github huggingface/transformers v4.9.0
v4.9.0: TensorFlow examples, CANINE, tokenizer training, ONNX rework
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latest releases: v4.46.2, v4.46.1, v4.46.0...
3 years ago

v4.9.0: TensorFlow examples, CANINE, tokenizer training, ONNX rework

ONNX rework

This version introduces a new package, transformers.onnx, which can be used to export models to ONNX. Contrary to the previous implementation, this approach is meant as an easily extendable package where users may define their own ONNX configurations and export the models they wish to export. 

python -m transformers.onnx --model=bert-base-cased onnx/bert-base-cased/
Validating ONNX model...
        -[✓] ONNX model outputs' name match reference model ({'pooler_output', 'last_hidden_state'}
        - Validating ONNX Model output "last_hidden_state":
                -[✓] (2, 8, 768) matchs (2, 8, 768)
                -[✓] all values close (atol: 0.0001)
        - Validating ONNX Model output "pooler_output":
                -[✓] (2, 768) matchs (2, 768)
                -[✓] all values close (atol: 0.0001)
All good, model saved at: onnx/bert-base-cased/model.onnx
  • [RFC] Laying down building stone for more flexible ONNX export capabilities #11786 (@mfuntowicz)

CANINE model

Four new models are released as part of the CANINE implementation: CanineForSequenceClassification, CanineForMultipleChoice, CanineForTokenClassification and CanineForQuestionAnswering, in PyTorch.

The CANINE model was proposed in CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting. It’s among the first papers that train a Transformer without using an explicit tokenization step (such as Byte Pair Encoding (BPE), WordPiece, or SentencePiece). Instead, the model is trained directly at a Unicode character level. Training at a character level inevitably comes with a longer sequence length, which CANINE solves with an efficient downsampling strategy, before applying a deep Transformer encoder.

Compatible checkpoints can be found on the Hub: https://huggingface.co/models?filter=canine

Tokenizer training

This version introduces a new method to train a tokenizer from scratch based off of an existing tokenizer configuration. 

from datasets import load_dataset
from transformers import AutoTokenizer

dataset = load_dataset("wikitext", name="wikitext-2-raw-v1", split="train")
# We train on batch of texts, 1000 at a time here.
batch_size = 1000
corpus = (dataset[i : i + batch_size]["text"] for i in range(0, len(dataset), batch_size))

tokenizer = AutoTokenizer.from_pretrained("gpt2")
new_tokenizer = tokenizer.train_new_from_iterator(corpus, vocab_size=20000)
  • Easily train a new fast tokenizer from a given one - tackle the special tokens format (str or AddedToken) #12420 (@SaulLu)
  • Easily train a new fast tokenizer from a given one #12361 (@sgugger)

TensorFlow examples

The TFTrainer is now entering deprecation - and it is replaced by Keras. With version v4.9.0 comes the end of a long rework of the TensorFlow examples, for them to be more Keras-idiomatic, clearer, and more robust.

TensorFlow implementations

HuBERT is now implemented in TensorFlow:

Breaking changes

When load_best_model_at_end was set to True in the TrainingArguments, having a different save_strategy and eval_strategy was accepted but the save_strategy was overwritten by the eval_strategy (the option to keep track of the best model needs to make sure there is an evaluation each time there is a save). This led to a lot of confusion with users not understanding why the script was not doing what it was told, so this situation will now raise an error indicating to set save_strategy and eval_strategy to the same values, and in the case that value is "steps", save_steps must be a round multiple of eval_steps.

General improvements and bugfixes

Check out latest releases or
releases around huggingface/transformers v4.9.0

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