Instructions to use ExponentialScience/LedgerBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ExponentialScience/LedgerBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ExponentialScience/LedgerBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ExponentialScience/LedgerBERT") model = AutoModelForMaskedLM.from_pretrained("ExponentialScience/LedgerBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Added citation to preprint
Browse files
README.md
CHANGED
|
@@ -187,10 +187,14 @@ predictions = outputs.logits.argmax(dim=-1)
|
|
| 187 |
If you use LedgerBERT in your research, please cite:
|
| 188 |
|
| 189 |
```bibtex
|
| 190 |
-
@
|
| 191 |
-
|
| 192 |
-
|
| 193 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
| 194 |
}
|
| 195 |
```
|
| 196 |
|
|
|
|
| 187 |
If you use LedgerBERT in your research, please cite:
|
| 188 |
|
| 189 |
```bibtex
|
| 190 |
+
@misc{hernandez2026dlt-corpus,
|
| 191 |
+
title={DLT-Corpus: A Large-Scale Text Collection for the Distributed Ledger Technology Domain},
|
| 192 |
+
author={Walter Hernandez Cruz and Peter Devine and Nikhil Vadgama and Paolo Tasca and Jiahua Xu},
|
| 193 |
+
year={2026},
|
| 194 |
+
eprint={2602.22045},
|
| 195 |
+
archivePrefix={arXiv},
|
| 196 |
+
primaryClass={cs.CL},
|
| 197 |
+
url={https://arxiv.org/abs/2602.22045},
|
| 198 |
}
|
| 199 |
```
|
| 200 |
|