Update README.md

README.md

@@ -36,8 +36,42 @@ It achieves the following results on the evaluation set:
 
 The training data consists of the 4 most widely available ner_tags from the Finer-139 dataset. The training and the test data were curated from this source accordingly
 
-## Training procedure
+## Prediction procedure
+```
+from transformers import TAutoTokenizer
+from optimum.onnxruntime import ORTModelForTokenClassification
+import torch
 
+def onnx_inference(checkpoint, test_data, export=False):
+  test_text = " ".join(test_data['tokens'])
+  print("Test Text: " + test_text)
+
+  tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+  model = ORTModelForTokenClassification.from_pretrained(checkpoint, export=export)
+
+  inputs = tokenizer(test_text, return_tensors="pt")
+  outputs = model(**inputs).logits
+
+  predictions = torch.argmax(outputs, dim=2)
+
+  # Convert each tensor element to a scalar before calling .item()
+  predicted_token_class = [label_list[int(t)] for t in predictions[0]]
+  ner_tags = [label_list[int(t)] for t in test_data['ner_tags']]
+
+  print("Original Tags: ")
+  print(ner_tags)
+  print("Predicted Tags: ")
+  print(predicted_token_class)
+
+onnx_model_path = "" #add the path
+
+onnx_inference(onnx_model_path, test_data)
+
+"""
+Here the test_data should contain "tokens" and "ner_tags". This can be of type Dataset.
+"""
+
+```
 ### Training hyperparameters
 
 The following hyperparameters were used during training: