Delete .ipynb_checkpoints

.ipynb_checkpoints/README-checkpoint.md

@@ -1,99 +0,0 @@
----
-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])
-
-
-

.ipynb_checkpoints/config-checkpoint.json

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-{
-  "_name_or_path": "answerdotai/ModernBERT-base",
-  "architectures": [
-    "ModernBertModel"
-  ],
-  "attention_bias": false,
-  "attention_dropout": 0.0,
-  "bos_token_id": 50281,
-  "classifier_activation": "gelu",
-  "classifier_bias": false,
-  "classifier_dropout": 0.0,
-  "classifier_pooling": "mean",
-  "cls_token_id": 50281,
-  "decoder_bias": true,
-  "deterministic_flash_attn": false,
-  "embedding_dropout": 0.0,
-  "eos_token_id": 50282,
-  "global_attn_every_n_layers": 3,
-  "global_rope_theta": 160000.0,
-  "gradient_checkpointing": false,
-  "hidden_activation": "gelu",
-  "hidden_size": 768,
-  "initializer_cutoff_factor": 2.0,
-  "initializer_range": 0.02,
-  "intermediate_size": 1152,
-  "layer_norm_eps": 1e-05,
-  "local_attention": 128,
-  "local_rope_theta": 10000.0,
-  "max_position_embeddings": 8192,
-  "mlp_bias": false,
-  "mlp_dropout": 0.0,
-  "model_type": "modernbert",
-  "norm_bias": false,
-  "norm_eps": 1e-05,
-  "num_attention_heads": 12,
-  "num_hidden_layers": 22,
-  "output_hidden_states": true,
-  "pad_token_id": 50283,
-  "position_embedding_type": "absolute",
-  "reference_compile": null,
-  "repad_logits_with_grad": false,
-  "sep_token_id": 50282,
-  "sparse_pred_ignore_index": -100,
-  "sparse_prediction": false,
-  "torch_dtype": "float32",
-  "transformers_version": "4.48.2",
-  "vocab_size": 50368
-}