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manelalab/
chrono-bert-v1-20191231

Name: chrono-bert-v1-20191231
Availability: InStock

chronologically consistent

glue

mit

autotrain_compatible

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ChronoBERT

Model Description

ChronoBERT is a series of high-performance chronologically consistent large language models (LLM) designed to eliminate lookahead bias and training leakage while maintaining 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)

🚀 Quickstart

You can try ChronoBERT directly in your browser via Google Colab:

Open in Colab

Or run it locally with:

pip install -U transformers>=4.48.0
pip install flash-attn

Extract Embeddings

The following contains a code snippet illustrating how to use the model to generate embeddings based on given inputs.

python

from transformers import AutoTokenizer, AutoModel
device = 'cuda:0'

model_name = "manelalab/chrono-bert-v1-19991231"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name).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)

Masked Language Modeling (MLM) Prediction

The following contains a code snippet illustrating how to use the model to predict a missing token given an incomplete sentence.

python

from transformers import AutoTokenizer, AutoModelForMaskedLM
device = 'cuda:0'

model_name = "manelalab/chrono-bert-v1-20201231"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForMaskedLM.from_pretrained(model_name).to(device)

year_election = 2016
year_begin = year_election+1
text = f"After the {year_election} U.S. presidential election, President [MASK] was inaugurated as U.S. President in the year {year_begin}."

inputs = tokenizer(text, return_tensors="pt").to(device)
outputs = model(**inputs)
masked_index = inputs["input_ids"][0].tolist().index(tokenizer.mask_token_id)
predicted_token_id = outputs.logits[0, masked_index].argmax(axis=-1)
predicted_token = tokenizer.decode(predicted_token_id)

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 scores 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, h.songrun@wustl.edu)
Linying Lv (Washington University in St. Louis, llyu@wustl.edu)
Asaf Manela (Washington University in St. Louis, amanela@wustl.edu)
Jimmy Wu (Washington University in St. Louis, jimmywu@wustl.edu)

manelalab/chrono-bert-v1-20191231