update readme

README.md

@@ -28,32 +28,6 @@ The model is intended for term extraction tasks. It can be applied in domains li
 - Named Entity Recognition (NER)
 - Information Extraction
 
-## How to use
-
-Here's a quick example of how to use the model with the Hugging Face `transformers` library:
-
-```python
-from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
-
-# Load the tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained("tthhanh/xlm-ate-nobi-en")
-model = AutoModelForTokenClassification.from_pretrained("tthhanh/xlm-ate-nobi-en")
-
-# Create a pipeline for token classification
-nlp = pipeline("token-classification", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
-
-# Example text
-text = "Treatment of anemia in patients with heart disease : a clinical practice guideline from the American College of Physicians ."
-
-# Get predictions
-predictions = nlp(text)
-
-# Print predictions
-for prediction in predictions:
-    print(prediction)
-
-```
-
 ## Training and evaluation data
 
 We fine-tuned the English version of the ACTER dataset where  Named Entities are excluded in the gold standard. We trained on the Corruption and Wind Energy domain, validated on the Equitation domain, and tested on the Heart Failure domain.