embeddings.py

@@ -1,35 +0,0 @@
-import numpy as np
-import torch
-from transformers import AutoModel, AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-pl-cased")
-model = AutoModel.from_pretrained("Geotrend/distilbert-base-pl-cased")
-
-text = """
-"nazwa": "Tatar wołowy","""
-# "skladniki": [
-#   "wołowina",
-#   "cebula",
-#   "ogórki kiszone",
-#   "musztarda",
-#   "jajko",
-#   "pieprz",
-#   "sól"
-# ],
-# "alergeny": [
-#   "jajko",
-#   "gorczyca"
-# ]
-# """
-encoded_input = tokenizer(text, return_tensors='pt', padding=True)
-output = model(**encoded_input)
-prompt = "tatar"
-encoded_prompt = tokenizer(prompt, return_tensors='pt', padding=True)
-output_prompt = model(**encoded_prompt)
-
-text_embedding = output.last_hidden_state[:, 0, :]
-prompt_embedding = output_prompt.last_hidden_state[:, 0, :]
-cosine = torch.nn.functional.cosine_similarity(
-    text_embedding, prompt_embedding, dim=1)
-
-print(cosine.item())