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dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e\u003ca href=\"https://github.com/NVIDIA-Merlin/Transformers4Rec/\"\u003eTransformers4Rec\u003c/a\u003e\u003c/h1\u003e\u003ca id=\"user-content-transformers4rec\" class=\"anchor\" aria-label=\"Permalink: Transformers4Rec\" href=\"#transformers4rec\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://pypi.python.org/pypi/Transformers4Rec\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/4adb86d5eee55b3d41519e40acf9a00c6df6bb7ef3db79201f41f6dbcaeed7df/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f5472616e73666f726d657273345265633f636f6c6f723d6f72616e6765266c6162656c3d76657273696f6e\" alt=\"PyPI\" data-canonical-src=\"https://img.shields.io/pypi/v/Transformers4Rec?color=orange\u0026amp;label=version\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/NVIDIA-Merlin/Transformers4Rec/blob/stable/LICENSE\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6682e1441bf419c4950f5e0297fa8a2ecdf9d9746aeb97086f75f0a9587dda8d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f4e56494449412d4d65726c696e2f5472616e73666f726d65727334526563\" alt=\"LICENSE\" data-canonical-src=\"https://img.shields.io/github/license/NVIDIA-Merlin/Transformers4Rec\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://nvidia-merlin.github.io/Transformers4Rec/stable/README.html\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c6ff5992c6aebec30ac94d38e92d15d9a1c178718f0101a35c131519c35ce1d1/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63756d656e746174696f6e2d626c75652e737667\" alt=\"Documentation\" data-canonical-src=\"https://img.shields.io/badge/documentation-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTransformers4Rec is a flexible and efficient library for sequential and session-based recommendation and can work with PyTorch.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe library works as a bridge between natural language processing (NLP) and recommender systems (RecSys) by integrating with one of the most popular NLP frameworks, \u003ca href=\"https://github.com/huggingface/transformers\"\u003eHugging Face Transformers\u003c/a\u003e (HF).\nTransformers4Rec makes state-of-the-art transformer architectures available for RecSys researchers and industry practitioners.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe following figure shows the use of the library in a recommender system.\nInput data is typically a sequence of interactions such as items that are browsed in a web session or items put in a cart.\nThe library helps you process and model the interactions so that you can output better recommendations for the next item.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/NVIDIA-Merlin/Transformers4Rec/blob/refs/heads/main/_images/sequential_rec.png\"\u003e\u003cimg src=\"/NVIDIA-Merlin/Transformers4Rec/raw/refs/heads/main/_images/sequential_rec.png\" alt=\"Sequential and Session-based recommendation with Transformers4Rec\" style=\"width: 800px; max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv dir=\"auto\"\u003e\n Sequential and Session-based recommendation with Transformers4Rec\n\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTraditional recommendation algorithms usually ignore the temporal dynamics and the sequence of interactions when trying to model user behavior.\nGenerally, the next user interaction is related to the sequence of the user's previous choices.\nIn some cases, it might be a repeated purchase or song play.\nUser interests can also suffer from interest drift because preferences can change over time.\nThose challenges are addressed by the \u003cstrong\u003esequential recommendation\u003c/strong\u003e task.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eA special use case of sequential-recommendation is the \u003cstrong\u003esession-based recommendation\u003c/strong\u003e task where you only have access to the short sequence of interactions within the current session.\nThis is very common in online services like e-commerce, news, and media portals where the user might choose to browse anonymously due to GDPR compliance that restricts collecting cookies or because the user is new to the site.\nThis task is also relevant for scenarios where the users' interests change a lot over time depending on the user context or intent.\nIn this case, leveraging the interactions for the current session is more promising than old interactions to provide relevant recommendations.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo deal with sequential and session-based recommendation, many sequence learning algorithms previously applied in machine learning and NLP research have been explored for RecSys based on k-Nearest Neighbors, Frequent Pattern Mining, Hidden Markov Models, Recurrent Neural Networks, and more recently neural architectures using the Self-Attention Mechanism and transformer architectures.\nUnlike Transformers4Rec, these frameworks only accept sequences of item IDs as input and do not provide a modularized, scalable implementation for production usage.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBenefits of Transformers4Rec\u003c/h2\u003e\u003ca id=\"user-content-benefits-of-transformers4rec\" class=\"anchor\" aria-label=\"Permalink: Benefits of Transformers4Rec\" href=\"#benefits-of-transformers4rec\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTransformers4Rec offers the following benefits:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFlexibility\u003c/strong\u003e: Transformers4Rec provides modularized building blocks that are configurable and compatible with standard PyTorch modules.\nThis building-block design enables you to create custom architectures with multiple towers, multiple heads/tasks, and losses.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAccess to HF Transformers\u003c/strong\u003e: More than 64 different Transformer architectures can be used to evaluate your sequential and session-based recommendation task as a result of the \u003ca href=\"https://github.com/huggingface/transformers\"\u003eHugging Face Transformers\u003c/a\u003e integration.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSupport for multiple input features\u003c/strong\u003e: HF Transformers only support sequences of token IDs as input because it was originally designed for NLP.\nTransformers4Rec enables you to use other types of sequential tabular data as input with HF transformers due to the rich features that are available in RecSys datasets.\nTransformers4Rec uses a schema to configure the input features and automatically creates the necessary layers, such as embedding tables, projection layers, and output layers based on the target without requiring code changes to include new features.