CINXE.COM

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.elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}</style><div class="elementor-heading-title elementor-size-default"><a href="https://xcelore.com/blog/category/blog/aiml/" rel="tag">AI and ML</a>, <a href="https://xcelore.com/blog/category/llama-3/" rel="tag">Llama-3</a></div> </div> </div> <div class="elementor-element elementor-element-a9c808a animated-slow elementor-invisible elementor-widget elementor-widget-theme-post-title elementor-page-title elementor-widget-heading" data-id="a9c808a" data-element_type="widget" data-settings="{"_animation":"fadeIn","_animation_tablet":"none"}" data-widget_type="theme-post-title.default"> <div class="elementor-widget-container"> <h1 class="elementor-heading-title elementor-size-default">Topic Modelling With Quantized LLaMA 3 & BERTopic</h1> </div> </div> <div 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href="https://xcelore.com/blog/author/roshan-sinha/"> <span class="elementor-icon-list-icon"> <i aria-hidden="true" class="far fa-user-circle"></i> </span> <span class="elementor-icon-list-text elementor-post-info__item elementor-post-info__item--type-author"> Roshan Sinha </span> </a> </li> </ul> </div> </div> <div class="elementor-element elementor-element-3984841 elementor-widget elementor-widget-spacer" data-id="3984841" data-element_type="widget" data-widget_type="spacer.default"> <div class="elementor-widget-container"> <div class="elementor-spacer"> <div class="elementor-spacer-inner"></div> </div> </div> </div> <section class="elementor-section elementor-inner-section elementor-element elementor-element-f684348 elementor-section-full_width elementor-section-height-default elementor-section-height-default" data-id="f684348" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-50 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"}</style> <div class="elementor-toc__header"> <h5 class="elementor-toc__header-title"> Table of Contents </h5> <div class="elementor-toc__toggle-button elementor-toc__toggle-button--expand" role="button" tabindex="0" aria-controls="elementor-toc__4529a03" aria-expanded="true" aria-label="Open table of contents"><i aria-hidden="true" class="fas fa-chevron-down"></i></div> <div class="elementor-toc__toggle-button elementor-toc__toggle-button--collapse" role="button" tabindex="0" aria-controls="elementor-toc__4529a03" aria-expanded="true" aria-label="Close table of contents"><i aria-hidden="true" class="fas fa-chevron-up"></i></div> </div> <div id="elementor-toc__4529a03" class="elementor-toc__body"> <div class="elementor-toc__spinner-container"> <i class="elementor-toc__spinner eicon-animation-spin eicon-loading" aria-hidden="true"></i> </div> </div> </div> </div> </div> </div> <div class="elementor-column elementor-col-50 elementor-inner-column elementor-element elementor-element-13f68b4" data-id="13f68b4" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-20bcd8f elementor-widget elementor-widget-theme-post-featured-image elementor-widget-image" data-id="20bcd8f" data-element_type="widget" data-widget_type="theme-post-featured-image.default"> <div class="elementor-widget-container"> <img width="1024" height="641" src="data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%201024%20641'%3E%3C/svg%3E" class="attachment-large size-large wp-image-992425" alt="Banner image for blog titles - Topic Modelling With Quantized LLaMA 3 & BERTopic" data-lazy-srcset="https://xcelore.com/wp-content/uploads/2024/05/LLaMA-3-1024x641.png 1024w, https://xcelore.com/wp-content/uploads/2024/05/LLaMA-3-300x188.png 300w, https://xcelore.com/wp-content/uploads/2024/05/LLaMA-3-768x481.png 768w, https://xcelore.com/wp-content/uploads/2024/05/LLaMA-3-1536x961.png 1536w, https://xcelore.com/wp-content/uploads/2024/05/LLaMA-3.png 1884w" data-lazy-sizes="(max-width: 1024px) 100vw, 1024px" data-lazy-src="https://xcelore.com/wp-content/uploads/2024/05/LLaMA-3-1024x641.png" /><noscript><img width="1024" height="641" src="https://xcelore.com/wp-content/uploads/2024/05/LLaMA-3-1024x641.png" class="attachment-large size-large wp-image-992425" alt="Banner image for blog titles - Topic Modelling With Quantized LLaMA 3 & BERTopic" srcset="https://xcelore.com/wp-content/uploads/2024/05/LLaMA-3-1024x641.png 1024w, https://xcelore.com/wp-content/uploads/2024/05/LLaMA-3-300x188.png 300w, https://xcelore.com/wp-content/uploads/2024/05/LLaMA-3-768x481.png 768w, https://xcelore.com/wp-content/uploads/2024/05/LLaMA-3-1536x961.png 1536w, https://xcelore.com/wp-content/uploads/2024/05/LLaMA-3.png 1884w" sizes="(max-width: 1024px) 100vw, 1024px" /></noscript> </div> </div> <div class="elementor-element elementor-element-a1a5a5f elementor-widget elementor-widget-theme-post-content" data-id="a1a5a5f" data-element_type="widget" data-widget_type="theme-post-content.default"> <div class="elementor-widget-container"> <div data-elementor-type="wp-post" data-elementor-id="992384" class="elementor elementor-992384" data-elementor-post-type="post"> <section class="elementor-section elementor-top-section elementor-element elementor-element-9871b95 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="9871b95" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-1088ff2" data-id="1088ff2" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-727be16 elementor-widget elementor-widget-text-editor" data-id="727be16" data-element_type="widget" data-widget_type="text-editor.default"> <div class="elementor-widget-container"> <style>/*! elementor - v3.21.0 - 26-05-2024 */ .elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}</style> <p><span style="font-weight: 400;">Today we’re diving into topic modeling using a powerful algorithm called </span><em><strong><a href="https://maartengr.github.io/BERTopic/" target="_blank" rel="noopener">BERTopic</a></strong></em><span style="font-weight: 400;">, and </span><span style="font-weight: 400;">Llama-3</span><span style="font-weight: 400;">, the next generation of Llama, is now available for broad use. It features pre-trained and instruction-fine-tuned language models with 8B and 70B parameters, supporting various use cases. We will be using topic modeling for topic extraction using BERTopic and the <em><strong><a href="https://xcelore.com/blog/quantization-in-llm/">quantized</a></strong></em> gguf version of </span><span style="font-weight: 400;">Llama-3-<em><strong><a href="https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct" target="_blank" rel="noopener">8b-instruct</a></strong></em></span><span style="font-weight: 400;"> from the dataset, and visualizing it for further understanding.