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README.md

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+# Model Card for ChronoBERT
+
+## Model Details
+
+### 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
+
+```python
+from transformers import AutoTokenizer, AutoModel
+
+tokenizer = AutoTokenizer.from_pretrained("manelalab/chronobert-v1-19991231")
+model = AutoModel.from_pretrained("manelalab/chronobert-v1-19991231")
+
+text = "You've gotta be very careful not to mess with the space-time continuum. -- Dr. Brown, Back to the Future"
+
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+```
+
+## Training Details
+
+### Training Data
+
+- **Pretraining corpus:** Our initial model $\text{ChronoBERT}_{1999}$ 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:** $\text{ChronoBERT}_{1999}$ and $\text{ChronoBERT}_{2024}$ 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, $\text{ChronoBERT}_{\text{Realtime}}$ 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])
+
+
+