Update README.md

README.md

@@ -9,23 +9,28 @@ model-index:
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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-# security-bert256-50k
+# CTI-BERT
 
-This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
+CTI-BERT is a pre-trained language model for the cybersecurity domain. 
+The model was trained on a large corpus of security-related text data, comprising approximately 1.2 billion tokens sourced from
+a diverse range of sources, including security news articles, vulnerability descriptions, books, academic publications, and security-related Wikipedia pages.
 
-## Model description
+For additional technical details and the model's performance metrics, please refer to [this paper](https://aclanthology.org/2023.emnlp-industry.12.pdf).
 
-More information needed
 
-## Intended uses & limitations
+## Model description
 
-More information needed
+This model has a vocabulary of 50,000 tokens and the sequence length of 256.
+Both the tokenizer and the BERT model were trained from scratch using the [run_mlm script](https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py)
+with the Masked language modeling (MLM) objective.
 
-## Training and evaluation data
 
-More information needed
+## Intended uses & limitations
 
-## Training procedure
+You can use the model for masked language modeling or token embedding generation, but the model is aimed at being fine-tuned on a downstream task, such as 
+sequence classification, text classification or question answering. 
+
+The model has shown improved performance for various cybersecurity text classification. However, it is not designed to be used as the main model for general-domain text.
 
 ### Training hyperparameters
 
@@ -41,9 +46,6 @@ The following hyperparameters were used during training:
 - lr_scheduler_warmup_steps: 10000
 - training_steps: 200000
 
-### Training results
-
-
 
 ### Framework versions