
* Initial commit of PatchTST model classes Co-authored-by: Phanwadee Sinthong <phsinthong@gmail.com> Co-authored-by: Nam Nguyen <namctin@gmail.com> Co-authored-by: Vijay Ekambaram <vijaykr.e@gmail.com> Co-authored-by: Ngoc Diep Do <55230119+diepi@users.noreply.github.com> Co-authored-by: Wesley Gifford <79663411+wgifford@users.noreply.github.com> * Add PatchTSTForPretraining * update to include classification Co-authored-by: Phanwadee Sinthong <phsinthong@gmail.com> Co-authored-by: Nam Nguyen <namctin@gmail.com> Co-authored-by: Vijay Ekambaram <vijaykr.e@gmail.com> Co-authored-by: Ngoc Diep Do <55230119+diepi@users.noreply.github.com> Co-authored-by: Wesley Gifford <79663411+wgifford@users.noreply.github.com> * clean up auto files * Add PatchTSTForPrediction * Fix relative import * Replace original PatchTSTEncoder with ChannelAttentionPatchTSTEncoder * temporary adding absolute path + add PatchTSTForForecasting class * Update base PatchTSTModel + Unittest * Update ForecastHead to use the config class * edit cv_random_masking, add mask to model output * Update configuration_patchtst.py * add masked_loss to the pretraining * add PatchEmbeddings * Update configuration_patchtst.py * edit loss which considers mask in the pretraining * remove patch_last option * Add commits from internal repo * Update ForecastHead * Add model weight initilization + unittest * Update PatchTST unittest to use local import * PatchTST integration tests for pretraining and prediction * Added PatchTSTForRegression + update unittest to include label generation * Revert unrelated model test file * Combine similar output classes * update PredictionHead * Update configuration_patchtst.py * Add Revin * small edit to PatchTSTModelOutputWithNoAttention * Update modeling_patchtst.py * Updating integration test for forecasting * Fix unittest after class structure changed * docstring updates * change input_size to num_input_channels * more formatting * Remove some unused params * Add a comment for pretrained models * add channel_attention option add channel_attention option and remove unused positional encoders. * Update PatchTST models to use HF's MultiHeadAttention module * Update paper + github urls * Fix hidden_state return value * Update integration test to use PatchTSTForForecasting * Adding dataclass decorator for model output classes * Run fixup script * Rename model repos for integration test * edit argument explanation * change individual option to shared_projection * style * Rename integration test + import cleanup * Fix outpu_hidden_states return value * removed unused mode * added std, mean and nops scaler * add initial distributional loss for predition * fix typo in docs * add generate function * formatting * add num_parallel_samples * Fix a typo * copy weighted_average function, edit PredictionHead * edit PredictionHead * add distribution head to forecasting * formatting * Add generate function for forecasting * Add generate function to prediction task * formatting * use argsort * add past_observed_mask ordering * fix arguments * docs * add back test_model_outputs_equivalence test * formatting * cleanup * formatting * use ACT2CLS * formatting * fix add_start_docstrings decorator * add distribution head and generate function to regression task add distribution head and generate function to regression task. Also made add PatchTSTForForecastingOutput, PatchTSTForRegressionOutput. * add distribution head and generate function to regression task add distribution head and generate function to regression task. Also made add PatchTSTForForecastingOutput, PatchTSTForRegressionOutput. * fix typos * add forecast_masking * fixed tests * use set_seed * fix doc test * formatting * Update docs/source/en/model_doc/patchtst.md Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * better var names * rename PatchTSTTranspose * fix argument names and docs string * remove compute_num_patches and unused class * remove assert * renamed to PatchTSTMasking * use num_labels for classification * use num_labels * use default num_labels from super class * move model_type after docstring * renamed PatchTSTForMaskPretraining * bs -> batch_size * more review fixes * use hidden_state * rename encoder layer and block class * remove commented seed_number * edit docstring * Add docstring * formatting * use past_observed_mask * doc suggestion * make fix-copies * use Args: * add docstring * add docstring * change some variable names and add PatchTST before some class names * formatting * fix argument types * fix tests * change x variable to patch_input * format * formatting * fix-copies * Update tests/models/patchtst/test_modeling_patchtst.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * move loss to forward * Update src/transformers/models/patchtst/modeling_patchtst.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update src/transformers/models/patchtst/modeling_patchtst.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update src/transformers/models/patchtst/modeling_patchtst.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update src/transformers/models/patchtst/modeling_patchtst.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update src/transformers/models/patchtst/modeling_patchtst.