\nYou can normalize and combine interaction and sequence-level input features in configurable ways.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSeamless preprocessing and feature engineering\u003c/strong\u003e: As part of the Merlin ecosystem, Transformers4Rec is integrated with \u003ca href=\"https://github.com/NVIDIA-Merlin/NVTabular\"\u003eNVTabular\u003c/a\u003e and \u003ca href=\"https://github.com/triton-inference-server/server\"\u003eTriton Inference Server\u003c/a\u003e.\nThese components enable you to build a fully GPU-accelerated pipeline for sequential and session-based recommendation.\nNVTabular has common preprocessing operations for session-based recommendation and exports a dataset schema.\nThe schema is compatible with Transformers4Rec so that input features can be configured automatically.\nYou can export your trained models to serve with Triton Inference Server in a single pipeline that includes online feature preprocessing and model inference.\nFor more information, refer to \u003ca href=\"https://nvidia-merlin.github.io/Transformers4Rec/stable/pipeline.html\" rel=\"nofollow\"\u003eEnd-to-end pipeline with NVIDIA Merlin\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/NVIDIA-Merlin/Transformers4Rec/blob/refs/heads/main/_images/pipeline.png\"\u003e\u003cimg src=\"/NVIDIA-Merlin/Transformers4Rec/raw/refs/heads/main/_images/pipeline.png\" alt=\"GPU-accelerated Sequential and Session-based recommendation\" style=\"width: 600px; max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv dir=\"auto\"\u003e\n GPU-accelerated pipeline for Sequential and Session-based recommendation using NVIDIA Merlin components\n\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTransformers4Rec Achievements\u003c/h2\u003e\u003ca id=\"user-content-transformers4rec-achievements\" class=\"anchor\" aria-label=\"Permalink: Transformers4Rec Achievements\" href=\"#transformers4rec-achievements\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTransformers4Rec recently won two session-based recommendation competitions: \u003ca href=\"https://developer.nvidia.com/blog/how-to-build-a-winning-deep-learning-powered-recommender-system-part-3/\" rel=\"nofollow\"\u003eWSDM WebTour Workshop Challenge 2021 (organized by Booking.com)\u003c/a\u003e and \u003ca href=\"https://medium.com/nvidia-merlin/winning-the-sigir-ecommerce-challenge-on-session-based-recommendation-with-transformers-v2-793f6fac2994\" rel=\"nofollow\"\u003eSIGIR eCommerce Workshop Data Challenge 2021 (organized by Coveo)\u003c/a\u003e.\nThe library provides higher accuracy for session-based recommendation than baseline algorithms and we performed extensive empirical analysis about the accuracy.\nThese observations are published in our \u003ca href=\"https://dl.acm.org/doi/10.1145/3460231.3474255\" rel=\"nofollow\"\u003eACM RecSys'21 paper\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSample Code: Defining and Training the Model\u003c/h2\u003e\u003ca id=\"user-content-sample-code-defining-and-training-the-model\" class=\"anchor\" aria-label=\"Permalink: Sample Code: Defining and Training the Model\" href=\"#sample-code-defining-and-training-the-model\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTraining a model with Transformers4Rec typically requires performing the following high-level steps:\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eProvide the \u003ca href=\"https://nvidia-merlin.github.io/Transformers4Rec/stable/api/merlin_standard_lib.schema.html#merlin_standard_lib.schema.schema.Schema\" rel=\"nofollow\"\u003eschema\u003c/a\u003e and construct an input-module.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf you encounter session-based recommendation issues, you typically want to use the\n\u003ca href=\"https://nvidia-merlin.github.io/Transformers4Rec/stable/api/transformers4rec.torch.features.html#transformers4rec.torch.features.sequence.TabularSequenceFeatures\" rel=\"nofollow\"\u003eTabularSequenceFeatures\u003c/a\u003e\nclass because it merges context features with sequential features.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eProvide the prediction-tasks.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe tasks that are provided right out of the box are available from our \u003ca href=\"https://nvidia-merlin.github.io/Transformers4Rec/stable/api/transformers4rec.torch.model.html#module-transformers4rec.torch.model.prediction_task\" rel=\"nofollow\"\u003eAPI documentation\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eConstruct a transformer-body and convert this into a model.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003eThe following code sample shows how to define and train an XLNet model with PyTorch for next-item prediction task:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from transformers4rec import torch as tr\nfrom transformers4rec.torch.ranking_metric import NDCGAt, RecallAt\n\n# Create a schema or read one from disk: tr.Schema().from_json(SCHEMA_PATH).\nschema: tr.Schema = tr.data.tabular_sequence_testing_data.schema\n\nmax_sequence_length, d_model = 20, 64\n\n# Define the input module to process the tabular input features.\ninput_module = tr.TabularSequenceFeatures.from_schema(\n schema,\n max_sequence_length=max_sequence_length,\n continuous_projection=d_model,\n aggregation=\u0026quot;concat\u0026quot;,\n masking=\u0026quot;causal\u0026quot;,\n)\n\n# Define a transformer-config like the XLNet architecture.\ntransformer_config = tr.XLNetConfig.build(\n d_model=d_model, n_head=4, n_layer=2, total_seq_length=max_sequence_length\n)\n\n# Define the model block including: inputs, masking, projection and transformer block.\nbody = tr.SequentialBlock(\n input_module,\n tr.MLPBlock([d_model]),\n tr.TransformerBlock(transformer_config, masking=input_module.masking)\n)\n\n# Define the evaluation top-N metrics and the cut-offs\nmetrics = [NDCGAt(top_ks=[20, 40], labels_onehot=True),\n RecallAt(top_ks=[20, 40], labels_onehot=True)]\n\n# Define a head with NextItemPredictionTask.\nhead = tr.Head(\n body,\n tr.NextItemPredictionTask(weight_tying=True, metrics=metrics),\n inputs=input_module,\n)\n\n# Get the end-to-end Model class.\nmodel = tr.Model(head)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etransformers4rec\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etr\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etransformers4rec\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eranking_metric\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eNDCGAt\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eRecallAt\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# Create a schema or read one from disk: tr.Schema().from_json(SCHEMA_PATH).