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| import torch | |
| import torch.nn.functional as F | |
| from torch import Tensor | |
| from transformers import AutoTokenizer, AutoModel | |
| import threading | |
| import queue | |
| import gradio as gr | |
| import os | |
| import json | |
| import numpy as np | |
| title = """ | |
| # 👋🏻Welcome to 🙋🏻♂️Tonic's 📽️Nvidia 🛌🏻Embed V-1 !""" | |
| description = """ | |
| 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. | |
| 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> | |
| 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 🤗 | |
| """ | |
| tasks = { | |
| 'ArguAna': 'Given a claim, find documents that refute the claim', | |
| 'ClimateFEVER': 'Given a claim about climate change, retrieve documents that support or refute the claim', | |
| 'DBPedia': 'Given a query, retrieve relevant entity descriptions from DBPedia', | |
| 'FEVER': 'Given a claim, retrieve documents that support or refute the claim', | |
| 'FiQA2018': 'Given a financial question, retrieve user replies that best answer the question', | |
| 'HotpotQA': 'Given a multi-hop question, retrieve documents that can help answer the question', | |
| 'MSMARCO': 'Given a web search query, retrieve relevant passages that answer the query', | |
| 'NFCorpus': 'Given a question, retrieve relevant documents that best answer the question', | |
| 'NQ': 'Given a question, retrieve Wikipedia passages that answer the question', | |
| 'QuoraRetrieval': 'Given a question, retrieve questions that are semantically equivalent to the given question', | |
| 'SCIDOCS': 'Given a scientific paper title, retrieve paper abstracts that are cited by the given paper', | |
| 'SciFact': 'Given a scientific claim, retrieve documents that support or refute the claim', | |
| 'Touche2020': 'Given a question, retrieve detailed and persuasive arguments that answer the question', | |
| 'Natural Language Inference' : 'Retrieve semantically similar text', | |
| 'Natural Language Inference' : 'Given a premise, retrieve a hypothesis that is entailed by the premise 20k', | |
| 'PAQ, MSMARCO' : 'Given a web search query, retrieve relevant passages that answer the query', | |
| 'PAQ, MSMARCO' : 'Given a question, retrieve passages that answer the question', | |
| 'SQUAD' : 'Given a question, retrieve Wikipedia passages that answer the question' , | |
| 'StackExchange' : 'Given a question paragraph at StackExchange, retrieve a question duplicated paragraph', | |
| 'Natural Question' : 'Given a question, retrieve Wikipedia passages that answer the question', | |
| 'BioASQ' : 'Given a question, retrieve detailed question descriptions that are duplicates to the given question', | |
| 'STS12, STS22, STSBenchmark' : 'Retrieve semantically similar text.', | |
| 'AmazonCounterfactualClassification' : 'Classify a given Amazon customer review text as either counterfactual or not-counterfactual' , | |
| 'AmazonReviewsClassification' : 'Classify the given Amazon review into its appropriate rating category' , | |
| 'Banking77Classification' : 'Given a online banking query, find the corresponding intents', | |
| 'EmotionClassification' : 'Classify the emotion expressed in the given Twitter message into one of the six emotions:anger, fear, joy, love, sadness, and surprise', | |
| 'ImdbClassification': 'Classify the sentiment expressed in the given movie review text from the IMDB dataset', | |
| 'MTOPIntentClassification' : 'Classify the intent of the given utterance in task-oriented conversation', | |
| 'ToxicConversationsClassification' : 'Classify the given comments as either toxic or not toxic', | |
| 'TweetSentimentExtractionClassification' : 'Classify the sentiment of a given tweet as either positive, negative, or neutral', | |
| 'ArxivClusteringP2P' : 'Identify the main and secondary category of Arxiv papers based on the titles and abstracts', | |
| 'ArxivClusteringS2S' : 'Identify the main and secondary category of Arxiv papers based on the titles', | |
| 'BiorxivClusteringP2P' : 'Identify the main category of Biorxiv papers based on the titles and abstracts' , | |
| 'BiorxivClusteringS2S' : 'Identify the main category of Biorxiv papers based on the titles', | |
| 'MedrxivClusteringP2P' : 'Identify the main category of Medrxiv papers based on the titles and abstracts', | |
| 'MedrxivClusteringS2S' : 'Identify the main category of Medrxiv papers based on the titles', | |
| 'TwentyNewsgroupsClustering' : 'Identify the topic or theme of the given news articles' | |
| } | |
| os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:30' | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| # Define the model and tokenizer globally | |
| tokenizer = AutoTokenizer.from_pretrained('nvidia/NV-Embed-v1', trust_remote_code=True) | |
| model = AutoModel.from_pretrained('nvidia/NV-Embed-v1', trust_remote_code=True).to(device) | |
