042 | 0.6690 | 0.6031 | |
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| 0.7896 | 1.96 | 150 | 0.7219 | 0.6802 | 0.5740 | |
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| 0.7174 | 2.61 | 200 | 0.6379 | 0.7514 | 0.6955 | |
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| 0.633 | 3.26 | 250 | 0.5745 | 0.7514 | 0.6930 | |
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| 0.5824 | 3.91 | 300 | 0.5303 | 0.75 | 0.6919 | |
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| 0.5365 | 4.57 | 350 | 0.4997 | 0.7514 | 0.7014 | |
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| 0.5089 | 5.22 | 400 | 0.4766 | 0.7458 | 0.6991 | |
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| 0.4893 | 5.87 | 450 | 0.4596 | 0.7486 | 0.7174 | |
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| 0.463 | 6.52 | 500 | 0.4446 | 0.7514 | 0.7127 | |
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| 0.4496 | 7.17 | 550 | 0.4407 | 0.7165 | 0.7048 | |
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| 0.4357 | 7.83 | 600 | 0.4364 | 0.7277 | 0.7246 | |
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| 0.4257 | 8.48 | 650 | 0.4324 | 0.7067 | 0.7115 | |
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| 0.4029 | 9.13 | 700 | 0.4314 | 0.7277 | 0.7180 | |
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| 0.3955 | 9.78 | 750 | 0.4354 | 0.7151 | 0.7164 | |
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| 0.3886 | 10.43 | 800 | 0.4396 | 0.7221 | 0.7244 | |
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| 0.3788 | 11.09 | 850 | 0.4363 | 0.7235 | 0.7194 | |
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| 0.366 | 11.74 | 900 | 0.4528 | 0.7179 | 0.7215 | |
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| 0.3298 | 12.39 | 950 | 0.4766 | 0.7053 | 0.7107 | |
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| 0.3423 | 13.04 | 1000 | 0.4542 | 0.7151 | 0.7213 | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