</span></p> </div> </div> <div class="elementor-element elementor-element-a4c44f9 elementor-widget elementor-widget-heading" data-id="a4c44f9" data-element_type="widget" data-widget_type="heading.default"> <div class="elementor-widget-container"> <h2 class="elementor-heading-title elementor-size-default">What is Topic Modeling?</h2> </div> </div> <div class="elementor-element elementor-element-861547a elementor-widget elementor-widget-text-editor" data-id="861547a" data-element_type="widget" data-widget_type="text-editor.default"> <div class="elementor-widget-container"> <p><span style="font-weight: 400;">Businesses need help with vast amounts of unstructured text daily, ranging from customer emails to online reviews. This abundance of textual data necessitates efficient organization and understanding, leading us to rely on topic modeling.聽</span></p><p><span style="font-weight: 400;">Topic modeling is a statistical technique using unsupervised machine learning to uncover and showcase thematic structures within textual data. It’s crucial in various domains like information retrieval, text mining, modern search systems, and data visualization. These applications enable researchers to explore vast textual datasets efficiently.</span></p><p><span style="font-weight: 400;">The complexity of unstructured text data has historically posed challenges, including a lack of labeled data, limited context understanding, and difficulties in topic interpretation. Additionally, traditional topic modeling methods needed more scalability and pre-trained models, further hindering efficient analysis. However, recent advancements in natural language processing (NLP), particularly with the development of large language models (LLMs) like ChatGPT and GPT-4, have revolutionized topic modeling efficiency, especially for smaller datasets.</span></p><p><span style="font-weight: 400;">While LLMs offer improved capabilities, directly passing all documents for analysis remains computationally impractical. Here, BERTopic emerges as a potent solution, leveraging Transformer models’ language comprehension and C-TF-IDF to create dense clusters of coherent topics. Instead of analyzing each document individually, BERTopic’s clusters provide a foundation for LLMs to distill and fine-tune topic representations accurately.</span></p><p><span style="font-weight: 400;">By leveraging BERTopic’s clustering abilities, we can improve topic modeling with LLMs, optimizing the analysis of large textual datasets without excessive computational requirements.</span></p> </div> </div> <div class="elementor-element elementor-element-187f066 elementor-widget elementor-widget-heading" data-id="187f066" data-element_type="widget" data-widget_type="heading.default"> <div class="elementor-widget-container"> <h2 class="elementor-heading-title elementor-size-default">Why LLaMA-3 instead of ChatGPT or Gemini? </h2> </div> </div> <div class="elementor-element elementor-element-bd7892c elementor-widget elementor-widget-text-editor" data-id="bd7892c" data-element_type="widget" data-widget_type="text-editor.default"> <div class="elementor-widget-container"> <p><span style="font-weight: 400;">The Llama 3 language model is trained on a large, high-quality pretraining dataset of over 15T tokens from publicly available sources.</span></p><p><span style="font-weight: 400;">The dataset is seven times larger than Llama 2 and includes four times more code. Over 5% of the dataset is non-English, covering over 30 languages. To ensure high-quality data, a series of data-filtering pipelines are developed, including heuristic filters, NSFW filters, semantic deduplication approaches, and text classifiers. Experiments are conducted to select the best data mix for Llama 3.</span></p><p><span style="font-weight: 400;">Llama 3 demonstrates superior capabilities across various AI benchmarks. The 8B model excels in human-like evaluations, while the 70B model shows outstanding performance in complex reasoning and mathematical tasks, surpassing other leading models such as Gemini Pro 1.5 and Claude 3 Sonnet and 8B outperforming Gemma 7B and Mistral 7B instruct.</span></p><p><span style="font-weight: 400;">The LMSYS Chatbot Arena uses over 800,000 human comparisons to rank large language models (LLMs) on an Elo scale. Currently, the Llama 3 70B-Instruct model holds the 6th position on this leaderboard, demonstrating its robust performance in real-world conversational settings. According to Meta鈥檚 Llama 3 release blog, the 8B model is the best in its category</span></p><p><span style="font-weight: 400;">Despite its high performance, Llama 3 is available at a fraction of the cost of comparable models like GPT-4. This price advantage, coupled with its open-source model, democratizes access to cutting-edge AI technologies, making it a financially attractive option for developers and businesses.</span></p> </div> </div> <div class="elementor-element elementor-element-bf0b0a0 elementor-widget elementor-widget-heading" data-id="bf0b0a0" data-element_type="widget" data-widget_type="heading.default"> <div class="elementor-widget-container"> <h2 class="elementor-heading-title elementor-size-default">Let's Implement the Topic Model on ML Research Papers!