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import torch |
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import torch.nn.functional as F |
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from torch import Tensor |
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from transformers import AutoTokenizer, AutoModel |
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import threading |
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import queue |
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import gradio as gr |
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import os |
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import json |
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import numpy as np |
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title = """ |
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# 👋🏻Welcome to 🙋🏻♂️Tonic's 📽️Nvidia 🛌🏻Embed V-1 !""" |
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description = """ |
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You can use this Space to test out the current model [nvidia/NV-Embed-v1](https://huggingface.co/nvidia/NV-Embed-v1). 🐣a generalist embedding model that ranks No. 1 on the Massive Text Embedding Benchmark (MTEB benchmark)(as of May 24, 2024), with 56 tasks, encompassing retrieval, reranking, classification, clustering, and semantic textual similarity tasks. |
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You can also use 📽️Nvidia 🛌🏻Embed V-1 by cloning this space. 🧬🔬🔍 Simply click here: <a style="display:inline-block" href="https://huggingface.co/spaces/Tonic/NV-Embed?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></h3> |
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Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's🛠️community 👻 [](https://discord.gg/GWpVpekp) On 🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Tonic-AI](https://github.com/tonic-ai) & contribute to 🌟 [MultiTonic](https://github.com/MultiTonic) 🤗Big thanks to Yuvi Sharma and all the folks at huggingface for the community grant 🤗 |
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""" |
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tasks = { |
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'ArguAna': 'Given a claim, find documents that refute the claim', |
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'ClimateFEVER': 'Given a claim about climate change, retrieve documents that support or refute the claim', |
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'DBPedia': 'Given a query, retrieve relevant entity descriptions from DBPedia', |
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'FEVER': 'Given a claim, retrieve documents that support or refute the claim', |
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'FiQA2018': 'Given a financial question, retrieve user replies that best answer the question', |
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'HotpotQA': 'Given a multi-hop question, retrieve documents that can help answer the question', |
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'MSMARCO': 'Given a web search query, retrieve relevant passages that answer the query', |
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'NFCorpus': 'Given a question, retrieve relevant documents that best answer the question', |
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'NQ': 'Given a question, retrieve Wikipedia passages that answer the question', |
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'QuoraRetrieval': 'Given a question, retrieve questions that are semantically equivalent to the given question', |
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'SCIDOCS': 'Given a scientific paper title, retrieve paper abstracts that are cited by the given paper', |
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'SciFact': 'Given a scientific claim, retrieve documents that support or refute the claim', |
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'Touche2020': 'Given a question, retrieve detailed and persuasive arguments that answer the question', |
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'Natural Language Inference' : 'Retrieve semantically similar text', |
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'Natural Language Inference' : 'Given a premise, retrieve a hypothesis that is entailed by the premise 20k', |
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'PAQ, MSMARCO' : 'Given a web search query, retrieve relevant passages that answer the query', |
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'PAQ, MSMARCO' : 'Given a question, retrieve passages that answer the question', |
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'SQUAD' : 'Given a question, retrieve Wikipedia passages that answer the question' , |
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'StackExchange' : 'Given a question paragraph at StackExchange, retrieve a question duplicated paragraph', |
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'Natural Question' : 'Given a question, retrieve Wikipedia passages that answer the question', |
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'BioASQ' : 'Given a question, retrieve detailed question descriptions that are duplicates to the given question', |
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'STS12, STS22, STSBenchmark' : 'Retrieve semantically similar text.', |
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'AmazonCounterfactualClassification' : 'Classify a given Amazon customer review text as either counterfactual or not-counterfactual' , |
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'AmazonReviewsClassification' : 'Classify the given Amazon review into its appropriate rating category' , |
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'Banking77Classification' : 'Given a online banking query, find the corresponding intents', |
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'EmotionClassification' : 'Classify the emotion expressed in the given Twitter message into one of the six emotions:anger, fear, joy, love, sadness, and surprise', |
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'ImdbClassification': 'Classify the sentiment expressed in the given movie review text from the IMDB dataset', |
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'MTOPIntentClassification' : 'Classify the intent of the given utterance in task-oriented conversation', |
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'ToxicConversationsClassification' : 'Classify the given comments as either toxic or not toxic', |
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'TweetSentimentExtractionClassification' : 'Classify the sentiment of a given tweet as either positive, negative, or neutral', |
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'ArxivClusteringP2P' : 'Identify the main and secondary category of Arxiv papers based on the titles and abstracts', |
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'ArxivClusteringS2S' : 'Identify the main and secondary category of Arxiv papers based on the titles', |
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'BiorxivClusteringP2P' : 'Identify the main category of Biorxiv papers based on the titles and abstracts' , |
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'BiorxivClusteringS2S' : 'Identify the main category of Biorxiv papers based on the titles', |
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'MedrxivClusteringP2P' : 'Identify the main category of Medrxiv papers based on the titles and abstracts', |
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'MedrxivClusteringS2S' : 'Identify the main category of Medrxiv papers based on the titles', |
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'TwentyNewsgroupsClustering' : 'Identify the topic or theme of the given news articles' |
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} |
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os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:30' |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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tokenizer = AutoTokenizer.from_pretrained('nvidia/NV-Embed-v1', trust_remote_code=True) |
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model = AutoModel.from_pretrained('nvidia/NV-Embed-v1', trust_remote_code=True).to(device) |
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embedding_request_queue = queue.Queue() |
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embedding_response_queue = queue.Queue() |
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def clear_cuda_cache(): |
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torch.cuda.empty_cache() |
