Transformers.rb

🙂 State-of-the-art transformers for Ruby

Installation

First, install Torch.rb.

Then add this line to your application’s Gemfile:

gem "transformers-rb"

Getting Started

• Models
• Pipelines

Models

sentence-transformers/all-MiniLM-L6-v2

Docs

sentences = ["This is an example sentence", "Each sentence is converted"]

model = Transformers::SentenceTransformer.new("sentence-transformers/all-MiniLM-L6-v2")
embeddings = model.encode(sentences)

sentence-transformers/multi-qa-MiniLM-L6-cos-v1

Docs

query = "How many people live in London?"
docs = ["Around 9 Million people live in London", "London is known for its financial district"]

model = Transformers::SentenceTransformer.new("sentence-transformers/multi-qa-MiniLM-L6-cos-v1")
query_embedding = model.encode(query)
doc_embeddings = model.encode(docs)
scores = Torch.mm(Torch.tensor([query_embedding]), Torch.tensor(doc_embeddings).transpose(0, 1))[0].to_a
doc_score_pairs = docs.zip(scores).sort_by { |d, s| -s }

mixedbread-ai/mxbai-embed-large-v1

Docs

def transform_query(query)
  "Represent this sentence for searching relevant passages: #{query}"
end

docs = [
  transform_query("puppy"),
  "The dog is barking",
  "The cat is purring"
]

model = Transformers::SentenceTransformer.new("mixedbread-ai/mxbai-embed-large-v1")
embeddings = model.encode(docs)

opensearch-project/opensearch-neural-sparse-encoding-v1

Docs

docs = ["The dog is barking", "The cat is purring", "The bear is growling"]

model_id = "opensearch-project/opensearch-neural-sparse-encoding-v1"
model = Transformers::AutoModelForMaskedLM.from_pretrained(model_id)
tokenizer = Transformers::AutoTokenizer.from_pretrained(model_id)
special_token_ids = tokenizer.special_tokens_map.map { |_, token| tokenizer.vocab[token] }

feature = tokenizer.(docs, padding: true, truncation: true, return_tensors: "pt", return_token_type_ids: false)
output = model.(**feature)[0]

values, _ = Torch.max(output * feature[:attention_mask].unsqueeze(-1), dim: 1)
values = Torch.log(1 + Torch.relu(values))
values[0.., special_token_ids] = 0
embeddings = values.to_a

Pipelines

Named-entity recognition

ner = Transformers.pipeline("ner")
ner.("Ruby is a programming language created by Matz")

Sentiment analysis

classifier = Transformers.pipeline("sentiment-analysis")
classifier.("We are very happy to show you the 🤗 Transformers library.")

Question answering

qa = Transformers.pipeline("question-answering")
qa.(question: "Who invented Ruby?", context: "Ruby is a programming language created by Matz")

Feature extraction

extractor = Transformers.pipeline("feature-extraction")
extractor.("We are very happy to show you the 🤗 Transformers library.")

Image classification

classifier = Transformers.pipeline("image-classification")
classifier.(URI("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"))

Image feature extraction

extractor = Transformers.pipeline("image-feature-extraction")
extractor.(URI("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"))

API

This library follows the Transformers Python API. Only a few model architectures are currently supported:

• BERT
• DistilBERT
• ViT

History

View the changelog

Contributing

Everyone is encouraged to help improve this project. Here are a few ways you can help:

• Report bugs
• Fix bugs and submit pull requests
• Write, clarify, or fix documentation
• Suggest or add new features

To get started with development:

git clone https://github.com/ankane/transformers-ruby.git
cd transformers-ruby
bundle install
bundle exec rake download:files
bundle exec rake test