---
datasets:
- ExponentialScience/DLT-Tweets
- ExponentialScience/DLT-Patents
- ExponentialScience/DLT-Scientific-Literature
language:
- en
base_model:
- allenai/scibert_scivocab_cased
---
# LedgerBERT

## Model Description

### Model Summary

LedgerBERT is a domain-adapted language model specialized for the Distributed Ledger Technology (DLT) field. It was created through continual pre-training of SciBERT on the DLT-Corpus, a comprehensive collection of 2.98 billion tokens from scientific literature, patents, and social media focused on blockchain, cryptocurrencies, and distributed ledger systems.

LedgerBERT captures DLT-specific terminology and concepts, making it particularly effective for NLP tasks involving blockchain technologies, cryptocurrency discourse, smart contracts, consensus mechanisms, and related domain-specific content.

- **Developed by:** Walter Hernandez Cruz, Peter Devine, Nikhil Vadgama, Paolo Tasca, Jiahua Xu
- **Model type:** BERT-base encoder (bidirectional transformer)
- **Language:** English
- **License:** CC-BY-NC 4.0 (Creative Commons Attribution-NonCommercial 4.0 International)
- **Base model:** SciBERT (allenai/scibert_scivocab_cased)
- **Training corpus:** DLT-Corpus (2.98 billion tokens)

### Model Architecture

- **Architecture:** BERT-base
- **Parameters:** 110 million
- **Hidden size:** 768
- **Number of layers:** 12
- **Attention heads:** 12
- **Vocabulary size:** 30,522 (SciBERT vocabulary)
- **Max sequence length:** 512 tokens

## Intended Uses

### Primary Use Cases

LedgerBERT is designed for NLP tasks in the DLT domain, including, but not limited to:

- **Named Entity Recognition (NER)**: Identifying DLT-specific entities such as consensus mechanisms (e.g., Proof of Stake), blockchain platforms (e.g., Ethereum, Hedera), cryptographic concepts (e.g., Merkle tree, hashing)
- **Text Classification**: Categorizing DLT-related documents, patents, or social media posts
- **Sentiment Analysis**: Analyzing sentiment in cryptocurrency news and social media
- **Information Extraction**: Extracting technical concepts and relationships from DLT literature
- **Document Retrieval**: Building search systems for DLT content
- **Question Answering (QA)**: Creating QA systems for blockchain and cryptocurrency topics

### Out-of-Scope Uses

- **Real-time trading systems**: LedgerBERT should not be used as the sole basis for automated trading decisions
- **Investment advice**: Not suitable for providing financial or investment recommendations without proper disclaimers
- **General-purpose NLP**: While LedgerBERT maintains general language understanding, it is optimized for DLT-specific tasks
- **Legal or regulatory compliance**: Should not be used for legal interpretation without expert review

## Training Details

### Training Data

LedgerBERT was continually pre-trained on the **DLT-Corpus**, consisting of:

- **Scientific Literature**: 37,440 documents, 564M tokens (1978-2025). See https://huggingface.co/datasets/ExponentialScience/DLT-Scientific-Literature
- **Patents**: 49,023 documents, 1,296M tokens (1990-2025). See https://huggingface.co/datasets/ExponentialScience/DLT-Patents
- **Social Media**: 22.03M documents, 1,120M tokens (2013-mid 2023). See https://huggingface.co/datasets/ExponentialScience/DLT-Tweets

**Total:** 22.12 million documents, 2.98 billion tokens

For more details, see: https://huggingface.co/collections/ExponentialScience/dlt-corpus-68e44e40d4e7a3bd7a224402

### Training Procedure

**Continual Pre-training:**

Starting from SciBERT (which already captures multidisciplinary scientific content), LedgerBERT was trained using Masked Language Modeling (MLM) on the DLT-Corpus to adapt the model to DLT-specific terminology and concepts.