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def last_token_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: |
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left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0]) |
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if left_padding: |
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return last_hidden_states[:, -1] |
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else: |
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sequence_lengths = attention_mask.sum(dim=1) - 1 |
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batch_size = last_hidden_states.shape[0] |
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return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths] |
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def format_response(embeddings): |
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return { |
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"data": [ |
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{ |
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"embedding": embeddings, |
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"index": 0, |
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"object": "embedding" |
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} |
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], |
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"model": "e5-mistral", |
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"object": "list", |
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"usage": { |
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"prompt_tokens": 17, |
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"total_tokens": 17 |
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} |
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} |
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def embedding_worker(): |
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while True: |
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item = embedding_request_queue.get() |
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if item is None: |
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break |
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selected_task, input_text = item |
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embeddings = compute_embeddings(selected_task, input_text) |
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formatted_response = format_response(embeddings) |
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embedding_response_queue.put(formatted_response) |
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embedding_request_queue.task_done() |
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clear_cuda_cache() |
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def compute_embeddings(selected_task, input_text): |
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try: |
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task_description = tasks[selected_task] |
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except KeyError: |
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print(f"Selected task not found: {selected_task}") |
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return f"Error: Task '{selected_task}' not found. Please select a valid task." |
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max_length = 2048 |
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processed_texts = [f'Instruct: {task_description}\nQuery: {input_text}'] |
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batch_dict = tokenizer(processed_texts, max_length=max_length - 1, return_attention_mask=False, padding=False, truncation=True) |
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batch_dict['input_ids'] = [input_ids + [tokenizer.eos_token_id] for input_ids in batch_dict['input_ids']] |
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batch_dict = tokenizer.pad(batch_dict, padding=True, return_attention_mask=True, return_tensors='pt') |
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batch_dict = {k: v.to(device) for k, v in batch_dict.items()} |
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outputs = model(**batch_dict) |
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embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask']) |
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embeddings = F.normalize(embeddings, p=2, dim=1) |
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embeddings_list = embeddings.detach().cpu().numpy().tolist() |
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clear_cuda_cache() |
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return embeddings_list |
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def compute_similarity(selected_task, sentence1, sentence2, extra_sentence1, extra_sentence2): |
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try: |
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task_description = tasks[selected_task] |
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except KeyError: |
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print(f"Selected task not found: {selected_task}") |
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return f"Error: Task '{selected_task}' not found. Please select a valid task." |
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embeddings1 = compute_embeddings(selected_task, sentence1) |
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embeddings2 = compute_embeddings(selected_task, sentence2) |
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embeddings3 = compute_embeddings(selected_task, extra_sentence1) |
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embeddings4 = compute_embeddings(selected_task, extra_sentence2) |
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similarity1 = compute_cosine_similarity(embeddings1, embeddings2) |
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similarity2 = compute_cosine_similarity(embeddings1, embeddings3) |
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similarity3 = compute_cosine_similarity(embeddings1, embeddings4) |
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similarity_scores = {"Similarity 1-2": similarity1, "Similarity 1-3": similarity2, "Similarity 1-4": similarity3} |
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clear_cuda_cache() |
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return similarity_scores |
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def compute_cosine_similarity(emb1, emb2): |
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tensor1 = torch.tensor(emb1).to(device).half() |
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tensor2 = torch.tensor(emb2).to(device).half() |
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similarity = F.cosine_similarity(tensor1, tensor2).item() |
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clear_cuda_cache() |
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return similarity |
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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.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("Sentence Similarity"): |
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sentence1_box = gr.Textbox(label="'Focus Sentence' - The 'Subject'") |
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sentence2_box = gr.Textbox(label="'Input Sentence' - 1") |
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extra_sentence1_box = gr.Textbox(label="'Input Sentence' - 2") |
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extra_sentence2_box = gr.Textbox(label="'Input Sentence' - 3") |
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similarity_button = gr.Button("Compute Similarity") |
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similarity_output = gr.Textbox(label="🐣e5-mistral🛌🏻 Similarity Scores") |
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similarity_button.click( |
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fn=compute_similarity, |
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inputs=[task_dropdown, sentence1_box, sentence2_box, extra_sentence1_box, extra_sentence2_box], |
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outputs=similarity_output |
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) |
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return demo |
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embedding_worker_thread = threading.Thread(target=embedding_worker, daemon=True) |
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embedding_worker_thread.start() |
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app_interface().queue() |
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app_interface().launch(share=True) |