</h2> </div> </div> <div class="elementor-element elementor-element-490c161 elementor-widget elementor-widget-text-editor" data-id="490c161" data-element_type="widget" data-widget_type="text-editor.default"> <div class="elementor-widget-container"> <p><span style="font-weight: 400;">We will start by installing many packages that we are going to use throughout this Tutorial:</span></p> </div> </div> <div class="elementor-element elementor-element-3016ddb elementor-widget elementor-widget-code-highlight" data-id="3016ddb" data-element_type="widget" data-widget_type="code-highlight.default"> <div class="elementor-widget-container"> <div class="prismjs-default copy-to-clipboard"> <pre data-line="" class="highlight-height language-javascript line-numbers"> <code readonly="true" class="language-javascript"> <xmp>%%capture # BERTopic + llama-cpp-python !CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install llama-cpp-python !pip install bertopic datasets # DataMapPlot !git clone https://github.com/TutteInstitute/datamapplot.git !pip install datamapplot/. # GPU-accelerated HDBSCAN + UMAP !pip install cudf-cu12 dask-cudf-cu12 --extra-index-url=https://pypi.nvidia.com !pip install cuml-cu12 --extra-index-url=https://pypi.nvidia.com !pip install cugraph-cu12 --extra-index-url=https://pypi.nvidia.com !pip install cupy-cuda12x -f https://pip.cupy.dev/aarch64</xmp> </code> </pre> </div> </div> </div> <div class="elementor-element elementor-element-2387169 elementor-widget elementor-widget-text-editor" data-id="2387169" data-element_type="widget" data-widget_type="text-editor.default"> <div class="elementor-widget-container"> <p><span style="font-weight: 400;">Now we will import all necessary modules that are going to be used to create a topic model</span></p> </div> </div> <div class="elementor-element elementor-element-928fce2 elementor-widget elementor-widget-code-highlight" data-id="928fce2" data-element_type="widget" data-widget_type="code-highlight.default"> <div class="elementor-widget-container"> <div class="prismjs-default copy-to-clipboard"> <pre data-line="" class="highlight-height language-javascript line-numbers"> <code readonly="true" class="language-javascript"> <xmp>from datasets import load_dataset # For loading the dataset from Hugging Face from huggingface_hub import hf_hub_download # For loading the model instance from Hugging Face import os # For creating system directories from llama_cpp import Llama # LLM Wrapper from bertopic.representation import KeyBERTInspired, LlamaCPP # Representation Comparison from sentence_transformers import SentenceTransformer # Embedding Model Wrapper from cuml.manifold import UMAP # For UMAP dimensionality reduction from cuml.cluster import HDBSCAN # For clustering with HDBSCAN from bertopic import BERTopic # For topic modeling with BERTopic import PIL # For image processing import numpy as np # For numerical computations import requests # For making HTTP requests import datamapplot # For data visualization import re # For regular expressions </xmp> </code> </pre> </div> </div> </div> <div class="elementor-element elementor-element-611b4ba elementor-widget elementor-widget-text-editor" data-id="611b4ba" data-element_type="widget" data-widget_type="text-editor.default"> <div class="elementor-widget-container"> <p><span style="font-weight: 400;">Use topic modeling on several ArXiv dataset鈥檚 abstracts and for the model, we make use of the </span><span style="font-weight: 400;">llama-3-<em><strong><a href="https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct-GGUF" target="_blank" rel="noopener">8b-instruct-GGUF</a></strong></em></span><span style="font-weight: 400;"> format together with llama-cpp-python from Huggingface.聽</span></p> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-4ef8c94 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="4ef8c94" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-22a90eb" data-id="22a90eb" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-2ce0f3f elementor-widget elementor-widget-code-highlight" data-id="2ce0f3f" data-element_type="widget" data-widget_type="code-highlight.default"> <div class="elementor-widget-container"> <div class="prismjs-default copy-to-clipboard"> <pre data-line="" class="highlight-height language-javascript line-numbers"> <code readonly="true" class="language-javascript"> <xmp>model_name_or_path = "NousResearch/Meta-Llama-3-8B-Instruct-GGUF" model_basename = "Meta-Llama-3-8B-Instruct-Q4_K_M.gguf" os.makedirs('model', exist_ok=True) path = "model" # Downloading the model from repo model_path = hf_hub_download(repo_id=model_name_or_path, filename=model_basename, cache_dir=path) # ArXiv ML Documents docs = load_dataset("CShorten/CORD19-init-160k")["train"]["abstract"] # CShorten/1000-CORD19-Papers-Text # Use llama.cpp to load in a Quantized LLM llm = Llama(model_path=model_path, n_gpu_layers=-1, n_ctx=4096, stop=["Q:", "\n"],verbose=False) </xmp> </code> </pre> </div> </div> </div> <div class="elementor-element elementor-element-606f6f3 elementor-widget elementor-widget-text-editor" data-id="606f6f3" data-element_type="widget" data-widget_type="text-editor.default"> <div class="elementor-widget-container"> <p><span style="font-weight: 400;">Once the LLM is loaded, we add KeyBERTInspired representations to compare the LLM representations with and then convert documents into numerical representations using sentence transformers, which are models optimized for semantic similarity. These models are highly beneficial for clustering tasks and can efficiently create document or sentence embeddings. Pre-calculating embeddings for each document accelerates exploration steps and facilitates quick iteration over BERTopic’s hyperparameters when needed. In this case, we are using the BAAI/bge-small-en-v1.5 model.</span></p><p><span style="font-weight: 400;">After obtaining our numerical document representations, we need to reduce their dimensionality. Cluster models often struggle with high-dimensional data due to the curse of dimensionality. While methods like PCA are effective at reducing dimensionality, the default choice in BERTopic is UMAP. UMAP is a technique that preserves both the local and global structure of a dataset while reducing its dimensionality. Preserving this structure is crucial as it contains information essential for creating clusters of semantically similar documents.</span></p><p><span style="font-weight: 400;">We then define the models for UMAP and HDBSCAN.