
* init commit * attention arch done except rotary emb * rotary emb done * text encoder working * outputs matching * arch first pass done * make commands done, tests and docs remaining * all tests passed, only docs remaining * docs done * doc-builder fix * convert script removed(not relevant) * minor comments done * added ckpt conversion script * tokenizer done * very minor fix of index.md 2 * mostly make fixup related * all done except fe and rotary emb * very small change * removed unidecode dependency * style changes * tokenizer removed require_backends * added require_inflect to tokenizer tests * removed VOCAB_FILES in tokenizer test * inflect dependency removed * added rotary pos emb cache and simplified the apply method * style * little doc change * more comments * feature extractor added * added processor * auto-regressive config added * added CLVPConditioningEncoder * comments done except the test one * weights added successfull(NOT tested) * tokenizer fix with numbers * generate outputs matching * almost tests passing Integ tests not written * Integ tests added * major CUDA error fixed * docs done * rebase and multiple fixes * fixed rebase overwrites * generate code simplified and tests for AutoRegressive model added * minor changes * refectored gpt2 code in clvp file * weights done and all code refactored * mostly done except the fast_tokenizer * doc test fix * config file's doc fixes * more config fix * more comments * tokenizer comments mostly done * modeling file mostly refactored and can load modules * ClvpEncoder tested * ClvpDecoder, ClvpModel and ClvpForCausalLM tested * integration and all tests passed * more fixes * docs almost done * ckpt conversion refectored * style and some failing tests fix * comments * temporary output fix but test_assisted_decoding_matches_greedy_search test fails * majority changes done * use_cache outputs same now! Along with the asisted_greedy_decoding test fix * more comments * more comments * prepare_inputs_for_generation fixed and _prepare_model_inputs added * style fix * clvp.md change * moved clvpconditionalencoder norms * add model to new index * added tokenizer input_ids_with_special_tokens * small fix * config mostly done * added config-tester and changed conversion script * more comments * comments * style fix * some comments * tokenizer changed back to prev state * small commnets * added output hidden states for the main model * style fix * comments * small change * revert small change * . * Update clvp.md * Update test_modeling_clvp.py * :) * some minor change * new fixes * remove to_dict from FE
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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.
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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.