Create utils.py

utils.py

@@ -0,0 +1,57 @@
+from transformers import AutoTokenizer,TFAutoModelForMaskedLM,TFAutoModelForSequenceClassification
+import tensorflow as tf
+import numpy as np
+def get_model(model):
+    DROP_OUT = 0.1
+    input_ids = tf.keras.Input(shape=(None, ),dtype='int32',name='input_ids')
+    token_type_ids = tf.keras.Input(shape=(None, ),dtype='int32',name='token_type_ids')
+    attention_mask = tf.keras.Input(shape=(None, ), dtype='int32',name='attention_mask')
+
+    transformer = model(input_ids,attention_mask, token_type_ids,output_hidden_states=True)
+    cls = transformer.hidden_states[-1][:,0,:]
+    drop_out = tf.keras.layers.Dropout(DROP_OUT)(cls)
+
+    sentiment = tf.keras.layers.Dense(768,activation='relu',name='sentiment')(drop_out)
+    sentiment = tf.keras.layers.Dense(768,activation='relu',name='sentiment2')(sentiment)
+    sentiment = tf.keras.layers.Dense(768,activation='relu',name='sentiment3')(sentiment)
+    sentiment = tf.keras.layers.Dense(4,activation='softmax',name='sentiment_out')(sentiment)
+
+    speech_act = tf.keras.layers.Dense(768,activation='relu',name='speech_act')(drop_out)
+    speech_act = tf.keras.layers.Dense(768,activation='relu',name='speech_act2')(speech_act)
+    speech_act = tf.keras.layers.Dense(768,activation='relu',name='speech_act3')(speech_act)
+    speech_act = tf.keras.layers.Dense(6,activation='softmax',name='speech_act_out')(speech_act)
+
+    sarcasm = tf.keras.layers.Dense(768,activation='relu',name='sarcasm')(drop_out)
+    sarcasm = tf.keras.layers.Dense(768,activation='relu',name='sarcasm2')(sarcasm)
+    sarcasm = tf.keras.layers.Dense(768,activation='relu',name='sarcasm3')(sarcasm)
+    sarcasm = tf.keras.layers.Dense(2,activation='softmax',name='sarcasm_out')(sarcasm)
+
+    output = tf.keras.layers.Concatenate(axis = -1)([sentiment,speech_act,sarcasm])
+
+    Fmodel = tf.keras.Model(inputs=[input_ids,token_type_ids, attention_mask], outputs=output)
+    Fmodel.load_weights('model_cls_weights.h5')
+    return Fmodel
+
+def classify(texts,model,tokenizer):
+    speech_act_decoder = {0:'Expression',1:'Assertion',2:'Question',3:'Recommendation',4:'Request',5:'Miscellaneous'}
+    sentiment_decoder = {0:'Positive',1:'Neutral',2:'Negative',3:'Mixed'}
+    sarcasm_decoder = {0:'Not Sarcastic',1:'Sarcastic'}
+    tokens = tokenizer(texts,return_tensors='tf',padding=True)
+    preds = model.predict(list(dict(tokens).values()),verbose=0)
+
+    sen_func = np.vectorize(lambda x : sentiment_decoder[x])
+    sentiment = sen_func(preds[:,0:4].argmax(axis=1))
+
+    sar_func = np.vectorize(lambda x : sarcasm_decoder[x])
+    sarcasm = sar_func(preds[:,10:].argmax(axis=1))
+
+    speech_func = np.vectorize(lambda x: speech_act_decoder[x])
+    speech_act = speech_func(preds[:,4:10].argmax(axis=1))
+    res = []
+    if isinstance(texts,list):
+        for i in range(len(texts)):
+            res.append({"text":texts[i],"sentiment":sentiment[i],"speech_act":speech_act[i],"sarcasm":sarcasm[i]})
+        return res
+    else:
+        res.append({"text":texts,"sentiment":sentiment[0],"speech_act":speech_act[0],"sarcasm":sarcasm[0]})
+        return res