</span></p> </div> </div> <div class="elementor-element elementor-element-6a9a81d elementor-widget elementor-widget-code-highlight" data-id="6a9a81d" data-element_type="widget" data-widget_type="code-highlight.default"> <div class="elementor-widget-container"> <div class="prismjs-default copy-to-clipboard"> <pre data-line="" class="highlight-height language-javascript line-numbers"> <code readonly="true" class="language-javascript"> <xmp>prompt = """ Q: I have a topic that contains the following documents: [DOCUMENTS] The topic is described by the following keywords: '[KEYWORDS]'. Based on the above information, can you give a short label of the topic of at most 5 words? A: """ representation_model = { "KeyBERT": KeyBERTInspired(), "LLM": LlamaCPP(llm, prompt=prompt), } # Pre-calculate embeddings embedding_model = SentenceTransformer("BAAI/bge-small-en-v1.5") embeddings = embedding_model.encode(docs, show_progress_bar=True) # Pre-reduce embeddings for visualization purposes reduced_embeddings = UMAP(n_neighbors=15, n_components=2, min_dist=0.0, metric='cosine', random_state=42).fit_transform(embeddings) # Define sub-models umap_model = UMAP(n_neighbors=15, n_components=5, min_dist=0.0, metric='cosine', random_state=42) hdbscan_model = HDBSCAN(min_cluster_size=400, metric='euclidean', cluster_selection_method='eom', prediction_data=True)</xmp> </code> </pre> </div> </div> </div> <div class="elementor-element elementor-element-cf86c1d elementor-widget elementor-widget-text-editor" data-id="cf86c1d" data-element_type="widget" data-widget_type="text-editor.default"> <div class="elementor-widget-container"> <p><span style="font-weight: 400;">Now that we have our models prepared, we can start training our topic model by using BERTopic with sub-model for Extract embeddings through SentenceTransformer, Reducing dimensionality through UMAP, cluster-reduced embeddings through HDBSCAN, and fine-tuning topic representations through Llama-3-8b-instruct and KeyBERTInspired. To get more details, check out this </span><strong><em><a href="https://maartengr.github.io/BERTopic/algorithm/algorithm.html#code-overview" target="_blank" rel="noopener">link</a>.</em></strong></p> </div> </div> <div class="elementor-element elementor-element-78b9894 elementor-widget elementor-widget-code-highlight" data-id="78b9894" data-element_type="widget" data-widget_type="code-highlight.default"> <div class="elementor-widget-container"> <div class="prismjs-default copy-to-clipboard"> <pre data-line="" class="highlight-height language-javascript line-numbers"> <code readonly="true" class="language-javascript"> <xmp>topic_model = BERTopic( # Sub-models embedding_model=embedding_model, umap_model=umap_model, hdbscan_model=hdbscan_model, representation_model=representation_model, # Hyperparameters top_n_words=10, verbose=True ) # Train model topics, probs = topic_model.fit_transform(docs, embeddings) # Show topics topic_model.get_topic_info() </xmp> </code> </pre> </div> </div> </div> <div class="elementor-element elementor-element-5681626 elementor-widget elementor-widget-text-editor" data-id="5681626" data-element_type="widget" data-widget_type="text-editor.default"> <div class="elementor-widget-container"> <p><span style="font-weight: 400;">After running .fit_transform, let鈥檚 see what kind of topics we get in visualization.</span></p> </div> </div> <div class="elementor-element elementor-element-8c67956 elementor-widget elementor-widget-code-highlight" data-id="8c67956" data-element_type="widget" data-widget_type="code-highlight.default"> <div class="elementor-widget-container"> <div class="prismjs-default copy-to-clipboard"> <pre data-line="" class="highlight-height language-javascript line-numbers"> <code readonly="true" class="language-javascript"> <xmp># Prepare logo bertopic_logo_response = requests.get( "https://raw.githubusercontent.com/MaartenGr/BERTopic/master/images/logo.png", stream=True, headers={'User-Agent': 'My User Agent 1.0'} ) bertopic_logo = np.asarray(PIL.Image.open(bertopic_logo_response.raw)) # Create a label for each document llm_labels = [re.sub(r'\W+', ' ', label[0][0].split("\n")[0].replace('"', '')) for label in topic_model.get_topics(full=True)["LLM"].values()] llm_labels = [label if label else "Unlabelled" for label in llm_labels] all_labels = [llm_labels[topic+topic_model._outliers] if topic != -1 else "Unlabelled" for topic in topics] # Run the visualization datamapplot.create_plot( reduced_embeddings, all_labels, label_font_size=11, title="ArXiv - BERTopic", sub_title="Topics labeled with `llama-3-8b-instruct`", label_wrap_width=20, use_medoids=True, logo=bertopic_logo, logo_width=0.16 ); </xmp> </code> </pre> </div> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-84090c7 elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="84090c7" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-38dc5de" data-id="38dc5de" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-e993254 elementor-widget elementor-widget-image" data-id="e993254" data-element_type="widget" data-widget_type="image.default"> <div class="elementor-widget-container"> <img fetchpriority="high" decoding="async" width="976" height="1024" src="data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%20976%201024'%3E%3C/svg%3E" class="attachment-large size-large wp-image-992403" alt="Representational image for the blog on Llama 3." data-lazy-srcset="https://xcelore.com/wp-content/uploads/2024/05/image-976x1024.png 976w, https://xcelore.com/wp-content/uploads/2024/05/image-286x300.png 286w, https://xcelore.com/wp-content/uploads/2024/05/image-768x805.png 768w, https://xcelore.com/wp-content/uploads/2024/05/image.png 1211w" data-lazy-sizes="(max-width: 976px) 100vw, 976px" data-lazy-src="https://xcelore.com/wp-content/uploads/2024/05/image-976x1024.png" /><noscript><img fetchpriority="high" decoding="async" width="976" height="1024" src="https://xcelore.com/wp-content/uploads/2024/05/image-976x1024.png" class="attachment-large size-large wp-image-992403" alt="Representational image for the blog on Llama 3." srcset="https://xcelore.com/wp-content/uploads/2024/05/image-976x1024.png 976w, https://xcelore.com/wp-content/uploads/2024/05/image-286x300.png 286w, https://xcelore.com/wp-content/uploads/2024/05/image-768x805.png 768w, https://xcelore.com/wp-content/uploads/2024/05/image.png 1211w" sizes="(max-width: 976px) 100vw, 976px" /></noscript> </div> </div> <div class="elementor-element elementor-element-a27ff54 elementor-widget elementor-widget-text-editor" data-id="a27ff54" data-element_type="widget" data-widget_type="text-editor.default"> <div class="elementor-widget-container"> <p><span style="font-weight: 400;">In this visualization, some unwanted labels have come up, to avoid you have to test the sub-model with different hyperparameters and choose appropriate ones over your dataset</span></p> <p><span style="font-weight: 400;">Furthermore, I would suggest you explore the original </span><em><strong><a href="https://maartengr.github.io/BERTopic/" target="_blank" rel="noopener">documentation</a></strong></em><span style="font-weight: 400;"> and another </span><em><strong><a href="https://www.pinecone.io/learn/bertopic/" target="_blank" rel="noopener">article</a></strong></em><span style="font-weight: 400;"> to understand it better.