\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eschema\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003etr\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eSchema\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etr\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etabular_sequence_testing_data\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eschema\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003emax_sequence_length\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ed_model\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e20\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e64\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# Define the input module to process the tabular input features.\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003einput_module\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etr\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eTabularSequenceFeatures\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003efrom_schema\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003eschema\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003emax_sequence_length\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003emax_sequence_length\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003econtinuous_projection\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ed_model\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eaggregation\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"concat\"\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003emasking\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"causal\"\u003c/span\u003e,\n)\n\n\u003cspan class=\"pl-c\"\u003e# Define a transformer-config like the XLNet architecture.\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003etransformer_config\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etr\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eXLNetConfig\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ebuild\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003ed_model\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003ed_model\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003en_head\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e4\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003en_layer\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003etotal_seq_length\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003emax_sequence_length\u003c/span\u003e\n)\n\n\u003cspan class=\"pl-c\"\u003e# Define the model block including: inputs, masking, projection and transformer block.\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ebody\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etr\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eSequentialBlock\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003einput_module\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003etr\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eMLPBlock\u003c/span\u003e([\u003cspan class=\"pl-s1\"\u003ed_model\u003c/span\u003e]),\n \u003cspan class=\"pl-s1\"\u003etr\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eTransformerBlock\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etransformer_config\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emasking\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003einput_module\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emasking\u003c/span\u003e)\n)\n\n\u003cspan class=\"pl-c\"\u003e# Define the evaluation top-N metrics and the cut-offs\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003emetrics\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e [\u003cspan class=\"pl-v\"\u003eNDCGAt\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etop_ks\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e20\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e40\u003c/span\u003e], \u003cspan class=\"pl-s1\"\u003elabels_onehot\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e),\n \u003cspan class=\"pl-v\"\u003eRecallAt\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etop_ks\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e20\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e40\u003c/span\u003e], \u003cspan class=\"pl-s1\"\u003elabels_onehot\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e)]\n\n\u003cspan class=\"pl-c\"\u003e# Define a head with NextItemPredictionTask.\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003ehead\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etr\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eHead\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003ebody\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003etr\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eNextItemPredictionTask\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eweight_tying\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emetrics\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003emetrics\u003c/span\u003e),\n \u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003einput_module\u003c/span\u003e,\n)\n\n\u003cspan class=\"pl-c\"\u003e# Get the end-to-end Model class.\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etr\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eModel\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ehead\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003eYou can modify the preceding code to perform binary classification.\nThe masking in the input module can be set to \u003ccode\u003eNone\u003c/code\u003e instead of \u003ccode\u003ecausal\u003c/code\u003e.\nWhen you define the head, you can replace the \u003ccode\u003eNextItemPredictionTask\u003c/code\u003e\nwith an instance of \u003ccode\u003eBinaryClassificationTask\u003c/code\u003e.\nSee the sample code in the \u003ca href=\"https://nvidia-merlin.github.io/Transformers4Rec/stable/api/transformers4rec.torch.html#transformers4rec.torch.BinaryClassificationTask\" rel=\"nofollow\"\u003eAPI documentation for the class\u003c/a\u003e.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can install Transformers4Rec with Pip, Conda, or run a Docker container.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstalling Transformers4Rec Using Pip\u003c/h3\u003e\u003ca id=\"user-content-installing-transformers4rec-using-pip\" class=\"anchor\" aria-label=\"Permalink: Installing Transformers4Rec Using Pip\" href=\"#installing-transformers4rec-using-pip\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can install Transformers4Rec with the functionality to use the GPU-accelerated Merlin dataloader.\nInstallation with the dataloader is highly recommended for better performance.