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * formatting * fix a bug when pre_norm is set to True * output_hidden_states is set to False as default * set pre_norm=True as default * format docstring * format * output_hidden_states is None by default * add missing docs * better var names * docstring: remove default to False in output_hidden_states * change labels name to target_values in regression task * format * fix tests * change to forecast_mask_ratios and random_mask_ratio * change mask names * change future_values to target_values param in the prediction class * remove nn.Sequential and make PatchTSTBatchNorm class * black * fix argument name for prediction * add output_attentions option * add output_attentions to PatchTSTEncoder * formatting * Add attention output option to all classes * Remove PatchTSTEncoderBlock * create PatchTSTEmbedding class * use config in PatchTSTPatchify * Use config in PatchTSTMasking class * add channel_attn_weights * Add PatchTSTScaler class * add output_attentions arg to test function * format * Update doc with image patchtst.md * fix-copies * rename Forecast <-> Prediction * change name of a few parameters to match with PatchTSMixer. * Remove *ForForecasting class to match with other time series models. * make style * Remove PatchTSTForForecasting in the test * remove PatchTSTForForecastingOutput class * change test_forecast_head to test_prediction_head * style * fix docs * fix tests * change num_labels to num_targets * Remove PatchTSTTranspose * remove arguments in PatchTSTMeanScaler * remove arguments in PatchTSTStdScaler * add config as an argument to all the scaler classes * reformat * Add norm_eps for batchnorm and layernorm * reformat. * reformat * edit docstring * update docstring * change variable name pooling to pooling_type * fix output_hidden_states as tuple * fix bug when calling PatchTSTBatchNorm * change stride to patch_stride * create PatchTSTPositionalEncoding class and restructure the PatchTSTEncoder * formatting * initialize scalers with configs * edit output_hidden_states * style * fix forecast_mask_patches doc string --------- Co-authored-by: Gift Sinthong <gift.sinthong@ibm.com> Co-authored-by: Nam Nguyen <namctin@gmail.com> Co-authored-by: Vijay Ekambaram <vijaykr.e@gmail.com> Co-authored-by: Ngoc Diep Do <55230119+diepi@users.noreply.github.com> Co-authored-by: Wesley Gifford <79663411+wgifford@users.noreply.github.com> Co-authored-by: Wesley M. Gifford <wmgifford@us.ibm.com> Co-authored-by: nnguyen <nnguyen@us.ibm.com> Co-authored-by: Ngoc Diep Do <diiepy@gmail.com> Co-authored-by: Kashif Rasul <kashif.rasul@gmail.com> Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
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🤗 Transformers
State-of-the-art Machine Learning for PyTorch, TensorFlow, and JAX.
🤗 Transformers provides APIs and tools to easily download and train state-of-the-art pretrained models. Using pretrained models can reduce your compute costs, carbon footprint, and save you the time and resources required to train a model from scratch. These models support common tasks in different modalities, such as:
📝 Natural Language Processing: text classification, named entity recognition, question answering, language modeling, summarization, translation, multiple choice, and text generation.
🖼️ Computer Vision: image classification, object detection, and segmentation.
🗣️ Audio: automatic speech recognition and audio classification.
🐙 Multimodal: table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.
🤗 Transformers support framework interoperability between PyTorch, TensorFlow, and JAX. This provides the flexibility to use a different framework at each stage of a model's life; train a model in three lines of code in one framework, and load it for inference in another. Models can also be exported to a format like ONNX and TorchScript for deployment in production environments.
Join the growing community on the Hub, forum, or Discord today!
If you are looking for custom support from the Hugging Face team

Contents
The documentation is organized into five sections:
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GET STARTED provides a quick tour of the library and installation instructions to get up and running.
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TUTORIALS are a great place to start if you're a beginner. This section will help you gain the basic skills you need to start using the library.
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HOW-TO GUIDES show you how to achieve a specific goal, like finetuning a pretrained model for language modeling or how to write and share a custom model.
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CONCEPTUAL GUIDES offers more discussion and explanation of the underlying concepts and ideas behind models, tasks, and the design philosophy of 🤗 Transformers.
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API describes all classes and functions:
- MAIN CLASSES details the most important classes like configuration, model, tokenizer, and pipeline.
- MODELS details the classes and functions related to each model implemented in the library.
- INTERNAL HELPERS details utility classes and functions used internally.
Supported models and frameworks
The table below represents the current support in the library for each of those models, whether they have a Python tokenizer (called "slow"). A "fast" tokenizer backed by the 🤗 Tokenizers library, whether they have support in Jax (via Flax), PyTorch, and/or TensorFlow.