聽</span></p> </div> </div> <div class="elementor-element elementor-element-3f411df elementor-widget elementor-widget-heading" data-id="3f411df" data-element_type="widget" data-widget_type="heading.default"> <div class="elementor-widget-container"> <h2 class="elementor-heading-title elementor-size-default">Conclusion</h2> </div> </div> <div class="elementor-element elementor-element-272e6ac elementor-widget elementor-widget-text-editor" data-id="272e6ac" data-element_type="widget" data-widget_type="text-editor.default"> <div class="elementor-widget-container"> <p><span style="font-weight: 400;">The article explores topic modelling using advanced algorithms like BERTopic and Llama-3, highlighting their significance in managing vast amounts of unstructured text data. It explains the challenges faced by traditional topic modeling methods and introduces BERTopic as a robust algorithm leveraging Transformer models for clustering coherent topics. Llama-3, a next-gen large language model, is also introduced for its superior performance and capabilities.</span></p> <p><span style="font-weight: 400;">The implementation process involves installing necessary packages, loading datasets, utilizing pre-trained models, generating embeddings, reducing dimensionality, clustering documents, and fine-tuning topic representations. The article concludes with a visualization using DataMapPlot to showcase clusters of similar documents labelled with topics derived from Llama-3-8b-instruct. Overall, these advancements empower businesses and researchers to extract meaningful insights from textual data efficiently, aiding in decision-making and knowledge discovery.</span></p> </div> </div> </div> </div> </div> </section> <section class="elementor-section elementor-top-section elementor-element elementor-element-6bb8f9c elementor-section-boxed elementor-section-height-default elementor-section-height-default" data-id="6bb8f9c" data-element_type="section"> <div class="elementor-container elementor-column-gap-default"> <div class="elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-810b3c5" data-id="810b3c5" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="vamtam-has-theme-widget-styles elementor-element elementor-element-a2df019 elementor-align-center elementor-widget elementor-widget-button" data-id="a2df019" data-element_type="widget" data-widget_type="button.default"> <div 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elementor-icon-list--layout-traditional elementor-list-item-link-full_width elementor-widget elementor-widget-icon-list" data-id="8173d70" data-element_type="widget" data-widget_type="icon-list.default"> <div class="elementor-widget-container"> <ul class="elementor-icon-list-items"> <li class="elementor-icon-list-item"> <span class="elementor-icon-list-icon"> <i aria-hidden="true" class="vamtamtheme- vamtam-theme-check-box"></i> </span> <span class="elementor-icon-list-text">Expert Collaboration</span> </li> <li class="elementor-icon-list-item"> <span class="elementor-icon-list-icon"> <i aria-hidden="true" class="vamtamtheme- vamtam-theme-check-box"></i> </span> <span class="elementor-icon-list-text">Cost Savings</span> </li> <li class="elementor-icon-list-item"> <span class="elementor-icon-list-icon"> <i aria-hidden="true" class="vamtamtheme- vamtam-theme-check-box"></i> </span> <span class="elementor-icon-list-text">Market Access</span> </li> </ul> </div> </div> <div class="elementor-element elementor-element-53c75d0 elementor-widget__width-auto elementor-icon-list--layout-traditional elementor-list-item-link-full_width elementor-widget elementor-widget-icon-list" data-id="53c75d0" data-element_type="widget" data-widget_type="icon-list.default"> <div class="elementor-widget-container"> <ul class="elementor-icon-list-items"> <li class="elementor-icon-list-item"> <span class="elementor-icon-list-icon"> <i aria-hidden="true" class="vamtamtheme- vamtam-theme-check-box"></i> </span> <span class="elementor-icon-list-text">Global Reach</span> </li> <li class="elementor-icon-list-item"> <span class="elementor-icon-list-icon"> <i aria-hidden="true" class="vamtamtheme- vamtam-theme-check-box"></i> </span> <span class="elementor-icon-list-text">Risk Mitigation</span> </li> <li class="elementor-icon-list-item"> <span class="elementor-icon-list-icon"> <i aria-hidden="true" class="vamtamtheme- vamtam-theme-check-box"></i> </span> <span class="elementor-icon-list-text">Cutting-edge Tech Exposure</span> </li> </ul> </div> </div> <div class="elementor-element elementor-element-89f0d85 elementor-widget elementor-widget-heading" data-id="89f0d85" data-element_type="widget" data-widget_type="heading.default"> <div class="elementor-widget-container"> <h6 class="elementor-heading-title elementor-size-default">What happens next?</h6> </div> </div> <section class="elementor-section elementor-inner-section elementor-element elementor-element-5da5408 elementor-section-full_width elementor-section-content-middle elementor-section-height-default elementor-section-height-default" data-id="5da5408" data-element_type="section"> <div class="elementor-container elementor-column-gap-no"> <div class="elementor-column elementor-col-20 elementor-inner-column elementor-element elementor-element-b8421a4 elementor-invisible" data-id="b8421a4" data-element_type="column" data-settings="{"animation":"fadeIn","animation_delay":200}"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-3daf07b elementor-widget__width-initial elementor-widget elementor-widget-heading" data-id="3daf07b" data-element_type="widget" data-widget_type="heading.default"> <div class="elementor-widget-container"> <span class="elementor-heading-title elementor-size-default">1</span> </div> </div> <div class="elementor-element elementor-element-135e6b2 elementor-widget__width-initial elementor-widget elementor-widget-text-editor" data-id="135e6b2" data-element_type="widget" data-widget_type="text-editor.default"> <div