**Training hyperparameters:**
- **Epochs:** 3
- **Learning rate:** 5×10⁻⁵ with linear decay schedule
- **MLM probability:** 0.15 (standard BERT masking)
- **Warmup ratio:** 0.10
- **Batch size:** 12 per device
- **Sequence length:** 512 tokens
- **Weight decay:** 0.01
- **Optimizer:** Stable AdamW
- **Precision:** bfloat16


## Limitations and Biases

### Known Limitations

- **Language coverage**: English only; does not support other languages
- **Temporal coverage**: Training data extends to mid-2023 for social media; may not capture very recent terminology
- **Domain specificity**: Optimized for DLT tasks; may underperform on general-purpose benchmarks compared to models like RoBERTa
- **Context length**: Limited to 512 tokens; longer documents require truncation or chunking

### Potential Biases

The model may reflect biases present in the training data:

- **Geographic bias**: English-language sources may over-represent certain regions
- **Platform bias**: Social media data only from Twitter/X; other platforms not represented
- **Temporal bias**: More recent DLT developments are more heavily represented
- **Market bias**: Training during periods of market volatility may influence sentiment understanding
- **Source bias**: Certain cryptocurrencies (e.g., Bitcoin, Ethereum) are more discussed than others

### Ethical Considerations

- **Market manipulation risk**: Could potentially be misused for analyzing or generating content for market manipulation
- **Investment decisions**: Should not be used as sole basis for financial decisions without proper risk disclaimers
- **Misinformation**: May reproduce or fail to identify false claims present in training data
- **Privacy**: While usernames were removed from social media data, care should be taken not to re-identify individuals

## How to Use

### Basic Usage

```python
from transformers import AutoTokenizer, AutoModel

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("ExponentialScience/LedgerBERT")
model = AutoModel.from_pretrained("ExponentialScience/LedgerBERT")

# Example text
text = "Ethereum uses Proof of Stake consensus mechanism for transaction validation."

# Tokenize and encode
inputs = tokenizer(text, return_tensors="pt", max_length=512, truncation=True)

# Get embeddings
outputs = model(**inputs)
embeddings = outputs.last_hidden_state
```

### Fine-tuning for NER

```python
from transformers import AutoTokenizer, AutoModelForTokenClassification, TrainingArguments, Trainer

# Load for token classification
tokenizer = AutoTokenizer.from_pretrained("ExponentialScience/LedgerBERT")
model = AutoModelForTokenClassification.from_pretrained(
    "ExponentialScience/LedgerBERT",
    num_labels=num_labels  # Set based on your NER task
)

# Fine-tune on your dataset
training_args = TrainingArguments(
    output_dir="./results",
    learning_rate=1e-5,
    per_device_train_batch_size=16,
    num_train_epochs=20,
    warmup_steps=500
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset
)

trainer.train()
```

### Fine-tuning for Sentiment Analysis

A fine-tuned version for market sentiment is available at: https://huggingface.co/ExponentialScience/LedgerBERT-Market-Sentiment

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("ExponentialScience/LedgerBERT-Market-Sentiment")
model = AutoModelForSequenceClassification.from_pretrained("ExponentialScience/LedgerBERT-Market-Sentiment")

text = "Bitcoin reaches new all-time high amid institutional adoption"
inputs = tokenizer(text, return_tensors="pt", max_length=512, truncation=True)
outputs = model(**inputs)
predictions = outputs.logits.argmax(dim=-1)
```

## Citation

If you use LedgerBERT in your research, please cite:

```bibtex
@article{hernandez2025dlt-corpus,
  title={DLT-Corpus: A Large-Scale Text Collection for the Distributed Ledger Technology Domain},
  author={Hernandez Cruz, Walter and Devine, Peter and Vadgama, Nikhil and Tasca, Paolo and Xu, Jiahua},
  year={2025}
}
```

## Related Resources

- **DLT-Corpus Collection**: https://huggingface.co/collections/ExponentialScience/dlt-corpus-68e44e40d4e7a3bd7a224402
- **Scientific Literature Dataset**: https://huggingface.co/datasets/ExponentialScience/DLT-Scientific-Literature
- **Patents Dataset**: https://huggingface.co/datasets/ExponentialScience/DLT-Patents
- **Social Media Dataset**: https://huggingface.co/datasets/ExponentialScience/DLT-Tweets
- **Sentiment Analysis Dataset**: https://huggingface.co/datasets/ExponentialScience/DLT-Sentiment-News
- **Fine-tuned Market Sentiment Model**: https://huggingface.co/ExponentialScience/LedgerBERT-Market-Sentiment

## Model Card Contact

For questions or feedback about LedgerBERT, please open an issue on the model repository or contact the authors through the DLT-Corpus GitHub repository: https://github.com/dlt-science/DLT-Corpus