Update README.md

README.md

@@ -10,20 +10,42 @@ tags:
 - BERT
 ---
 # Computing Skill NER
-You can employ this model using the Transformers library's pipeline for NER, or incorporate it as a conventional Transformer in the HuggingFace ecosystem.
+Nucha_SkillNER_BERT is a Named Entity Recognition (NER) model specifically fine-tuned to recognize skill-related entities from text, focusing on identifying both hard and soft skills. This model is built on top of a BERT-based architecture, allowing it to leverage contextual understanding for accurate extraction of skill-related information. It is particularly useful for analyzing job descriptions, resumes, or any text where skills are explicitly mentioned.
 
+The model supports the recognition of multiple skill categories, including technical skills (e.g., programming languages, software tools) and soft skills (e.g., communication, leadership). It is ideal for applications in recruitment, talent management, or skill-based data analysis.
 
-## How to Use
-You can employ this model using the Transformers library's *pipeline* for NER, or incorporate it as a conventional Transformer in the HuggingFace ecosystem.
 
+## How to Use
+You can use the Nucha/Nucha_SkillNER_BERT model for Named Entity Recognition (NER) by loading it directly from Hugging Face's transformers library. Below is an example of how to use the model with the pipeline API for entity extraction.
+### Step-by-Step Example:
 ```python
-# Import necessary libraries
-from transformers import BertTokenizer, BertForTokenClassification, AdamW
+# Libraly
+from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
+
+# Load the pre-trained model and tokenizer
+model_name = "Nucha/Nucha_SkillNER_BERT"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForTokenClassification.from_pretrained(model_name)
+
+# Create a NER pipeline
+ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
+
+# Sample text
+text = "I have experience in Python, JavaScript, and cloud technologies like AWS and Azure."
+
+# Run the pipeline on the text
+ner_results = ner_pipeline(text)
+
+# Display the results
+for entity in ner_results:
+    print(f"Entity: {entity['word']}, Label: {entity['entity_group']}, Score: {entity['score']:.4f}")
+
 
-# Load pre-trained BERT model and tokenizer
-tokenizer = BertTokenizer.from_pretrained('Nucha/Nucha_SkillNER_BERT')
-model = BertForTokenClassification.from_pretrained('Nucha/Nucha_SkillNER_BERT', num_labels=5)
 ```
+### Output Explanation:
+Entity: This is the word or phrase identified in the text that matches one of the model's recognized categories.
+Label: The classification label assigned to the entity, such as SKILL or TECHNOLOGY.
+Score: The confidence score of the model for the identified entity, represented as a floating-point number.
 
 ## Demo
 https://huggingface.co/spaces/Nucha/NuchaSkillNER