\nThose components can be installed as optional arguments for the \u003ccode\u003epip install\u003c/code\u003e command.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo install Transformers4Rec using Pip, run the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"pip install transformers4rec[nvtabular]\"\u003e\u003cpre\u003epip install transformers4rec[nvtabular]\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e-\u0026gt; Be aware that installing Transformers4Rec with \u003ccode\u003epip\u003c/code\u003e does not automatically install RAPIDS cuDF.\n-\u0026gt; cuDF is required for GPU-accelerated versions of NVTabular transforms and the Merlin Dataloader.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eInstructions for installing cuDF with pip are available here: \u003ca href=\"https://docs.rapids.ai/install#pip-install\" rel=\"nofollow\"\u003ehttps://docs.rapids.ai/install#pip-install\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"pip install cudf-cu11 dask-cudf-cu11 --extra-index-url=https://pypi.nvidia.com\"\u003e\u003cpre\u003epip install cudf-cu11 dask-cudf-cu11 --extra-index-url=https://pypi.nvidia.com\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstalling Transformers4Rec Using Conda\u003c/h3\u003e\u003ca id=\"user-content-installing-transformers4rec-using-conda\" class=\"anchor\" aria-label=\"Permalink: Installing Transformers4Rec Using Conda\" href=\"#installing-transformers4rec-using-conda\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo install Transformers4Rec using Conda, run the following command with \u003ccode\u003econda\u003c/code\u003e or \u003ccode\u003emamba\u003c/code\u003e to create a new environment.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"mamba create -n transformers4rec-23.04 -c nvidia -c rapidsai -c pytorch -c conda-forge \\\n transformers4rec=23.04 `# NVIDIA Merlin` \\\n nvtabular=23.04 `# NVIDIA Merlin - Used in example notebooks` \\\n python=3.10 `# Compatible Python environment` \\\n cudf=23.02 `# RAPIDS cuDF - GPU accelerated DataFrame` \\\n cudatoolkit=11.8 pytorch-cuda=11.8 `# NVIDIA CUDA version`\"\u003e\u003cpre\u003emamba create -n transformers4rec-23.04 -c nvidia -c rapidsai -c pytorch -c conda-forge \\\n transformers4rec=23.04 \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e NVIDIA Merlin\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e \\\n nvtabular=23.04 \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e NVIDIA Merlin - Used in example notebooks\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e \\\n python=3.10 \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Compatible Python environment\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e \\\n cudf=23.02 \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e RAPIDS cuDF - GPU accelerated DataFrame\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e \\\n cudatoolkit=11.8 pytorch-cuda=11.8 \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e NVIDIA CUDA version\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e`\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstalling Transformers4Rec Using Docker\u003c/h3\u003e\u003ca id=\"user-content-installing-transformers4rec-using-docker\" class=\"anchor\" aria-label=\"Permalink: Installing Transformers4Rec Using Docker\" href=\"#installing-transformers4rec-using-docker\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTransformers4Rec is pre-installed in the \u003ccode\u003emerlin-pytorch\u003c/code\u003e container that is available from the NVIDIA GPU Cloud (NGC) catalog.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eRefer to the \u003ca href=\"https://nvidia-merlin.github.io/Merlin/stable/containers.html\" rel=\"nofollow\"\u003eMerlin Containers\u003c/a\u003e documentation page for information about the Merlin container names, URLs to container images in the catalog, and key Merlin components.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eNotebook Examples and Tutorials\u003c/h2\u003e\u003ca id=\"user-content-notebook-examples-and-tutorials\" class=\"anchor\" aria-label=\"Permalink: Notebook Examples and Tutorials\" href=\"#notebook-examples-and-tutorials\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe \u003ca href=\"https://nvidia-merlin.github.io/Transformers4Rec/stable/pipeline.html\" rel=\"nofollow\"\u003eEnd-to-end pipeline with NVIDIA Merlin\u003c/a\u003e page\nshows how to use Transformers4Rec and other Merlin libraries like NVTabular to build a complete recommender system.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWe have several \u003ca href=\"/NVIDIA-Merlin/Transformers4Rec/blob/refs/heads/main/examples\"\u003eexample\u003c/a\u003e notebooks to help you build a recommender system or integrate Transformers4Rec into your system:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eA getting started example that includes training a session-based model with an XLNET transformer architecture.\u003c/li\u003e\n\u003cli\u003eAn end-to-end example that trains a model and takes the next step to serve inference with Triton Inference Server.\u003c/li\u003e\n\u003cli\u003eAnother end-to-end example that trains and evaluates a session-based model on RNN and also serves inference with Triton Inference Server.\u003c/li\u003e\n\u003cli\u003eA notebook and scripts that reproduce the experiments presented in a paper for RecSys 2021.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFeedback and Support\u003c/h2\u003e\u003ca id=\"user-content-feedback-and-support\" class=\"anchor\" aria-label=\"Permalink: Feedback and Support\" href=\"#feedback-and-support\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you'd like to make direct contributions to Transformers4Rec, refer to \u003ca href=\"/NVIDIA-Merlin/Transformers4Rec/blob/refs/heads/main/CONTRIBUTING.md\"\u003eContributing to Transformers4Rec\u003c/a\u003e. We're particularly interested in contributions or feature requests for our feature engineering and preprocessing operations. To further advance our Merlin roadmap, we encourage you to share all the details regarding your recommender system pipeline by going to \u003ca href=\"https://developer.nvidia.com/merlin-devzone-survey\" rel=\"nofollow\"\u003ehttps://developer.nvidia.com/merlin-devzone-survey\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf you're interested in learning more about how Transformers4Rec works, refer to our\n\u003ca href=\"https://nvidia-merlin.github.io/Transformers4Rec/stable/getting_started.html\" rel=\"nofollow\"\u003eTransformers4Rec documentation\u003c/a\u003e. We also have \u003ca href=\"https://nvidia-merlin.github.io/Transformers4Rec/stable/api/modules.html\" rel=\"nofollow\"\u003eAPI documentation\u003c/a\u003e that outlines the specifics of the available modules and classes within Transformers4Rec.