class="elementor-widget-container"> We’ll promptly review your inquiry and respond </div> </div> </div> </div> <div class="elementor-column elementor-col-20 elementor-inner-column elementor-element elementor-element-9f0e556" data-id="9f0e556" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-8340781 elementor-widget__width-auto elementor-view-default elementor-invisible elementor-widget elementor-widget-icon" data-id="8340781" data-element_type="widget" data-settings="{"_animation":"fadeInLeft","_animation_delay":300}" data-widget_type="icon.default"> <div class="elementor-widget-container"> <div class="elementor-icon-wrapper"> <div class="elementor-icon"> <svg xmlns="http://www.w3.org/2000/svg" width="12" height="61" viewBox="0 0 12 61"><path d="m.37.6.95-.3 10.21 30.62L1.31 60.57l-.95-.33 10.11-29.32z" fill="" fill-rule="nonzero" fill-opacity=""></path></svg> </div> </div> </div> </div> </div> </div> <div class="elementor-column elementor-col-20 elementor-inner-column elementor-element elementor-element-0dcedc2 elementor-invisible" data-id="0dcedc2" data-element_type="column" data-settings="{"animation":"fadeIn","animation_delay":400}"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-577d59b elementor-widget__width-initial elementor-widget elementor-widget-heading" data-id="577d59b" data-element_type="widget" data-widget_type="heading.default"> <div class="elementor-widget-container"> <span class="elementor-heading-title elementor-size-default">2</span> </div> </div> <div class="elementor-element elementor-element-fba0314 elementor-widget__width-initial elementor-widget elementor-widget-text-editor" data-id="fba0314" data-element_type="widget" data-widget_type="text-editor.default"> <div class="elementor-widget-container"> Our team will guide you through solutions </div> </div> </div> </div> <div class="elementor-column elementor-col-20 elementor-inner-column elementor-element elementor-element-74c0b3b" data-id="74c0b3b" data-element_type="column"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-b5b2107 elementor-widget__width-auto elementor-view-default elementor-invisible elementor-widget elementor-widget-icon" data-id="b5b2107" data-element_type="widget" data-settings="{"_animation":"fadeInLeft","_animation_delay":500}" data-widget_type="icon.default"> <div class="elementor-widget-container"> <div class="elementor-icon-wrapper"> <div class="elementor-icon"> <svg xmlns="http://www.w3.org/2000/svg" width="12" height="61" viewBox="0 0 12 61"><path d="m.37.6.95-.3 10.21 30.62L1.31 60.57l-.95-.33 10.11-29.32z" fill="" fill-rule="nonzero" fill-opacity=""></path></svg> </div> </div> </div> </div> </div> </div> <div class="elementor-column elementor-col-20 elementor-inner-column elementor-element elementor-element-41214d0 elementor-invisible" data-id="41214d0" data-element_type="column" data-settings="{"animation":"fadeIn","animation_delay":600}"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-be6d1e1 elementor-widget__width-initial elementor-widget elementor-widget-heading" data-id="be6d1e1" data-element_type="widget" data-widget_type="heading.default"> <div class="elementor-widget-container"> <span class="elementor-heading-title elementor-size-default">3</span> </div> </div> <div class="elementor-element elementor-element-1bb9b6e elementor-widget__width-initial elementor-widget elementor-widget-text-editor" data-id="1bb9b6e" data-element_type="widget" data-widget_type="text-editor.default"> <div class="elementor-widget-container"> <p>We will share you the proposal & kick off post your approval</p> </div> </div> </div> </div> </div> </section> </div> </div> <div class="elementor-column elementor-col-50 elementor-inner-column elementor-element elementor-element-9a1c1b5" data-id="9a1c1b5" data-element_type="column" data-settings="{"background_background":"classic"}"> <div class="elementor-widget-wrap elementor-element-populated"> <div class="elementor-element elementor-element-45d8aa6 elementor-widget elementor-widget-heading" data-id="45d8aa6" data-element_type="widget" 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data-widget_type="posts.cards"> <div class="elementor-widget-container"> <link rel="stylesheet" href="https://xcelore.com/wp-content/plugins/pro-elements/assets/css/widget-posts.min.css"> <div class="elementor-posts-container elementor-posts elementor-posts--skin-cards elementor-grid"> <article class="elementor-post elementor-grid-item post-993274 post type-post status-publish format-standard has-post-thumbnail hentry category-aiml tag-ai tag-artificial-intelligence tag-automotive"> <div class="elementor-post__card"> <a class="elementor-post__thumbnail__link" href="https://xcelore.com/blog/use-cases-of-ai-in-automotive-industry/" tabindex="-1" ><div class="elementor-post__thumbnail"><img loading="lazy" width="300" height="200" src="data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%20300%20200'%3E%3C/svg%3E" class="attachment-medium size-medium wp-image-993342" alt="AI in automotive industry" decoding="async" 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class="elementor-post__excerpt"> <p>Imagine a world where your car anticipates your needs before you even move. 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{"@context":"https:\/\/schema.org\/","@type":"Article","@id":"https:\/\/xcelore.com\/blog\/topic-modelling-with-quantized-llama-3-bertopic\/#Article","url":"https:\/\/xcelore.com\/blog\/topic-modelling-with-quantized-llama-3-bertopic\/","inLanguage":"en-US","mainEntityOfPage":"https:\/\/xcelore.com\/blog\/topic-modelling-with-quantized-llama-3-bertopic\/","headline":"Topic Modelling With Quantized LLaMA 3 & BERTopic","description":"Today we\u2019re diving into Topic Modelling using a powerful algorithm called BERTopic, and Llama-3, the next generation of Llama, is now available for broad use.","articleBody":"Today we're diving into topic modeling using a powerful algorithm called BERTopic, and Llama-3, the next generation of Llama, is now available for broad use. It features pre-trained and instruction-fine-tuned language models with 8B and 70B parameters, supporting various use cases. We will be using topic modeling for topic extraction using BERTopic and the quantized gguf version of Llama-3-8b-instruct from the dataset, and visualizing it for further understanding.