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v4.21.0

huggingface/transformers

版本发布时间: 2022-07-27 21:38:27

huggingface/transformers最新发布版本:v4.47.1(2024-12-17 23:42:54)

TensorFlow XLA Text Generation

The TensorFlow text generation method can now be wrapped with tf.function and compiled to XLA. You should be able to achieve up to 100x speedup this way. See our blog post and our benchmarks. You can also see XLA generation in action in our example notebooks, particularly for summarization and translation.

import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("t5-small")
model = TFAutoModelForSeq2SeqLM.from_pretrained("t5-small")

# Main changes with respect to the original generate workflow: `tf.function` and `pad_to_multiple_of`
xla_generate = tf.function(model.generate, jit_compile=True)
tokenization_kwargs = {"pad_to_multiple_of": 32, "padding": True, "return_tensors": "tf"}

# The first prompt will be slow (compiling), the others will be very fast!
input_prompts = [
    f"translate English to {language}: I have four cats and three dogs."
    for language in ["German", "French", "Romanian"]
]
for input_prompt in input_prompts:
    tokenized_inputs = tokenizer([input_prompt], **tokenization_kwargs)
    generated_text = xla_generate(**tokenized_inputs, max_new_tokens=32)
    print(tokenizer.decode(generated_text[0], skip_special_tokens=True))

New model additions

OwlViT

The OWL-ViT model (short for Vision Transformer for Open-World Localization) was proposed in Simple Open-Vocabulary Object Detection with Vision Transformers by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby. OWL-ViT is an open-vocabulary object detection network trained on a variety of (image, text) pairs. It can be used to query an image with one or multiple text queries to search for and detect target objects described in text.

NLLB

The NLLB model was presented in No Language Left Behind: Scaling Human-Centered Machine Translation by Marta R. Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula, Loic Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, Shannon Spruit, Chau Tran, Pierre Andrews, Necip Fazil Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia Gao, Vedanuj Goswami, Francisco Guzmán, Philipp Koehn, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, and Jeff Wang. No Language Left Behind (NLLB) is a model capable of delivering high-quality translations directly between any pair of 200+ languages — including low-resource languages like Asturian, Luganda, Urdu and more.

MobileViT

The MobileViT model was proposed in MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer by Sachin Mehta and Mohammad Rastegari. MobileViT introduces a new layer that replaces local processing in convolutions with global processing using transformers.

Nezha

The Nezha model was proposed in NEZHA: Neural Contextualized Representation for Chinese Language Understanding by Junqiu Wei et al. NEZHA is a language model based on BERT with a collection of proven improvements, which include Functional Relative Positional Encoding as an effective positional encoding scheme, Whole Word Masking strategy, Mixed Precision Training and the LAMB Optimizer in training the models.

GroupViT

The GroupViT model was proposed in GroupViT: Semantic Segmentation Emerges from Text Supervision by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang. Inspired by CLIP, GroupViT is a vision-language model that can perform zero-shot semantic segmentation on any given vocabulary categories, inspired by CLIP.

MVP

The MVP model was proposed in MVP: Multi-task Supervised Pre-training for Natural Language Generation by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen. MVP is a generative language model, pre-trained on a labeled pre-training corpus from 45 datasets over seven generation tasks. For each task, the model is further pre-trained using specific soft prompts to stimulate the model capacity in performing a specific task.

CodeGen

The CodeGen model was proposed in A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. CodeGen is an autoregressive language model for program synthesis trained sequentially on The Pile, BigQuery, and BigPython.

UL2

The UL2 model was presented in Unifying Language Learning Paradigms by Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Neil Houlsby, Donald Metzler. UL2 is a unified framework for pretraining models that are universally effective across datasets and setups. UL2 uses Mixture-of-Denoisers (MoD), a pre-training objective that combines diverse pre-training paradigms together. UL2 introduces a notion of mode switching, wherein downstream fine-tuning is associated with specific pre-training schemes.

Custom pipelines

This adds the ability to support custom pipelines on the Hub and share it with everyone else. Like the code in the Hub feature for models, tokenizers etc., the user has to add trust_remote_code=True when they want to use it. Apart from this, the best way to get familiar with the feature is to look at the added documentation.

PyTorch to TensorFlow CLI utility

This adds a CLI to convert PT weights into TF weights, validate them, and (optionally) open a PR.

TensorFlow-specific improvements

The following models have been ported to be used in TensorFlow: SegFormer, DeiT, ResNet and RegNet.

Additionally, our TF models now support loading sharded checkpoints:

Flax-specific improvements

The following models have been ported to be used in JAX:

Additionally, our JAX models now support loading sharded checkpoints:

Additional model heads

The following models now have a brand new head for new tasks:

ONNX support

A continued community effort provides ONNX converters for an increasing number of models.

Documentation translation

A community effort aiming to translate the documentation in several languages has been continued.

Portuguese

Spanish

Italian

Improvements and bugfixes

Significant community contributions

The following contributors have made significant changes to the library over the last release:

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