Update README.md

README.md

@@ -3,7 +3,7 @@ license: apache-2.0
 tags:
 - generated_from_keras_callback
 model-index:
-- name: kasrahabib/KM35NCDF-NF-SUBCLASSES-cls
+- name: kasrahabib/KM35NCDF
   results: []
 widget:
 - text: "Application needs to keep track of subtasks in a task."
@@ -17,7 +17,7 @@ widget:
 <!-- This model card has been generated automatically according to the information Keras had access to. You should
 probably proofread and complete it, then remove this comment. -->
 
-# kasrahabib/KM35NCDF-NF-SUBCLASSES-cls
+# kasrahabib/KM35NCDF
 
 This model is a fine-tuned version of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on Software Requirements Dataset (SWARD) for classifying 19 Non-functional requirements. Note that based on literature, two out of 19 classes are Data and Behavior, which belong to types of Functional software requirements. It achieves the following results on the evaluation set:
 - Train Loss: 0.1691
@@ -54,7 +54,7 @@ from transformers import pipeline
 
 frame_work = 'tf'
 task = 'text-classification'
-model_ckpt = 'kasrahabib/KM35NCDF-NF-SUBCLASSES-cls'
+model_ckpt = 'kasrahabib/KM35NCDF '
 
 software_requirment_cls = pipeline(task = task, model = model_ckpt, framework = frame_work)
 
@@ -76,7 +76,7 @@ software_requirment_cls([example_1_US, example_2_PE, example_3_AC])
 import numpy as np
 from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
 
-model_ckpt = 'kasrahabib/KM35NCDF-NF-SUBCLASSES-cls'
+model_ckpt = 'kasrahabib/KM35NCDF '
 tokenizer = AutoTokenizer.from_pretrained(model_ckpt)
 model = TFAutoModelForSequenceClassification.from_pretrained(model_ckpt)
 
@@ -113,7 +113,7 @@ Then modify the code as below:
 import numpy as np
 from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
 
-model_ckpt =  'rest_of_the_path/KM35NCDF-NF-SUBCLASSES-cls'
+model_ckpt =  'rest_of_the_path/KM35NCDF '
 tokenizer = AutoTokenizer.from_pretrained(model_ckpt)
 model = TFAutoModelForSequenceClassification.from_pretrained(model_ckpt)