\u003c/p\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"Transformers4Rec","anchor":"transformers4rec","htmlText":"Transformers4Rec"},{"level":2,"text":"Benefits of Transformers4Rec","anchor":"benefits-of-transformers4rec","htmlText":"Benefits of Transformers4Rec"},{"level":2,"text":"Transformers4Rec Achievements","anchor":"transformers4rec-achievements","htmlText":"Transformers4Rec Achievements"},{"level":2,"text":"Sample Code: Defining and Training the Model","anchor":"sample-code-defining-and-training-the-model","htmlText":"Sample Code: Defining and Training the Model"},{"level":2,"text":"Installation","anchor":"installation","htmlText":"Installation"},{"level":3,"text":"Installing Transformers4Rec Using Pip","anchor":"installing-transformers4rec-using-pip","htmlText":"Installing Transformers4Rec Using Pip"},{"level":3,"text":"Installing Transformers4Rec Using Conda","anchor":"installing-transformers4rec-using-conda","htmlText":"Installing Transformers4Rec Using Conda"},{"level":3,"text":"Installing Transformers4Rec Using Docker","anchor":"installing-transformers4rec-using-docker","htmlText":"Installing Transformers4Rec Using Docker"},{"level":2,"text":"Notebook Examples and Tutorials","anchor":"notebook-examples-and-tutorials","htmlText":"Notebook Examples and Tutorials"},{"level":2,"text":"Feedback and Support","anchor":"feedback-and-support","htmlText":"Feedback and Support"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2FNVIDIA-Merlin%2FTransformers4Rec%2Ftree%2Frefs%2Fheads%2Fmain"}},{"displayName":"LICENSE","repoName":"Transformers4Rec","refName":"refs/heads/main","path":"LICENSE","preferredFileType":"license","tabName":"License","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2FNVIDIA-Merlin%2FTransformers4Rec%2Ftree%2Frefs%2Fheads%2Fmain"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-1583894afd38.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-67668e8c2caa.js","githubDevUrl":null,"enabled_features":{"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"copilot_conversational_ux_embedding_update":false,"copilot_smell_icebreaker_ux":true,"copilot_workspace":false,"accessible_code_button":true}}}}</script> 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1.5h-8.5a.75.75 0 0 1 0-1.5ZM2 14a1 1 0 1 1 0-2 1 1 0 0 1 0 2Zm1-6a1 1 0 1 1-2 0 1 1 0 0 1 2 0ZM2 4a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg></button></div><div class="Box-sc-g0xbh4-0 QkQOb js-snippet-clipboard-copy-unpositioned" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto"><a href="https://github.com/NVIDIA-Merlin/Transformers4Rec/">Transformers4Rec</a></h1><a id="user-content-transformers4rec" class="anchor" aria-label="Permalink: Transformers4Rec" href="#transformers4rec"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a href="https://pypi.python.org/pypi/Transformers4Rec" rel="nofollow"><img src="https://camo.githubusercontent.com/4adb86d5eee55b3d41519e40acf9a00c6df6bb7ef3db79201f41f6dbcaeed7df/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f5472616e73666f726d657273345265633f636f6c6f723d6f72616e6765266c6162656c3d76657273696f6e" alt="PyPI" data-canonical-src="https://img.shields.io/pypi/v/Transformers4Rec?color=orange&label=version" style="max-width: 100%;"></a> <a href="https://github.com/NVIDIA-Merlin/Transformers4Rec/blob/stable/LICENSE"><img src="https://camo.githubusercontent.com/6682e1441bf419c4950f5e0297fa8a2ecdf9d9746aeb97086f75f0a9587dda8d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f4e56494449412d4d65726c696e2f5472616e73666f726d65727334526563" alt="LICENSE" data-canonical-src="https://img.shields.io/github/license/NVIDIA-Merlin/Transformers4Rec" style="max-width: 100%;"></a> <a href="https://nvidia-merlin.github.io/Transformers4Rec/stable/README.html" rel="nofollow"><img src="https://camo.githubusercontent.com/c6ff5992c6aebec30ac94d38e92d15d9a1c178718f0101a35c131519c35ce1d1/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63756d656e746174696f6e2d626c75652e737667" alt="Documentation" data-canonical-src="https://img.shields.io/badge/documentation-blue.svg" style="max-width: 100%;"></a></p> <p dir="auto">Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation and can work with PyTorch.</p> <p dir="auto">The library works as a bridge between natural language processing (NLP) and recommender systems (RecSys) by integrating with one of the most popular NLP frameworks, <a href="https://github.com/huggingface/transformers">Hugging Face Transformers</a> (HF). Transformers4Rec makes state-of-the-art transformer architectures available for RecSys researchers and industry practitioners.</p> <p dir="auto">The following figure shows the use of the library in a recommender system. Input data is typically a sequence of interactions such as items that are browsed in a web session or items put in a cart. The library helps you process and model the interactions so that you can output better recommendations for the next item.</p> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="/NVIDIA-Merlin/Transformers4Rec/blob/refs/heads/main/_images/sequential_rec.png"><img src="/NVIDIA-Merlin/Transformers4Rec/raw/refs/heads/main/_images/sequential_rec.png" alt="Sequential and Session-based recommendation with Transformers4Rec" style="width: 800px; max-width: 100%;"></a><br></p> <div dir="auto"> Sequential and Session-based recommendation with Transformers4Rec </div> <p dir="auto">Traditional recommendation algorithms usually ignore the temporal dynamics and the sequence of interactions when trying to model user behavior. Generally, the next user interaction is related to the sequence of the user's previous choices. In some cases, it might be a repeated purchase or song play. User interests can also suffer from interest drift because preferences can change over time. Those challenges are addressed by the <strong>sequential recommendation</strong> task.</p> <p dir="auto">A special use case of sequential-recommendation is the <strong>session-based recommendation</strong> task where you only have access to the short sequence of interactions within the current session. This is very common in online services like e-commerce, news, and media portals where the user might choose to browse anonymously due to GDPR compliance that restricts collecting cookies or because the user is new to the site. This task is also relevant for scenarios where the users' interests change a lot over time depending on the user context or intent. In this case, leveraging the interactions for the current session is more promising than old interactions to provide relevant recommendations.