--- 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, 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)