Upload comparevec2vecwithada.py

comparevec2vecwithada.py

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+# -*- coding: utf-8 -*-
+"""compareVec2VecWithAda.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1jPaNXdO0_oW6VczlWfm5RPUVpMtVQD9c
+"""
+
+import pandas as pd
+import numpy as np
+import openai
+from sklearn.metrics.pairwise import cosine_similarity
+from tensorflow.keras.models import load_model
+from transformers import AutoTokenizer, AutoModel
+import torch
+import torch.nn.functional as F
+
+# Load model (available from Hugging Face)
+tokenizer = AutoTokenizer.from_pretrained('all-mpnet-base-v2')
+model = AutoModel.from_pretrained('all-mpnet-base-v2')
+
+# Define cosine similarity loss
+def cosine_similarity_loss(y_true, y_pred):
+    y_true = tf.nn.l2_normalize(y_true, axis=-1)
+    y_pred = tf.nn.l2_normalize(y_pred, axis=-1)
+    return -tf.reduce_mean(y_true * y_pred, axis=-1)
+
+
+#Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+loaded_model = load_model('mpnet2adaE75V4.h5', custom_objects={'cosine_similarity_loss': cosine_similarity_loss})
+
+openai.api_key="insert API key here"
+
+# load in csv of 10,000 embeddings in our test set paired with the original reviews
+df2 = pd.read_csv('Actual_Embeddings.csv')
+
+# Convert strings of lists to numpy arrays. this takes a while
+df2['Actual_Embeddings'] = df2['Actual_Embeddings'].apply(eval).apply(np.array)
+
+
+def get_top_5_texts(query):
+    encoded_input = tokenizer(query, padding=True, truncation=True, return_tensors='pt')
+
+    with torch.no_grad():
+        model_output = model(**encoded_input)
+
+    mpnetEmbeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+    mpnetEmbeddings = F.normalize(mpnetEmbeddings, p=2, dim=1)
+    mpnetEmbeddings = mpnetEmbeddings.detach().cpu().numpy()
+    mpnetEmbeddings = np.reshape(mpnetEmbeddings, (1,-1))
+    query_embedding = loaded_model.predict(mpnetEmbeddings)
+
+    similarities = [cosine_similarity(query_embedding.reshape(1, -1), emb.reshape(1, -1))[0][0] for emb in df2['Actual_Embeddings']]
+
+    print("Converted MPNet Embedding Results:")
+    top_5_idx2 = np.argsort(similarities)[-5:][::-1]
+    for i, idx in enumerate(top_5_idx2, 1):
+        print(f'Text {i}')
+        print(df2['combined'].iloc[idx])
+        print("\n")
+
+    response = openai.Embedding.create(input=query, model="text-embedding-ada-002")
+    query_embedding = np.array(response['data'][0]['embedding'])
+    similarities2 = [cosine_similarity(query_embedding.reshape(1, -1), emb.reshape(1, -1))[0][0] for emb in df2['Actual_Embeddings']]
+
+    print("OpenAI Embedding Results:")
+    top_5_idx2 = np.argsort(similarities2)[-5:][::-1]
+    for i, idx in enumerate(top_5_idx2, 1):
+        print(f'Text {i}')
+        print(df2['combined'].iloc[idx])
+        print("\n")
+
+while True:
+    query = input("Enter your query: ")
+    get_top_5_texts(query)