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.ipynb_checkpoints/README-checkpoint.md

@@ -32,8 +32,8 @@ All models in the series achieve **GLUE benchmark scores that surpass standard B
 ```python
 from transformers import AutoTokenizer, AutoModel
 
-tokenizer = AutoTokenizer.from_pretrained("manelalab/chronobert-v1-19991231")
-model = AutoModel.from_pretrained("manelalab/chronobert-v1-19991231")
+tokenizer = AutoTokenizer.from_pretrained("manelalab/chrono-bert-v1-19991231")
+model = AutoModel.from_pretrained("manelalab/chrono-bert-v1-19991231")
 
 text = "You've gotta be very careful not to mess with the space-time continuum. -- Dr. Brown, Back to the Future"
 
@@ -45,7 +45,7 @@ outputs = model(**inputs)
 
 ### 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.
+- **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
@@ -63,8 +63,8 @@ outputs = model(**inputs)
 
 ### 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)**.
+- **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

README.md

@@ -32,8 +32,8 @@ All models in the series achieve **GLUE benchmark scores that surpass standard B
 ```python
 from transformers import AutoTokenizer, AutoModel
 
-tokenizer = AutoTokenizer.from_pretrained("manelalab/chronobert-v1-19991231")
-model = AutoModel.from_pretrained("manelalab/chronobert-v1-19991231")
+tokenizer = AutoTokenizer.from_pretrained("manelalab/chrono-bert-v1-19991231")
+model = AutoModel.from_pretrained("manelalab/chrono-bert-v1-19991231")
 
 text = "You've gotta be very careful not to mess with the space-time continuum. -- Dr. Brown, Back to the Future"
 
@@ -45,7 +45,7 @@ outputs = model(**inputs)
 
 ### 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.
+- **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
@@ -63,8 +63,8 @@ outputs = model(**inputs)
 
 ### 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)**.
+- **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