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---
library_name: transformers
license: mit
language:
- en
tags:
- chronologically consistent
- modernbert
- glue
pipeline_tag: fill-mask
inference: false
---
# ChronoBERT
## Model Description
ChronoBERT is a series **high-performance chronologically consistent large language models (LLM)** designed to eliminate lookahead bias and training leakage while maintain good language understanding in time-sensitive applications. The model is pretrained on **diverse, high-quality, open-source, and timestamped text** to maintain chronological consistency.
All models in the series achieve **GLUE benchmark scores that surpass standard BERT.** This approach preserves the integrity of historical analysis and enables more reliable economic and financial modeling.
- **Developed by:** Songrun He, Linying Lv, Asaf Manela, Jimmy Wu
- **Model type:** Transformer-based bidirectional encoder (ModernBERT architecture)
- **Language(s) (NLP):** English
- **License:** MIT License
## Model Sources
- **Paper:** "Chronologically Consistent Large Language Models" (He, Lv, Manela, Wu, 2025)
## How to Get Started with the Model
The model is compatible with the `transformers` library starting from v4.48.0:
```sh
pip install -U transformers>=4.48.0
pip install flash-attn
```
Here is an example code of using the model:
```python
from transformers import AutoTokenizer, AutoModel
device = 'cuda:0'
tokenizer = AutoTokenizer.from_pretrained("manelalab/chrono-bert-v1-19991231")
model = AutoModel.from_pretrained("manelalab/chrono-bert-v1-19991231").to(device)
text = "Obviously, the time continuum has been disrupted, creating a new temporal event sequence resulting in this alternate reality. -- Dr. Brown, Back to the Future Part II"
inputs = tokenizer(text, return_tensors="pt").to(device)
outputs = model(**inputs)
```
## Training Details
### Training Data
- **Pretraining corpus:** Our initial model chrono-bert-v1-19991231 is pretrained on 460 billion tokens of pre-2000, diverse, high-quality, and open-source text data to ensure no leakage of data afterwards.
- **Incremental updates:** Yearly updates from 2000 to 2024 with an additional 65 billion tokens of timestamped text.
### Training Procedure
- **Architecture:** ModernBERT-based model with rotary embeddings and flash attention.
- **Objective:** Masked token prediction.
## Evaluation
### Testing Data, Factors & Metrics
- **Language understanding:** Evaluated on **GLUE benchmark** tasks.
- **Financial forecasting:** Evaluated using **return prediction task** based on Dow Jones Newswire data.
- **Comparison models:** ChronoBERT was benchmarked against **BERT, FinBERT, StoriesLM-v1-1963, and Llama 3.1**.
### Results
- **GLUE Score:** chrono-bert-v1-19991231 and chrono-bert-v1-20241231 achieved GLUE score of 84.71 and 85.54 respectively, outperforming BERT (84.52).
- **Stock return predictions:** During the sample from 2008-01 to 2023-07, chrono-bert-v1-realtime achieves a long-short portfolio **Sharpe ratio of 4.80**, outperforming BERT, FinBERT, and StoriesLM-v1-1963, and comparable to **Llama 3.1 8B (4.90)**.
## Citation
```
@article{He2025ChronoBERT,
title={Chronologically Consistent Large Language Models},
author={He, Songrun and Lv, Linying and Manela, Asaf and Wu, Jimmy},
journal={Working Paper},
year={2025}
}
```
## Model Card Authors
- Songrun He (Washington University in St. Louis, [email protected])
- Linying Lv (Washington University in St. Louis, [email protected])
- Asaf Manela (Washington University in St. Louis, [email protected])
- Jimmy Wu (Washington University in St. Louis, [email protected])
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