</p> <p dir="auto">To deal with sequential and session-based recommendation, many sequence learning algorithms previously applied in machine learning and NLP research have been explored for RecSys based on k-Nearest Neighbors, Frequent Pattern Mining, Hidden Markov Models, Recurrent Neural Networks, and more recently neural architectures using the Self-Attention Mechanism and transformer architectures. Unlike Transformers4Rec, these frameworks only accept sequences of item IDs as input and do not provide a modularized, scalable implementation for production usage.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Benefits of Transformers4Rec</h2><a id="user-content-benefits-of-transformers4rec" class="anchor" aria-label="Permalink: Benefits of Transformers4Rec" href="#benefits-of-transformers4rec"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Transformers4Rec offers the following benefits:</p> <ul dir="auto"> <li> <p dir="auto"><strong>Flexibility</strong>: Transformers4Rec provides modularized building blocks that are configurable and compatible with standard PyTorch modules. This building-block design enables you to create custom architectures with multiple towers, multiple heads/tasks, and losses.</p> </li> <li> <p dir="auto"><strong>Access to HF Transformers</strong>: More than 64 different Transformer architectures can be used to evaluate your sequential and session-based recommendation task as a result of the <a href="https://github.com/huggingface/transformers">Hugging Face Transformers</a> integration.</p> </li> <li> <p dir="auto"><strong>Support for multiple input features</strong>: HF Transformers only support sequences of token IDs as input because it was originally designed for NLP. Transformers4Rec enables you to use other types of sequential tabular data as input with HF transformers due to the rich features that are available in RecSys datasets. Transformers4Rec uses a schema to configure the input features and automatically creates the necessary layers, such as embedding tables, projection layers, and output layers based on the target without requiring code changes to include new features. You can normalize and combine interaction and sequence-level input features in configurable ways.</p> </li> <li> <p dir="auto"><strong>Seamless preprocessing and feature engineering</strong>: As part of the Merlin ecosystem, Transformers4Rec is integrated with <a href="https://github.com/NVIDIA-Merlin/NVTabular">NVTabular</a> and <a href="https://github.com/triton-inference-server/server">Triton Inference Server</a>. These components enable you to build a fully GPU-accelerated pipeline for sequential and session-based recommendation. NVTabular has common preprocessing operations for session-based recommendation and exports a dataset schema. The schema is compatible with Transformers4Rec so that input features can be configured automatically. You can export your trained models to serve with Triton Inference Server in a single pipeline that includes online feature preprocessing and model inference. For more information, refer to <a href="https://nvidia-merlin.github.io/Transformers4Rec/stable/pipeline.html" rel="nofollow">End-to-end pipeline with NVIDIA Merlin</a>.</p> </li> </ul> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="/NVIDIA-Merlin/Transformers4Rec/blob/refs/heads/main/_images/pipeline.png"><img src="/NVIDIA-Merlin/Transformers4Rec/raw/refs/heads/main/_images/pipeline.png" alt="GPU-accelerated Sequential and Session-based recommendation" style="width: 600px; max-width: 100%;"></a><br></p> <div dir="auto"> GPU-accelerated pipeline for Sequential and Session-based recommendation using NVIDIA Merlin components </div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Transformers4Rec Achievements</h2><a id="user-content-transformers4rec-achievements" class="anchor" aria-label="Permalink: Transformers4Rec Achievements" href="#transformers4rec-achievements"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Transformers4Rec recently won two session-based recommendation competitions: <a href="https://developer.nvidia.com/blog/how-to-build-a-winning-deep-learning-powered-recommender-system-part-3/" rel="nofollow">WSDM WebTour Workshop Challenge 2021 (organized by Booking.com)</a> and <a href="https://medium.com/nvidia-merlin/winning-the-sigir-ecommerce-challenge-on-session-based-recommendation-with-transformers-v2-793f6fac2994" rel="nofollow">SIGIR eCommerce Workshop Data Challenge 2021 (organized by Coveo)</a>. The library provides higher accuracy for session-based recommendation than baseline algorithms and we performed extensive empirical analysis about the accuracy. These observations are published in our <a href="https://dl.acm.org/doi/10.1145/3460231.3474255" rel="nofollow">ACM RecSys'21 paper</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Sample Code: Defining and Training the Model</h2><a id="user-content-sample-code-defining-and-training-the-model" class="anchor" aria-label="Permalink: Sample Code: Defining and Training the Model" href="#sample-code-defining-and-training-the-model"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Training a model with Transformers4Rec typically requires performing the following high-level steps:</p> <ol dir="auto"> <li> <p dir="auto">Provide the <a href="https://nvidia-merlin.github.io/Transformers4Rec/stable/api/merlin_standard_lib.schema.html#merlin_standard_lib.schema.schema.Schema" rel="nofollow">schema</a> and construct an input-module.</p> <p dir="auto">If you encounter session-based recommendation issues, you typically want to use the <a href="https://nvidia-merlin.github.io/Transformers4Rec/stable/api/transformers4rec.torch.features.html#transformers4rec.torch.features.sequence.TabularSequenceFeatures" rel="nofollow">TabularSequenceFeatures</a> class because it merges context features with sequential features.</p> </li> <li> <p dir="auto">Provide the prediction-tasks.</p> <p dir="auto">The tasks that are provided right out of the box are available from our <a href="https://nvidia-merlin.github.io/Transformers4Rec/stable/api/transformers4rec.torch.model.html#module-transformers4rec.torch.model.prediction_task" rel="nofollow">API documentation</a>.</p> </li> <li> <p dir="auto">Construct a transformer-body and convert this into a model.