\t\t \t\t\tWhat is Topic Modeling?\t\t \t\tBusinesses need help with vast amounts of unstructured text daily, ranging from customer emails to online reviews. This abundance of textual data necessitates efficient organization and understanding, leading us to rely on topic modeling.\u00a0Topic modeling is a statistical technique using unsupervised machine learning to uncover and showcase thematic structures within textual data. It's crucial in various domains like information retrieval, text mining, modern search systems, and data visualization. These applications enable researchers to explore vast textual datasets efficiently.The complexity of unstructured text data has historically posed challenges, including a lack of labeled data, limited context understanding, and difficulties in topic interpretation. Additionally, traditional topic modeling methods needed more scalability and pre-trained models, further hindering efficient analysis. However, recent advancements in natural language processing (NLP), particularly with the development of large language models (LLMs) like ChatGPT and GPT-4, have revolutionized topic modeling efficiency, especially for smaller datasets.While LLMs offer improved capabilities, directly passing all documents for analysis remains computationally impractical. Here, BERTopic emerges as a potent solution, leveraging Transformer models' language comprehension and C-TF-IDF to create dense clusters of coherent topics. Instead of analyzing each document individually, BERTopic's clusters provide a foundation for LLMs to distill and fine-tune topic representations accurately.By leveraging BERTopic's clustering abilities, we can improve topic modeling with LLMs, optimizing the analysis of large textual datasets without excessive computational requirements.\t\t \t\t\tWhy LLaMA-3 instead of ChatGPT or Gemini? \t\t \t\tThe Llama 3 language model is trained on a large, high-quality pretraining dataset of over 15T tokens from publicly available sources.The dataset is seven times larger than Llama 2 and includes four times more code. Over 5% of the dataset is non-English, covering over 30 languages. To ensure high-quality data, a series of data-filtering pipelines are developed, including heuristic filters, NSFW filters, semantic deduplication approaches, and text classifiers. Experiments are conducted to select the best data mix for Llama 3.Llama 3 demonstrates superior capabilities across various AI benchmarks. The 8B model excels in human-like evaluations, while the 70B model shows outstanding performance in complex reasoning and mathematical tasks, surpassing other leading models such as Gemini Pro 1.5 and Claude 3 Sonnet and 8B outperforming Gemma 7B and Mistral 7B instruct.The LMSYS Chatbot Arena uses over 800,000 human comparisons to rank large language models (LLMs) on an Elo scale. Currently, the Llama 3 70B-Instruct model holds the 6th position on this leaderboard, demonstrating its robust performance in real-world conversational settings. According to Meta\u2019s Llama 3 release blog, the 8B model is the best in its categoryDespite its high performance, Llama 3 is available at a fraction of the cost of comparable models like GPT-4. This price advantage, coupled with its open-source model, democratizes access to cutting-edge AI technologies, making it a financially attractive option for developers and businesses.\t\t \t\t\tLet's Implement the Topic Model on ML Research Papers!\t\t \t\tWe will start by installing many packages that we are going to use throughout this Tutorial:\t\t \t\t\t \t\t\t\t \t\t\t\t\t%%capture # BERTopic + llama-cpp-python !CMAKE_ARGS\"-DLLAMA_CUBLASon\" FORCE_CMAKE1 pip install llama-cpp-python !pip install bertopic datasets # DataMapPlot !git clone https:\/\/github.com\/TutteInstitute\/datamapplot.git !pip install datamapplot\/. # GPU-accelerated HDBSCAN + UMAP !pip install cudf-cu12 dask-cudf-cu12 --extra-index-urlhttps:\/\/pypi.nvidia.com !pip install cuml-cu12 --extra-index-urlhttps:\/\/pypi.nvidia.com !pip install cugraph-cu12 --extra-index-urlhttps:\/\/pypi.nvidia.com !pip install cupy-cuda12x -f https:\/\/pip.cupy.dev\/aarch64 \t\t\t\t \t\t\t \t\tNow we will import all necessary modules that are going to be used to create a topic model\t\t \t\t\t \t\t\t\t \t\t\t\t\tfrom datasets import load_dataset # For loading the dataset from Hugging Face from huggingface_hub import hf_hub_download # For loading the model instance from Hugging Face import os # For creating system directories from llama_cpp import Llama # LLM Wrapper from bertopic.representation import KeyBERTInspired, LlamaCPP # Representation Comparison from sentence_transformers import SentenceTransformer # Embedding Model Wrapper from cuml.manifold import UMAP # For UMAP dimensionality reduction from cuml.cluster import HDBSCAN # For clustering with HDBSCAN from bertopic import BERTopic # For topic modeling with BERTopic import PIL # For image processing import numpy as np # For numerical computations import requests # For making HTTP requests import datamapplot # For data visualization import re # For regular expressions \t\t\t\t \t\t\t \t\tUse topic modeling on several ArXiv dataset\u2019s abstracts and for the model, we make use of the llama-3-8b-instruct-GGUF format together with llama-cpp-python from Huggingface.\u00a0\t\t \t\t\t \t\t\t\t \t\t\t\t\tmodel_name_or_path \"NousResearch\/Meta-Llama-3-8B-Instruct-GGUF\" model_basename \"Meta-Llama-3-8B-Instruct-Q4_K_M.gguf\" os.makedirs('model', exist_okTrue) path \"model\" # Downloading the model from repo model_path hf_hub_download(repo_idmodel_name_or_path, filenamemodel_basename, cache_dirpath) # ArXiv ML Documents docs load_dataset(\"CShorten\/CORD19-init-160k\") # CShorten\/1000-CORD19-Papers-Text # Use llama.cpp to load in a Quantized LLM llm Llama(model_pathmodel_path, n_gpu_layers-1, n_ctx4096, stop,verboseFalse) \t\t\t\t \t\t\t \t\tOnce the LLM is loaded, we add KeyBERTInspired representations to compare the LLM representations with and then convert documents into numerical representations using sentence transformers, which are models optimized for semantic similarity. These models are highly beneficial for clustering tasks and can efficiently create document or sentence embeddings. Pre-calculating embeddings for each document accelerates exploration steps and facilitates quick iteration over BERTopic's hyperparameters when needed. In this case, we are using the BAAI\/bge-small-en-v1.5 model.After obtaining our numerical document representations, we need to reduce their dimensionality. Cluster models often struggle with high-dimensional data due to the curse of dimensionality. While methods like PCA are effective at reducing dimensionality, the default choice in BERTopic is UMAP. UMAP is a technique that preserves both the local and global structure of a dataset while reducing its dimensionality. Preserving this structure is crucial as it contains information essential for creating clusters of semantically similar documents.We then define the models for UMAP and HDBSCAN.