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--- |
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language: |
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- en |
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tags: |
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- generated_from_trainer |
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- crypto |
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- sentiment |
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- analysis |
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pipeline_tag: text-classification |
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base_model: ProsusAI/finbert |
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model-index: |
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- name: CryptoBERT |
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results: [] |
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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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# CryptoBERT |
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This model is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) on the Custom Crypto Market Sentiment dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3823 |
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```python |
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from transformers import BertTokenizer, BertForSequenceClassification |
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from transformers import pipeline |
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tokenizer = BertTokenizer.from_pretrained("kk08/CryptoBERT") |
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model = BertForSequenceClassification.from_pretrained("kk08/CryptoBERT") |
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classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer) |
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text = "Bitcoin (BTC) touches $29k, Ethereum (ETH) Set To Explode, RenQ Finance (RENQ) Crosses Massive Milestone" |
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result = classifier(text) |
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print(result) |
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``` |
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``` |
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[{'label': 'LABEL_1', 'score': 0.9678454399108887}] |
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``` |
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## Model description |
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This model fine-tunes the [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert), which is a pre-trained NLP model to analyze the sentiment of the financial text. |
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CryptoBERT model fine-tunes this by training the model as a downstream task on Custom Crypto Sentiment data to predict whether the given text related to the Crypto market is |
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Positive (LABEL_1) or Negative (LABEL_0). |
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## Intended uses & limitations |
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The model can perform well on Crypto-related data. The main limitation is that the fine-tuning was done using only a small corpus of data |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.4077 | 1.0 | 27 | 0.4257 | |
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| 0.2048 | 2.0 | 54 | 0.2479 | |
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| 0.0725 | 3.0 | 81 | 0.3068 | |
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| 0.0028 | 4.0 | 108 | 0.4120 | |
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| 0.0014 | 5.0 | 135 | 0.3566 | |
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| 0.0007 | 6.0 | 162 | 0.3495 | |
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| 0.0006 | 7.0 | 189 | 0.3645 | |
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| 0.0005 | 8.0 | 216 | 0.3754 | |
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| 0.0004 | 9.0 | 243 | 0.3804 | |
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| 0.0004 | 10.0 | 270 | 0.3823 | |
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### Framework versions |
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- Transformers 4.28.0 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
