New model architectures: ALBERT, CamemBERT, GPT2-XL, DistilRoberta
Four new models have been added in v2.2.0
- ALBERT (Pytorch & TF) (from Google Research and the Toyota Technological Institute at Chicago) released with the paper ALBERT: A Lite BERT for Self-supervised Learning of Language Representations, by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
- CamemBERT (Pytorch) (from Facebook AI Research, INRIA, and La Sorbonne Université), as the first large-scale Transformer language model. Released alongside the paper CamemBERT: a Tasty French Language Model by Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suarez, Yoann Dupont, Laurent Romary, Eric Villemonte de la Clergerie, Djame Seddah, and Benoît Sagot. It was added by @louismartin with the help of @julien-c.
- DistilRoberta (Pytorch & TF) from @VictorSanh as the third distilled model after DistilBERT and DistilGPT-2.
- GPT-2 XL (Pytorch & TF) as the last GPT-2 checkpoint released by OpenAI
We welcome the possibility to create fully seq2seq models by incorporating Encoder-Decoder architectures using a
PreTrainedEncoderDecoder class that can be initialized from pre-trained models. The base BERT class has be modified so that it may behave as a decoder.
Model2Model class that simplifies the definition of an encoder-decoder when both encoder and decoder are based on the same model has been added. @rlouf
Benchmarks and performance improvements
Works by @tlkh and @LysandreJik aiming to benchmark the library models with different technologies: with TensorFlow and Pytorch, with mixed precision (AMP and FP-16) and with model tracing (Torchscript and XLA). A new section was created in the documentation: benchmarks pointing to Google sheets with the results.
Tokenizers now add special tokens by default. @LysandreJik
New model templates
Model templates to ease the addition of new models to the library have been added. @thomwolf
A new input has been added to all models'
forward (for Pytorch) and
call (for TensorFlow) methods. These
inputs_embeds are a direct embedded representation. This is useful as it gives more control over how to convert
input_ids indices into associated vectors than the model's internal embedding lookup matrix. @julien-c
Getters and setters for input and output embeddings
A new API for the input and output embeddings are available. These methods are model-independent and allow easy acquisition/modification of the models' embeddings. @thomwolf
New model architectures are available, namely:
- The Fairseq RoBERTa model conversion script has been patched. @louismartin
- einsum now runs in FP-16 in the library's examples @slayton58
- In-depth work on the squad script for XLNet to reproduce the original paper's results @hlums
- Additional improvements on the run_squad script by @WilliamTambellini, @orena1
- The run_generation script has seen several improvements by @leo-du
- The RoBERTaTensorFlow model has been patched for several use-cases: TPU and keras.fit @LysandreJik
- The documentation is now versioned, links are available on the github readme @LysandreJik
- The run_ner script has seen several improvements @mmaybeno, @oneraghavan, @manansanghi
- The run_tf_glue script now works for all GLUE tasks @LysandreJik
- The run_lm_finetuning script now correctly evaluates perplexity on MLM tasks @altsoph
- An issue related to the XLM TensorFlow implementation's training has been fixed @tlkh
- run_bertology has been updated to be closer to the run_glue example @adrianbg
- Fixed added special tokens in decoded sequences @LysandreJik
- Several performance improvements have been done to the tokenizers @iedmrc
- A memory leak has been identified and patched in the library's schedulers @rlouf
- Correct warning when encoding a sequence too long while specifying a maximum length @LysandreJik
- Resizing the token embeddings now works as expected in the run_lm_finetuning script @iedmrc
- The difference in versions between Pypi/source in order to run the examples has been clarified @rlouf