initial commit

README.md

@@ -1,10 +1,10 @@
 ---
-title: Embedding
-emoji: 📈
-colorFrom: yellow
-colorTo: purple
+title: Gpt 2 Secret
+emoji: 😻
+colorFrom: pink
+colorTo: pink
 sdk: gradio
-sdk_version: 3.40.1
+sdk_version: 3.39.0
 app_file: app.py
 pinned: false
 ---

app.py

@@ -0,0 +1,42 @@
+import gradio as gr
+import os
+from transformers import pipeline, set_seed
+from transformers import AutoTokenizer, AutoModel
+import torch
+import torch.nn.functional as F
+
+
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0]
+    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)
+
+
+
+tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
+model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
+
+
+
+def Bemenet(bemenet):
+    # Tokenize sentences
+    encoded_input = tokenizer([bemenet], padding=True, truncation=True, return_tensors='pt')
+
+    # Compute token embeddings
+    with torch.no_grad():
+        model_output = model(**encoded_input)
+
+    # Perform pooling
+    sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+    # Normalize embeddings
+    return F.normalize(sentence_embeddings, p=2, dim=1)
+
+
+interface = gr.Interface(fn=Bemenet,
+                        title="Cím..",
+                        description="Leírás..",
+                        inputs="text",
+                        outputs="text")
+
+interface.launch()

requirements.txt

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+transformers
+tensorflow
+torch