</p> </li> </ol> <p dir="auto">The following code sample shows how to define and train an XLNet model with PyTorch for next-item prediction task:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from transformers4rec import torch as tr from transformers4rec.torch.ranking_metric import NDCGAt, RecallAt # Create a schema or read one from disk: tr.Schema().from_json(SCHEMA_PATH). schema: tr.Schema = tr.data.tabular_sequence_testing_data.schema max_sequence_length, d_model = 20, 64 # Define the input module to process the tabular input features. input_module = tr.TabularSequenceFeatures.from_schema( schema, max_sequence_length=max_sequence_length, continuous_projection=d_model, aggregation="concat", masking="causal", ) # Define a transformer-config like the XLNet architecture. transformer_config = tr.XLNetConfig.build( d_model=d_model, n_head=4, n_layer=2, total_seq_length=max_sequence_length ) # Define the model block including: inputs, masking, projection and transformer block. body = tr.SequentialBlock( input_module, tr.MLPBlock([d_model]), tr.TransformerBlock(transformer_config, masking=input_module.masking) ) # Define the evaluation top-N metrics and the cut-offs metrics = [NDCGAt(top_ks=[20, 40], labels_onehot=True), RecallAt(top_ks=[20, 40], labels_onehot=True)] # Define a head with NextItemPredictionTask. head = tr.Head( body, tr.NextItemPredictionTask(weight_tying=True, metrics=metrics), inputs=input_module, ) # Get the end-to-end Model class. model = tr.Model(head)"><pre><span class="pl-k">from</span> <span class="pl-s1">transformers4rec</span> <span class="pl-k">import</span> <span class="pl-s1">torch</span> <span class="pl-k">as</span> <span class="pl-s1">tr</span> <span class="pl-k">from</span> <span class="pl-s1">transformers4rec</span>.<span class="pl-s1">torch</span>.<span class="pl-s1">ranking_metric</span> <span class="pl-k">import</span> <span class="pl-v">NDCGAt</span>, <span class="pl-v">RecallAt</span> <span class="pl-c"># Create a schema or read one from disk: tr.Schema().from_json(SCHEMA_PATH).</span> <span class="pl-s1">schema</span>: <span class="pl-s1">tr</span>.<span class="pl-v">Schema</span> <span class="pl-c1">=</span> <span class="pl-s1">tr</span>.<span class="pl-s1">data</span>.<span class="pl-s1">tabular_sequence_testing_data</span>.<span class="pl-s1">schema</span> <span class="pl-s1">max_sequence_length</span>, <span class="pl-s1">d_model</span> <span class="pl-c1">=</span> <span class="pl-c1">20</span>, <span class="pl-c1">64</span> <span class="pl-c"># Define the input module to process the tabular input features.</span> <span class="pl-s1">input_module</span> <span class="pl-c1">=</span> <span class="pl-s1">tr</span>.<span class="pl-v">TabularSequenceFeatures</span>.<span class="pl-en">from_schema</span>( <span class="pl-s1">schema</span>, <span class="pl-s1">max_sequence_length</span><span class="pl-c1">=</span><span class="pl-s1">max_sequence_length</span>, <span class="pl-s1">continuous_projection</span><span class="pl-c1">=</span><span class="pl-s1">d_model</span>, <span class="pl-s1">aggregation</span><span class="pl-c1">=</span><span class="pl-s">"concat"</span>, <span class="pl-s1">masking</span><span class="pl-c1">=</span><span class="pl-s">"causal"</span>, ) <span class="pl-c"># Define a transformer-config like the XLNet architecture.</span> <span class="pl-s1">transformer_config</span> <span class="pl-c1">=</span> <span class="pl-s1">tr</span>.<span class="pl-v">XLNetConfig</span>.<span class="pl-en">build</span>( <span class="pl-s1">d_model</span><span class="pl-c1">=</span><span class="pl-s1">d_model</span>, <span class="pl-s1">n_head</span><span class="pl-c1">=</span><span class="pl-c1">4</span>, <span class="pl-s1">n_layer</span><span class="pl-c1">=</span><span class="pl-c1">2</span>, <span class="pl-s1">total_seq_length</span><span class="pl-c1">=</span><span class="pl-s1">max_sequence_length</span> ) <span class="pl-c"># Define the model block including: inputs, masking, projection and transformer block.</span> <span class="pl-s1">body</span> <span class="pl-c1">=</span> <span class="pl-s1">tr</span>.<span class="pl-v">SequentialBlock</span>( <span class="pl-s1">input_module</span>, <span class="pl-s1">tr</span>.<span class="pl-v">MLPBlock</span>([<span class="pl-s1">d_model</span>]), <span class="pl-s1">tr</span>.<span class="pl-v">TransformerBlock</span>(<span class="pl-s1">transformer_config</span>, <span class="pl-s1">masking</span><span class="pl-c1">=</span><span class="pl-s1">input_module</span>.<span class="pl-s1">masking</span>) ) <span class="pl-c"># Define the evaluation top-N metrics and the cut-offs</span> <span class="pl-s1">metrics</span> <span class="pl-c1">=</span> [<span class="pl-v">NDCGAt</span>(<span class="pl-s1">top_ks</span><span class="pl-c1">=</span>[<span class="pl-c1">20</span>, <span class="pl-c1">40</span>], <span class="pl-s1">labels_onehot</span><span class="pl-c1">=</span><span class="pl-c1">True</span>), <span class="pl-v">RecallAt</span>(<span class="pl-s1">top_ks</span><span class="pl-c1">=</span>[<span class="pl-c1">20</span>, <span class="pl-c1">40</span>], <span class="pl-s1">labels_onehot</span><span class="pl-c1">=</span><span class="pl-c1">True</span>)] <span class="pl-c"># Define a head with NextItemPredictionTask.</span> <span class="pl-s1">head</span> <span class="pl-c1">=</span> <span class="pl-s1">tr</span>.<span class="pl-v">Head</span>( <span class="pl-s1">body</span>, <span class="pl-s1">tr</span>.<span class="pl-v">NextItemPredictionTask</span>(<span class="pl-s1">weight_tying</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">metrics</span><span class="pl-c1">=</span><span class="pl-s1">metrics</span>), <span class="pl-s1">inputs</span><span class="pl-c1">=</span><span class="pl-s1">input_module</span>, ) <span class="pl-c"># Get the end-to-end Model class.</span> <span class="pl-s1">model</span> <span class="pl-c1">=</span> <span class="pl-s1">tr</span>.<span class="pl-v">Model</span>(<span class="pl-s1">head</span>)</pre></div> <blockquote> <p dir="auto">You can modify the preceding code to perform binary classification. The masking in the input module can be set to <code>None</code> instead of <code>causal</code>. When you define the head, you can replace the <code>NextItemPredictionTask</code> with an instance of <code>BinaryClassificationTask</code>. See the sample code in the <a href="https://nvidia-merlin.github.io/Transformers4Rec/stable/api/transformers4rec.torch.html#transformers4rec.torch.BinaryClassificationTask" rel="nofollow">API documentation for the class</a>.</p> </blockquote> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Installation</h2><a id="user-content-installation" class="anchor" aria-label="Permalink: Installation" href="#installation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">You can install Transformers4Rec with Pip, Conda, or run a Docker container.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Installing Transformers4Rec Using Pip</h3><a id="user-content-installing-transformers4rec-using-pip" class="anchor" aria-label="Permalink: Installing Transformers4Rec Using Pip" href="#installing-transformers4rec-using-pip"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">You can install Transformers4Rec with the functionality to use the GPU-accelerated Merlin dataloader. Installation with the dataloader is highly recommended for better performance. Those components can be installed as optional arguments for the <code>pip install</code> command.