Update README.md

README.md

@@ -1,11 +1,32 @@
+---
+language: en
+tags:
+- token-classification
+- ner
+- transformers
+license: apache-2.0
+datasets:
+- custom-dataset
+metrics:
+- precision
+- recall
+- f1
+model_name: my-custom-ner-model
+widget:
+- text: "The SALES of BEER and WINE in TTL US is increasing."
+---
+
 # My Custom NER Model
 
-This is a custom Named Entity Recognition (NER) model fine-tuned on domain-specific data using a BERT-based architecture.
+This is a custom **Named Entity Recognition (NER)** model fine-tuned on domain-specific data using a **BERT-based architecture**.
 
 ## Entities
-- `FACT`: Facts related to sales, revenue, etc.
-- `PRDC_CHAR`: Product characteristics like product names.
-- `MRKT_CHAR`: Market details like regions.
+The model is trained to recognize the following entities:
+- **`FACT`**: Facts related to sales, revenue, etc.
+- **`PRDC_CHAR`**: Product characteristics like product names.
+- **`MRKT_CHAR`**: Market details like regions.
+
+---
 
 ## Example Usage
 
@@ -23,37 +44,3 @@ ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer)
 text = "The SALES of BEER and WINE in TTL US is increasing."
 entities = ner_pipeline(text)
 print(entities)
-
-## SAmple Output
-[
-  {"entity": "FACT", "score": 0.98, "start": 4, "end": 9, "word": "SALES"},
-  {"entity": "PRDC_CHAR", "score": 0.95, "start": 13, "end": 17, "word": "BEER"},
-  {"entity": "PRDC_CHAR", "score": 0.94, "start": 22, "end": 26, "word": "WINE"},
-  {"entity": "MRKT_CHAR", "score": 0.96, "start": 30, "end": 36, "word": "TTL US"}
-]
-
-
-Training Details
-
-Base Model: distilbert-base-uncased
-Dataset: Custom dataset with 200 annotated sentences.
-Training Epochs: 3
-Learning Rate: 2e-5
-
-
-## To use the model
-`
-from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
-
-# Load the model and tokenizer from Hugging Face
-model = AutoModelForTokenClassification.from_pretrained("username/my-custom-ner-model")
-tokenizer = AutoTokenizer.from_pretrained("username/my-custom-ner-model")
-
-# Create an NER pipeline
-ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer)
-
-# Test the pipeline
-text = "The SALES of BEER and WINE in TTL US is increasing."
-entities = ner_pipeline(text)
-print(entities)
-`