Update README.md

README.md

@@ -60,4 +60,40 @@ T-FREX includes a set of released, fine-tuned models which are compared in the o
 
 ## How to use
 
-You can use this model following the instructions for [model inference for token classification](https://huggingface.co/docs/transformers/tasks/token_classification#inference).
+Below are code snippets to demonstrate how to use the T-FREX BERT base model (uncased) for named entity recognition on app reviews:
+
+```python
+
+from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
+
+# Load the pre-trained model and tokenizer
+model_name = "quim-motger/t-frex-bert-base-uncased"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForTokenClassification.from_pretrained(model_name)
+
+# Create a pipeline for named entity recognition
+ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer)
+
+# Example text
+text = "The share note file feature is completely useless."
+
+# Perform named entity recognition
+entities = ner_pipeline(text)
+
+# Print the recognized entities
+for entity in entities:
+    print(f"Entity: {entity['word']}, Label: {entity['entity']}, Score: {entity['score']:.4f}")
+
+# Example with multiple texts
+texts = [
+    "Great app I've tested a lot of free habit tracking apps and this is by far my favorite.",
+    "The only negative feedback I can give about this app is the difficulty level to set a sleep timer on it."
+]
+
+# Perform named entity recognition on multiple texts
+for text in texts:
+    entities = ner_pipeline(text)
+    print(f"Text: {text}")
+    for entity in entities:
+        print(f"  Entity: {entity['word']}, Label: {entity['entity']}, Score: {entity['score']:.4f}")
+```