Tonic commited on
Commit
df3747d
·
verified ·
1 Parent(s): a374398

Update app.py

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Files changed (1) hide show
  1. app.py +5 -5
app.py CHANGED
@@ -68,13 +68,12 @@ class EmbeddingModel:
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  @spaces.GPU
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  def compute_similarity(self, sentence1, sentence2, extra_sentence1, extra_sentence2):
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- # Tokenize and encode sentences
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  sentences = [sentence1, sentence2, extra_sentence1, extra_sentence2]
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  encoded_input = self.tokenizer(sentences, padding=True, truncation=True, return_tensors='pt').to(device)
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  with torch.no_grad():
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  model_output = self.model(**encoded_input)
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- # Compute embeddings
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  embeddings = last_token_pool(model_output.last_hidden_state, encoded_input['attention_mask'])
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  embeddings = F.normalize(embeddings, p=2, dim=1)
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@@ -88,9 +87,10 @@ def app_interface():
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  with gr.Blocks() as demo:
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  gr.Markdown(title)
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  gr.Markdown(description)
 
 
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  with gr.Tab("Embedding Generation"):
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- task_dropdown = gr.Dropdown(list(tasks.keys()), label="Select a Task", value=list(tasks.keys())[0])
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  input_text_box = gr.Textbox(label="📖Input Text")
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  system_prompt_box = gr.Textbox(label="🤖System Prompt (Optional)")
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  compute_button = gr.Button("Try🐣🛌🏻e5")
@@ -104,8 +104,8 @@ def app_interface():
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  with gr.Tab("Sentence Similarity"):
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  sentence1_box = gr.Textbox(label="Sentence 1")
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  sentence2_box = gr.Textbox(label="Sentence 2")
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- extra_sentence1_box = gr.Textbox(label="Extra Sentence 1")
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- extra_sentence2_box = gr.Textbox(label="Extra Sentence 2")
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  similarity_button = gr.Button("Compute Similarity")
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  similarity_output = gr.Label(label="🐣e5-mistral🛌🏻 Similarity Scores")
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  similarity_button.click(
 
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  @spaces.GPU
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  def compute_similarity(self, sentence1, sentence2, extra_sentence1, extra_sentence2):
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+
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  sentences = [sentence1, sentence2, extra_sentence1, extra_sentence2]
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  encoded_input = self.tokenizer(sentences, padding=True, truncation=True, return_tensors='pt').to(device)
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  with torch.no_grad():
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  model_output = self.model(**encoded_input)
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  embeddings = last_token_pool(model_output.last_hidden_state, encoded_input['attention_mask'])
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  embeddings = F.normalize(embeddings, p=2, dim=1)
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  with gr.Blocks() as demo:
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  gr.Markdown(title)
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  gr.Markdown(description)
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+ with gr.Row():
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+ task_dropdown = gr.Dropdown(list(tasks.keys()), label="Select a Task", value=list(tasks.keys())[0])
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  with gr.Tab("Embedding Generation"):
 
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  input_text_box = gr.Textbox(label="📖Input Text")
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  system_prompt_box = gr.Textbox(label="🤖System Prompt (Optional)")
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  compute_button = gr.Button("Try🐣🛌🏻e5")
 
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  with gr.Tab("Sentence Similarity"):
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  sentence1_box = gr.Textbox(label="Sentence 1")
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  sentence2_box = gr.Textbox(label="Sentence 2")
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+ extra_sentence1_box = gr.Textbox(label="Sentence 3")
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+ extra_sentence2_box = gr.Textbox(label="Sentence 4")
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  similarity_button = gr.Button("Compute Similarity")
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  similarity_output = gr.Label(label="🐣e5-mistral🛌🏻 Similarity Scores")
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  similarity_button.click(