</p> <p dir="auto">To install Transformers4Rec using Pip, run the following command:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pip install transformers4rec[nvtabular]"><pre>pip install transformers4rec[nvtabular]</pre></div> <p dir="auto">-> Be aware that installing Transformers4Rec with <code>pip</code> does not automatically install RAPIDS cuDF. -> cuDF is required for GPU-accelerated versions of NVTabular transforms and the Merlin Dataloader.</p> <p dir="auto">Instructions for installing cuDF with pip are available here: <a href="https://docs.rapids.ai/install#pip-install" rel="nofollow">https://docs.rapids.ai/install#pip-install</a></p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pip install cudf-cu11 dask-cudf-cu11 --extra-index-url=https://pypi.nvidia.com"><pre>pip install cudf-cu11 dask-cudf-cu11 --extra-index-url=https://pypi.nvidia.com</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Installing Transformers4Rec Using Conda</h3><a id="user-content-installing-transformers4rec-using-conda" class="anchor" aria-label="Permalink: Installing Transformers4Rec Using Conda" href="#installing-transformers4rec-using-conda"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">To install Transformers4Rec using Conda, run the following command with <code>conda</code> or <code>mamba</code> to create a new environment.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="mamba create -n transformers4rec-23.04 -c nvidia -c rapidsai -c pytorch -c conda-forge \ transformers4rec=23.04 `# NVIDIA Merlin` \ nvtabular=23.04 `# NVIDIA Merlin - Used in example notebooks` \ python=3.10 `# Compatible Python environment` \ cudf=23.02 `# RAPIDS cuDF - GPU accelerated DataFrame` \ cudatoolkit=11.8 pytorch-cuda=11.8 `# NVIDIA CUDA version`"><pre>mamba create -n transformers4rec-23.04 -c nvidia -c rapidsai -c pytorch -c conda-forge \ transformers4rec=23.04 <span class="pl-s"><span class="pl-pds">`</span><span class="pl-c"><span class="pl-c">#</span> NVIDIA Merlin</span><span class="pl-pds">`</span></span> \ nvtabular=23.04 <span class="pl-s"><span class="pl-pds">`</span><span class="pl-c"><span class="pl-c">#</span> NVIDIA Merlin - Used in example notebooks</span><span class="pl-pds">`</span></span> \ python=3.10 <span class="pl-s"><span class="pl-pds">`</span><span class="pl-c"><span class="pl-c">#</span> Compatible Python environment</span><span class="pl-pds">`</span></span> \ cudf=23.02 <span class="pl-s"><span class="pl-pds">`</span><span class="pl-c"><span class="pl-c">#</span> RAPIDS cuDF - GPU accelerated DataFrame</span><span class="pl-pds">`</span></span> \ cudatoolkit=11.8 pytorch-cuda=11.8 <span class="pl-s"><span class="pl-pds">`</span><span class="pl-c"><span class="pl-c">#</span> NVIDIA CUDA version</span><span class="pl-pds">`</span></span></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Installing Transformers4Rec Using Docker</h3><a id="user-content-installing-transformers4rec-using-docker" class="anchor" aria-label="Permalink: Installing Transformers4Rec Using Docker" href="#installing-transformers4rec-using-docker"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Transformers4Rec is pre-installed in the <code>merlin-pytorch</code> container that is available from the NVIDIA GPU Cloud (NGC) catalog.</p> <p dir="auto">Refer to the <a href="https://nvidia-merlin.github.io/Merlin/stable/containers.html" rel="nofollow">Merlin Containers</a> documentation page for information about the Merlin container names, URLs to container images in the catalog, and key Merlin components.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Notebook Examples and Tutorials</h2><a id="user-content-notebook-examples-and-tutorials" class="anchor" aria-label="Permalink: Notebook Examples and Tutorials" href="#notebook-examples-and-tutorials"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The <a href="https://nvidia-merlin.github.io/Transformers4Rec/stable/pipeline.html" rel="nofollow">End-to-end pipeline with NVIDIA Merlin</a> page shows how to use Transformers4Rec and other Merlin libraries like NVTabular to build a complete recommender system.</p> <p dir="auto">We have several <a href="/NVIDIA-Merlin/Transformers4Rec/blob/refs/heads/main/examples">example</a> notebooks to help you build a recommender system or integrate Transformers4Rec into your system:</p> <ul dir="auto"> <li>A getting started example that includes training a session-based model with an XLNET transformer architecture.</li> <li>An end-to-end example that trains a model and takes the next step to serve inference with Triton Inference Server.</li> <li>Another end-to-end example that trains and evaluates a session-based model on RNN and also serves inference with Triton Inference Server.</li> <li>A notebook and scripts that reproduce the experiments presented in a paper for RecSys 2021.</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Feedback and Support</h2><a id="user-content-feedback-and-support" class="anchor" aria-label="Permalink: Feedback and Support" href="#feedback-and-support"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">If you'd like to make direct contributions to Transformers4Rec, refer to <a href="/NVIDIA-Merlin/Transformers4Rec/blob/refs/heads/main/CONTRIBUTING.md">Contributing to Transformers4Rec</a>. We're particularly interested in contributions or feature requests for our feature engineering and preprocessing operations. To further advance our Merlin roadmap, we encourage you to share all the details regarding your recommender system pipeline by going to <a href="https://developer.nvidia.com/merlin-devzone-survey" rel="nofollow">https://developer.nvidia.com/merlin-devzone-survey</a>.</p> <p dir="auto">If you're interested in learning more about how Transformers4Rec works, refer to our <a href="https://nvidia-merlin.github.io/Transformers4Rec/stable/getting_started.html" rel="nofollow">Transformers4Rec documentation</a>. We also have <a href="https://nvidia-merlin.github.io/Transformers4Rec/stable/api/modules.html" rel="nofollow">API documentation</a> that outlines the specifics of the available modules and classes within Transformers4Rec.</p> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="+0HjtjmmC3hk+IvVAyx8tV4m4o/8emgbZQm9UWduZEEBO1zrlIXll6s8spCIjZYQqaGe4xccOB3oyNoeDTRxEw==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation and works with PyTorch. </p> <div class="my-3 d-flex flex-items-center"> <svg aria-hidden="true" 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