Cryptobert

🚀 CryptoBERT

CryptoBERT is a fine - tuned model based on ProsusAI/finbert. It is designed for sentiment analysis of cryptocurrency - related text, offering valuable insights into the crypto market sentiment.

🚀 Quick Start

You can use the following code to quickly start using the CryptoBERT model for sentiment analysis:

from transformers import BertTokenizer, BertForSequenceClassification
from transformers import pipeline

tokenizer = BertTokenizer.from_pretrained("kk08/CryptoBERT")
model = BertForSequenceClassification.from_pretrained("kk08/CryptoBERT")

classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
text = "Bitcoin (BTC) touches $29k, Ethereum (ETH) Set To Explode, RenQ Finance (RENQ) Crosses Massive Milestone"
result = classifier(text)
print(result)

The output will be like:

[{'label': 'LABEL_1', 'score': 0.9678454399108887}]

✨ Features

• Fine - tuned on Crypto Data: CryptoBERT fine - tunes the pre - trained ProsusAI/finbert on a custom cryptocurrency market sentiment dataset, enabling it to better understand and analyze crypto - related text.
• Sentiment Prediction: It can predict whether the given text related to the crypto market is positive (LABEL_1) or negative (LABEL_0).

💻 Usage Examples

Basic Usage

from transformers import BertTokenizer, BertForSequenceClassification
from transformers import pipeline

tokenizer = BertTokenizer.from_pretrained("kk08/CryptoBERT")
model = BertForSequenceClassification.from_pretrained("kk08/CryptoBERT")

classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
text = "Bitcoin (BTC) touches $29k, Ethereum (ETH) Set To Explode, RenQ Finance (RENQ) Crosses Massive Milestone"
result = classifier(text)
print(result)

Advanced Usage

# You can use the model in a loop to analyze multiple texts at once
from transformers import BertTokenizer, BertForSequenceClassification
from transformers import pipeline

tokenizer = BertTokenizer.from_pretrained("kk08/CryptoBERT")
model = BertForSequenceClassification.from_pretrained("kk08/CryptoBERT")

classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
texts = ["Bitcoin price is rising", "Ethereum has some technical issues"]
results = classifier(texts)
for result in results:
    print(result)

📚 Documentation

Model description

This model fine - tunes the ProsusAI/finbert, which is a pre - trained NLP model to analyze the sentiment of financial text. CryptoBERT fine - tunes this model by training it as a downstream task on custom crypto sentiment data to predict whether the given text related to the crypto market is positive (LABEL_1) or negative (LABEL_0).

Intended uses & limitations

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.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e - 05
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9, 0.999) and epsilon = 1e - 08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Framework versions

• Transformers 4.28.0
• Pytorch 2.0.0+cu118
• Datasets 2.11.0
• Tokenizers 0.13.3

🔧 Technical Details

• Base Model: ProsusAI/finbert, a pre - trained NLP model for financial text sentiment analysis.
• Fine - tuning: The model is fine - tuned on a custom cryptocurrency market sentiment dataset to predict the sentiment of crypto - related text.
• Hyperparameters: Specific hyperparameters are used during training, including learning rate, batch size, optimizer, etc., to ensure the model's performance.

📄 License

The license information is not provided in the original document. If you have the license details, please add them here.

📦 Installation

The installation steps are not provided in the original document. If you want to install the necessary libraries, you can use the following commands:

pip install transformers datasets torch