# Model Card for Model ID

This model is sequential classification model that will take string content and categorize the 
content as what kind of Lean Contract is. This is a proof-of-concept and is constantly getting 
updated to improve the accuracy.

## Model Details
This is a research model, intended to show that we can use AI models to identify whether 
Lean principles are embedded in a contract or not 

### Model Description

This model is finetuned on Bertbasecase sequential classifier. The training data is created from a 
subject matter SMEs from Civil Engineering Domain.

Label Info : 
{ 'Lean_Prevents_Delay': 0,
  'Misuse_No_waiver': 1,
  'Lean_Promotes_Early_Completion': 2,
  'Lean_Prevents_Waiting' : 3,
  'Misuse_ambiguous' : 4,
  'No_Damages_Provision' : 5,
   'Non_compliance_Unaddressed' : 6,
  'No_timelines' : 7,
  'Lean_Prevents_Disputes' : 8,
 'Misuse_one_sided' : 9,
'Liability_undefined' : 10,
'Waiting' : 11,
'Lean_Prevents_Rework' : 12,
'Misuse_unfairness' : 13 }


## Getting started

from transformers import AutoTokenizer, BertForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("RameshBal/LeanContractModel")

model = BertForSequenceClassification.from_pretrained("RameshBal/LeanContractModel")

inputs = tokenizer("All disagreements between the contracting parties, with the exception of those 
explicitly denoted as finally and absolutely binding by the Contract, shall, following written 
notification by one party to the other, be resolved through arbitration. 
The arbitration process will be administered by an Engineer officer designated by the 
authority detailed in the tender documents.", return_tensors="pt")

with torch.no_grad():
  logits = model(**inputs).logits

predicted_class_id = logits.argmax().item()

model.config.id2label[predicted_class_id]

{"license": "mit", "metrics": ["accuracy", "f1"]}
transformers safetensors bert text-classification license:mit autotrain_compatible endpoints_compatible region:us