| # Embedding requests and response queues | |
| embedding_request_queue = queue.Queue() | |
| embedding_response_queue = queue.Queue() | |
| def clear_cuda_cache(): | |
| torch.cuda.empty_cache() | |
| def last_token_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: | |
| left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0]) | |
| if left_padding: | |
| return last_hidden_states[:, -1] | |
| else: | |
| sequence_lengths = attention_mask.sum(dim=1) - 1 | |
| batch_size = last_hidden_states.shape[0] | |
| return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths] | |
| def format_response(embeddings): | |
| return { | |
| "data": [ | |
| { | |
| "embedding": embeddings, | |
| "index": 0, | |
| "object": "embedding" | |
| } | |
| ], | |
| "model": "e5-mistral", | |
| "object": "list", | |
| "usage": { | |
| "prompt_tokens": 17, | |
| "total_tokens": 17 | |
| } | |
| } | |
| def embedding_worker(): | |
| while True: | |
| # Wait for an item in the queue | |
| item = embedding_request_queue.get() | |
| if item is None: | |
| break | |
| selected_task, input_text = item | |
| embeddings = compute_embeddings(selected_task, input_text) | |
| formatted_response = format_response(embeddings) | |
| embedding_response_queue.put(formatted_response) | |
| embedding_request_queue.task_done() | |
| clear_cuda_cache() | |
| def compute_embeddings(selected_task, input_text): | |
| try: | |
| task_description = tasks[selected_task] | |
| except KeyError: | |
| print(f"Selected task not found: {selected_task}") | |
| return f"Error: Task '{selected_task}' not found. Please select a valid task." | |
| max_length = 2048 | |
| processed_texts = [f'Instruct: {task_description}\nQuery: {input_text}'] | |
| batch_dict = tokenizer(processed_texts, max_length=max_length - 1, return_attention_mask=False, padding=False, truncation=True) | |
| batch_dict['input_ids'] = [input_ids + [tokenizer.eos_token_id] for input_ids in batch_dict['input_ids']] | |
| batch_dict = tokenizer.pad(batch_dict, padding=True, return_attention_mask=True, return_tensors='pt') | |
| batch_dict = {k: v.to(device) for k, v in batch_dict.items()} | |
| outputs = model(**batch_dict) | |
| embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask']) | |
| embeddings = F.normalize(embeddings, p=2, dim=1) | |
| embeddings_list = embeddings.detach().cpu().numpy().tolist() | |
| clear_cuda_cache() | |
| return embeddings_list | |
| def compute_similarity(selected_task, sentence1, sentence2, extra_sentence1, extra_sentence2): | |
| try: | |
| task_description = tasks[selected_task] | |
| except KeyError: | |
| print(f"Selected task not found: {selected_task}") | |
| return f"Error: Task '{selected_task}' not found. Please select a valid task." | |
| # Compute embeddings for each sentence | |
| embeddings1 = compute_embeddings(selected_task, sentence1) | |
| embeddings2 = compute_embeddings(selected_task, sentence2) | |
| embeddings3 = compute_embeddings(selected_task, extra_sentence1) | |
| embeddings4 = compute_embeddings(selected_task, extra_sentence2) | |
| similarity1 = compute_cosine_similarity(embeddings1, embeddings2) | |
| similarity2 = compute_cosine_similarity(embeddings1, embeddings3) | |
| similarity3 = compute_cosine_similarity(embeddings1, embeddings4) | |
| similarity_scores = {"Similarity 1-2": similarity1, "Similarity 1-3": similarity2, "Similarity 1-4": similarity3} | |
| clear_cuda_cache() | |
| return similarity_scores | |
| def compute_cosine_similarity(emb1, emb2): | |
| tensor1 = torch.tensor(emb1).to(device).half() | |
| tensor2 = torch.tensor(emb2).to(device).half() | |
| similarity = F.cosine_similarity(tensor1, tensor2).item() | |
| clear_cuda_cache() | |
| return similarity | |
| def app_interface(): | |
| with gr.Blocks() as demo: | |
| gr.Markdown(title) | |
| gr.Markdown(description) | |
| with gr.Row(): | |
| task_dropdown = gr.Dropdown(list(tasks.keys()), label="Select a Task", value=list(tasks.keys())[0]) | |
| with gr.Tab("Sentence Similarity"): | |
| sentence1_box = gr.Textbox(label="'Focus Sentence' - The 'Subject'") | |
| sentence2_box = gr.Textbox(label="'Input Sentence' - 1") | |
| extra_sentence1_box = gr.Textbox(label="'Input Sentence' - 2") | |
| extra_sentence2_box = gr.Textbox(label="'Input Sentence' - 3") | |
| similarity_button = gr.Button("Compute Similarity") | |
| similarity_output = gr.Textbox(label="🐣e5-mistral🛌🏻 Similarity Scores") | |
| similarity_button.click( | |
| fn=compute_similarity, | |
| inputs=[task_dropdown, sentence1_box, sentence2_box, extra_sentence1_box, extra_sentence2_box], | |
| outputs=similarity_output | |
| ) | |
| return demo | |
| embedding_worker_thread = threading.Thread(target=embedding_worker, daemon=True) | |
| embedding_worker_thread.start() | |
| app_interface().queue() | |
| app_interface().launch(share=True) |