com.johnsnowlabs.ml.tensorflow
TensorflowDistilBert
Related Doc:
package tensorflow
Bert Model wrapper with TensorFlow Wrapper
Id of sentence start Token
Id of sentence end Token.
Configuration for TensorFlow session
Encode the input sequence to indexes IDs adding padding where necessary
batches of sentences
batches of vectors for each sentence
Bert Model wrapper with TensorFlow Wrapper
The DistilBERT model was proposed in the paper DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter https://arxiv.org/abs/1910.01108. DistilBERT is a small, fast, cheap and light Transformer model trained by distilling BERT base. It has 40% less parameters than
bert-base-uncased, runs 60% faster while preserving over 95% of BERT's performances as measured on the GLUE language understanding benchmark.The abstract from the paper is the following:
As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP), operating these large models in on-the-edge and/or under constrained computational training or inference budgets remains challenging. In this work, we propose a method to pre-train a smaller general-purpose language representation model, called DistilBERT, which can then be fine-tuned with good performances on a wide range of tasks like its larger counterparts. While most prior work investigated the use of distillation for building task-specific models, we leverage knowledge distillation during the pretraining phase and show that it is possible to reduce the size of a BERT model by 40%, while retaining 97% of its language understanding capabilities and being 60% faster. To leverage the inductive biases learned by larger models during pretraining, we introduce a triple loss combining language modeling, distillation and cosine-distance losses. Our smaller, faster and lighter model is cheaper to pre-train and we demonstrate its capabilities for on-device computations in a proof-of-concept experiment and a comparative on-device study.
Tips:
- DistilBERT doesn't have :obj:
token_type_ids, you don't need to indicate which token belongs to which segment. Just separate your segments with the separation token :obj:tokenizer.sep_token(or :obj:[SEP]).- DistilBERT doesn't have options to select the input positions (:obj:
position_idsinput). This could be added if necessary though, just let us know if you need this option.