Update README.md

README.md

@@ -15,13 +15,16 @@ This modelcard aims to be a base template for new models. It has been generated
 
 ## Model Details
 
+Instruction fine tuned Flan-T5 on Contracts
+
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
 
-This model is fine-tuned using Alpaca like instructions. The base data for instruction fine-tuning was a legal corpus with fields like agreement date, party name, and addresses.
+This model is fine-tuned using Alpaca like instructions. The base data for instruction fine-tuning is a legal corpus with fields like Titles , agreement date, party name, and addresses.
 
-An encoder-decoder architecture like flag T5 is used because the author found it to be better than a decoder only architecture given the same number of parameters.
+There are many type of models trained on above DataSet (DataSet will be released soon for the community)
+An encoder-decoder architecture like Flan-T5 is used because the author found it to be better than a decoder only architecture given the same number of parameters.
 
 
 - **Developed by:** [More Information Needed]
@@ -42,6 +45,7 @@ An encoder-decoder architecture like flag T5 is used because the author found it
 ## Uses
 
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+Just like any ChatGPT equivalent model (For Contracts Domain)
 
 ### Direct Use
 
@@ -82,17 +86,24 @@ Use the code below to get started with the model.
 >>> model_name = "scholarly360/contracts-extraction-flan-t5-base"
 >>> model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
 >>> tokenizer = AutoTokenizer.from_pretrained(model_name)
->>> 
->>> prompt = """ what kind of clause is "Neither Party shall be liable to the other for any abatement of Charges, delay or non-performance of its obligations under the Services Agreement arising from any cause or causes beyond its reasonable control (a "Force Majeure Event") including, without limitation" """
+>>> ### Example 1
+>>> prompt = """ what kind of clause is "Neither Party shall be liable to the other for any abatement of Charges, delay or non-performance of its obligations under the Services Agreement arising from any cause or causes beyond its reasonable control (a Force Majeure Event) including, without limitation """
 >>> inputs = tokenizer(prompt, return_tensors="pt")
 >>> outputs = model.generate(**inputs)
 >>> print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
->>> 
->>> prompt =  """ what is agreement date in "This COLLABORATION AGREEMENT (“Agreement”) dated November 14, 2002, is made by and between ZZZ, INC., a Delaware corporation""""
+>>> ### Example 1
+>>> prompt =  """ what is agreement date in 'This COLLABORATION AGREEMENT (Agreement) dated November 14, 2002, is made by and between ZZZ, INC., a Delaware corporation' """"
+>>> inputs = tokenizer(prompt, return_tensors="pt")
+>>> outputs = model.generate(**inputs)
+>>> print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
+>>> ### Example 3
+>>> prompt =  """ ### Instruction:
+what is agreement date 
+### Input:
+This COLLABORATION AGREEMENT (Agreement) dated November 14, 2002, is made by and between ZZZ, INC., a Delaware corporation """"
 >>> inputs = tokenizer(prompt, return_tensors="pt")
 >>> outputs = model.generate(**inputs)
 >>> print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
-
 ```
 
 
@@ -103,7 +114,7 @@ Use the code below to get started with the model.
 ### Training Data
 
 <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
+DataSet will be released soon for the community
 [More Information Needed]
 
 ### Training Procedure