Julia implementation of transformer-based models, with Flux.jl.

notice: The current version is almost completely different from the 0.1.x version. If you are using the old version, make sure to update the changes or stick to the old version.

Installation

In the Julia REPL:

]add Transformers

Example

Using pretrained Bert with Transformers.jl.

using Transformers
using Transformers.TextEncoders
using Transformers.HuggingFace

textencoder, bert_model = hgf"bert-base-uncased"

text1 = "Peter Piper picked a peck of pickled peppers"
text2 = "Fuzzy Wuzzy was a bear"

text = [[ text1, text2 ]] # 1 batch of contiguous sentences
sample = encode(textencoder, text) # tokenize + pre-process (add special tokens + truncate / padding + one-hot encode)

@assert reshape(decode(textencoder, sample.token), :) == [
    "[CLS]", "peter", "piper", "picked", "a", "peck", "of", "pick", "##led", "peppers", "[SEP]",
    "fuzzy", "wu", "##zzy",  "was", "a", "bear", "[SEP]"
]

bert_features = bert_model(sample).hidden_state

See example folder for the complete example.

For more information

If you want to know more about this package, see the document and read code in the example folder. You can also tag me (@chengchingwen) on Julia's slack or discourse if you have any questions, or just create a new Issue on GitHub.