added script for onnx sent clf

sentiment_onnx_classify.py

@@ -0,0 +1,67 @@
+import onnxruntime as ort
+import torch
+from transformers import AutoTokenizer
+import numpy as np
+
+tokenizer=AutoTokenizer.from_pretrained("sentiment_classifier/")
+
+#create onnx & onnx_int_8 sessions
+session=ort.InferenceSession("sent_clf_onnx/sentiment_classifier_onnx.onnx")
+session_int8=ort.InferenceSession("sent_clf_onnx/sentiment_classifier_onnx_int8.onnx")
+
+
+
+def classify_sentiment_onnx(texts,_model=session,_tokenizer=tokenizer):
+    """
+        user will pass texts separated by comma
+    """
+    try:
+        texts=texts.split(',')
+    except:
+        pass
+
+    _inputs = _tokenizer(texts, padding=True, truncation=True,
+                      return_tensors="np")
+
+    input_feed={
+        "input_ids":np.array(_inputs['input_ids']),
+        "attention_mask":np.array((_inputs['attention_mask']))
+    }
+
+    output = _model.run(input_feed=input_feed, output_names=['output_0'])[0]
+
+    output=np.argmax(output,axis=1)
+    output = ['Positive' if i == 1 else 'Negative' for i in output]
+    return output
+
+def classify_sentiment_onnx_quant(texts, _model=session_int8, _tokenizer=tokenizer):
+    """
+        user will pass texts separated by comma
+    """
+    try:
+        texts=texts.split(',')
+    except:
+        pass
+
+    _inputs = _tokenizer(texts, padding=True, truncation=True,
+                      return_tensors="np")
+
+
+    input_feed={
+        "input_ids":np.array(_inputs['input_ids']),
+        "attention_mask":np.array((_inputs['attention_mask']))
+    }
+
+    output = _model.run(input_feed=input_feed, output_names=['output_0'])[0]
+
+    output=np.argmax(output,axis=1)
+    output = ['Positive' if i == 1 else 'Negative' for i in output]
+
+    return output
+
+
+
+
+
+
+