Update README.md

README.md

@@ -11,15 +11,17 @@ Model and tokenizer files for the InLegalBERT model.
 
 ### Training Data
 For building the pre-training corpus of Indian legal text, we collected a large corpus of case documents from the Indian Supreme Court and many High Courts of India.
-These documents were collected from diverse publicly available sources on the Web, such as official websites of these courts (e.g., [the website of the Indian Supreme Court](https://main.sci.gov.in/)), the erstwhile website of the Legal Information Institute of India, 
-the popular legal repository [IndianKanoon](https://www.indiankanoon.org), and so on.
 The court cases in our dataset range from 1950 to 2019, and belong to all legal domains, such as Civil, Criminal, Constitutional, and so on.
-Additionally, we collected 1,113 Central Government Acts, which are the documents codifying the laws of the country. Each Act is a collection of related laws, called Sections. These 1,113 Acts contain a total of 32,021 Sections. 
 In total, our dataset contains around 5.4 million Indian legal documents (all in the English language). 
 The raw text corpus size is around 27 GB.
 
-### Training Objective
+### Training Setup
 This model is initialized with the [LEGAL-BERT-SC model](https://huggingface.co/nlpaueb/legal-bert-base-uncased) from the paper [LEGAL-BERT: The Muppets straight out of Law School](https://aclanthology.org/2020.findings-emnlp.261/). In our work, we refer to this model as LegalBERT, and our re-trained model as InLegalBERT.
+We further train this model on our data for 300K steps on the Masked Language Modeling (MLM) and Next Sentence Prediction (NSP) tasks.
+
+### Model Overview
+This model has the same configuration as the [bert-base-uncased model](https://huggingface.co/bert-base-uncased):
+12 hidden layers, 768 hidden dimensionality, 12 attention heads, ~110M parameters
 
 ### Usage
 Using the tokenizer (same as [LegalBERT](https://huggingface.co/nlpaueb/legal-bert-base-uncased))
@@ -32,10 +34,7 @@ Using the model to get embeddings/representations for a sentence
 from transformers import AutoModel
 model = AutoModel.from_pretrained("law-ai/InLegalBERT")
 ```
-Using the model for further pre-training with MLM and NSP
-```python
-from transformers import BertForPreTraining
-model_with_pretraining_heads = BertForPreTraining.from_pretrained("law-ai/InLegalBERT")
-```
+
+### Fine-tuning Results
 
 ### Citation