Test.

README.md

@@ -1,8 +1,19 @@
-# NLPCategoryGame
-
-## Information
-### Database
-[GLUE](https://huggingface.co/datasets/glue)
-
-### Pre-trained model
-[sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
+---
+title: AnalogyArcade
+emoji: 🏆
+colorFrom: blue
+colorTo: yellow
+sdk: gradio
+sdk_version: 4.8.0
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+## Information
+### Database
+[GLUE](https://huggingface.co/datasets/glue)
+
+### Pre-trained model
+[sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)

main.py

@@ -1,52 +1,62 @@
-import spacy
-import math
-from datasets import load_dataset
-from sentence_transformers import SentenceTransformer
-from transformers import AutoTokenizer, AutoModel
-import torch
-import torch.nn.functional as F
-
-
-
-#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)
-
-
-def main():
-    dataset = load_dataset("glue", "cola")
-    dataset = dataset["train"]
-
-    sentences = ["This is an example sentence", "Each sentence is converted"]
-
-    model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
-    embeddings = model.encode(sentences)
-    print(embeddings)
-    
-    # Sentences we want sentence embeddings for
-    sentences = ['This is an example sentence', 'Each sentence is converted']
-
-    # Load model from HuggingFace Hub
-    tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
-    model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
-
-    # Tokenize sentences
-    encoded_input = tokenizer(sentences, 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
-    sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
-
-    print("Sentence embeddings:")
-    print(sentence_embeddings)
-    
-if __name__ == "__main__":
+import gradio as gr
+import spacy
+import math
+from datasets import load_dataset
+from sentence_transformers import SentenceTransformer
+from transformers import AutoTokenizer, AutoModel
+import torch
+import torch.nn.functional as F
+
+
+#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)
+
+
+def training():
+    dataset = load_dataset("glue", "cola")
+    dataset = dataset["train"]
+
+    sentences = ["This is an example sentence", "Each sentence is converted"]
+
+    model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+    embeddings = model.encode(sentences)
+    print(embeddings)
+    
+    # Sentences we want sentence embeddings for
+    sentences = ['This is an example sentence', 'Each sentence is converted']
+
+    # Load model from HuggingFace Hub
+    tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
+    model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
+
+    # Tokenize sentences
+    encoded_input = tokenizer(sentences, 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
+    sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
+
+    print("Sentence embeddings:")
+    print(sentence_embeddings)
+    
+
+def greet(name):
+    return "Hello " + name + "!!"
+
+
+def main():
+    iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+    iface.launch()
+
+
+if __name__ == "__main__":
     main()