\t\t \t\t\t \t\t\t\t \t\t\t\t\tprompt \"\"\" Q: I have a topic that contains the following documents: The topic is described by the following keywords: ''. Based on the above information, can you give a short label of the topic of at most 5 words? A: \"\"\" representation_model { \"KeyBERT\": KeyBERTInspired(), \"LLM\": LlamaCPP(llm, promptprompt), } # Pre-calculate embeddings embedding_model SentenceTransformer(\"BAAI\/bge-small-en-v1.5\") embeddings embedding_model.encode(docs, show_progress_barTrue) # Pre-reduce embeddings for visualization purposes reduced_embeddings UMAP(n_neighbors15, n_components2, min_dist0.0, metric'cosine', random_state42).fit_transform(embeddings) # Define sub-models umap_model UMAP(n_neighbors15, n_components5, min_dist0.0, metric'cosine', random_state42) hdbscan_model HDBSCAN(min_cluster_size400, metric'euclidean', cluster_selection_method'eom', prediction_dataTrue) \t\t\t\t \t\t\t \t\tNow that we have our models prepared, we can start training our topic model by using BERTopic with sub-model for Extract embeddings through SentenceTransformer, Reducing dimensionality through UMAP, cluster-reduced embeddings through HDBSCAN, and fine-tuning topic representations through Llama-3-8b-instruct and KeyBERTInspired. To get more details, check out this link.\t\t \t\t\t \t\t\t\t \t\t\t\t\ttopic_model BERTopic( # Sub-models embedding_modelembedding_model, umap_modelumap_model, hdbscan_modelhdbscan_model, representation_modelrepresentation_model, # Hyperparameters top_n_words10, verboseTrue ) # Train model topics, probs topic_model.fit_transform(docs, embeddings) # Show topics topic_model.get_topic_info() \t\t\t\t \t\t\t \t\tAfter running .fit_transform, let\u2019s see what kind of topics we get in visualization.\t\t \t\t\t \t\t\t\t \t\t\t\t\t# Prepare logo bertopic_logo_response requests.get( \"https:\/\/raw.githubusercontent.com\/MaartenGr\/BERTopic\/master\/images\/logo.png\", streamTrue, headers{'User-Agent': 'My User Agent 1.0'} ) bertopic_logo np.asarray(PIL.Image.open(bertopic_logo_response.raw)) # Create a label for each document llm_labels .split(\"n\").replace('\"', '')) for label in topic_model.get_topics(fullTrue).values()] llm_labels all_labels if topic ! -1 else \"Unlabelled\" for topic in topics] # Run the visualization datamapplot.create_plot( reduced_embeddings, all_labels, label_font_size11, title\"ArXiv - BERTopic\", sub_title\"Topics labeled with `llama-3-8b-instruct`\", label_wrap_width20, use_medoidsTrue, logobertopic_logo, logo_width0.16 ); \t\t\t\t \t\t\t \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t \t\tIn this visualization, some unwanted labels have come up, to avoid you have to test the sub-model with different hyperparameters and choose appropriate ones over your dataset Furthermore, I would suggest you explore the original documentation and another article to understand it better.\u00a0\t\t \t\t\tConclusion\t\t \t\tThe article explores topic modelling using advanced algorithms like BERTopic and Llama-3, highlighting their significance in managing vast amounts of unstructured text data. It explains the challenges faced by traditional topic modeling methods and introduces BERTopic as a robust algorithm leveraging Transformer models for clustering coherent topics. Llama-3, a next-gen large language model, is also introduced for its superior performance and capabilities. The implementation process involves installing necessary packages, loading datasets, utilizing pre-trained models, generating embeddings, reducing dimensionality, clustering documents, and fine-tuning topic representations. The article concludes with a visualization using DataMapPlot to showcase clusters of similar documents labelled with topics derived from Llama-3-8b-instruct. Overall, these advancements empower businesses and researchers to extract meaningful insights from textual data efficiently, aiding in decision-making and knowledge discovery.\t\t \t\t\t \t\t\t\t\t\t\t\t\tKnow more about AI\/ML","keywords":"Artificial Intelligence, BERTopic, Generative AI, llama-3, Machine Learning, Topic modelling, virtual service assistant, Virtual Shopping Assistant, ","datePublished":"2024-05-09T14:56:00+05:30","dateModified":"2024-11-12T15:13:27+05:30","author":{"@type":"Person","name":"Roshan Sinha","url":"https:\/\/xcelore.com\/blog\/author\/roshan-sinha\/","sameAs":[],"image":{"@type":"ImageObject","url":"https:\/\/secure.gravatar.com\/avatar\/1af3e6965bfa3a84471bbdb32e3f2869?s=96&d=mm&r=g","height":96,"width":96}},"editor":{"@type":"Person","name":"Roshan Sinha","url":"https:\/\/xcelore.com\/blog\/author\/roshan-sinha\/","sameAs":[],"image":{"@type":"ImageObject","url":"https:\/\/secure.gravatar.com\/avatar\/1af3e6965bfa3a84471bbdb32e3f2869?s=96&d=mm&r=g","height":96,"width":96}},"publisher":{"@type":"Organization","name":"Xcelore","url":"https:\/\/xcelore.com","logo":{"@type":"ImageObject","url":"https:\/\/xcelore.com\/wp-content\/uploads\/2023\/08\/cropped-cropped-cropped-logo-1.png","width":"350","height":"76"}},"image":[{"@type":"ImageObject","@id":"https:\/\/xcelore.com\/blog\/topic-modelling-with-quantized-llama-3-bertopic\/#primaryimage","url":"https:\/\/xcelore.com\/wp-content\/uploads\/2024\/05\/LLaMA-3.png","width":"1884","height":"1179","caption":"Banner image for blog titles - Topic Modelling With Quantized LLaMA 3 & BERTopic"},{"@type":"ImageObject","url":"https:\/\/xcelore.com\/wp-content\/uploads\/2024\/05\/LLaMA-3-1200x900.png","width":"1200","height":"900","caption":"Banner image for blog titles - Topic Modelling With 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