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README.md

@@ -9,29 +9,37 @@ This model uses a custom BERT-CRF architecture for token classification, specifi
 
 ```python
 from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
 
-# Load model and tokenizer
 model_name = "RaduGabriel/gene-entity-recognition"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForTokenClassification.from_pretrained(model_name)
-
-# Example usage for inference
-text = "The BRCA1 gene is associated with breast cancer."
-inputs = tokenizer(text, return_tensors="pt")
-outputs = model(**inputs)
-predictions = outputs.logits
+hf_token = None
+tokenizer = AutoTokenizer.from_pretrained(model_name, token=hf_token)
+model = AutoModelForTokenClassification.from_pretrained(model_name, token=hf_token)
+
+text = "TIF1gamma, a novel member of the transcriptional intermediary factor 1 family, plays a crucial role in gene regulation."
+
+# Create NER pipeline
+ner_pipeline = pipeline(
+    "ner",
+    model=model,
+    tokenizer=tokenizer,
+    aggregation_strategy="simple"
+)
+
+
+results = ner_pipeline(text)
+print(results)
 ```
 
 ## Labels
 - O
 - B-GENE
 - I-GENE
-- E-GENE
 
 ## Model Details
 - Architecture: BERT-CRF
 - Base Model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext
-- Number of Labels: 4
+- Number of Labels: 3
 - CRF Layer: Disabled
 
 ## Training Details