Transformers.rb

:slightly_smiling_face: State-of-the-art transformers for Ruby

For fast inference, check out Informers :fire:

Installation

First, install Torch.rb.

Then add this line to your application’s Gemfile:

gem "transformers-rb"

Getting Started

• Models
• Pipelines

Models

Embedding

• sentence-transformers/all-MiniLM-L6-v2
• sentence-transformers/multi-qa-MiniLM-L6-cos-v1
• sentence-transformers/all-mpnet-base-v2
• sentence-transformers/paraphrase-MiniLM-L6-v2
• mixedbread-ai/mxbai-embed-large-v1
• thenlper/gte-small
• intfloat/e5-base-v2
• BAAI/bge-base-en-v1.5
• Snowflake/snowflake-arctic-embed-m-v1.5

Sparse embedding

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

Reranking

• mixedbread-ai/mxbai-rerank-base-v1
• BAAI/bge-reranker-base

sentence-transformers/all-MiniLM-L6-v2

Docs

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

model = Transformers.pipeline("embedding", "sentence-transformers/all-MiniLM-L6-v2")
embeddings = model.(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.pipeline("embedding", "sentence-transformers/multi-qa-MiniLM-L6-cos-v1")
query_embedding = model.(query)
doc_embeddings = model.(docs)
scores = doc_embeddings.map { |e| e.zip(query_embedding).sum { |d, q| d * q } }
doc_score_pairs = docs.zip(scores).sort_by { |d, s| -s }

sentence-transformers/all-mpnet-base-v2

Docs

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

model = Transformers.pipeline("embedding", "sentence-transformers/all-mpnet-base-v2")
embeddings = model.(sentences)

sentence-transformers/paraphrase-MiniLM-L6-v2

Docs

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

model = Transformers.pipeline("embedding", "sentence-transformers/paraphrase-MiniLM-L6-v2")
embeddings = model.(sentences)

mixedbread-ai/mxbai-embed-large-v1

Docs

query_prefix = "Represent this sentence for searching relevant passages: "

input = [
  "The dog is barking",
  "The cat is purring",
  query_prefix + "puppy"
]

model = Transformers.pipeline("embedding", "mixedbread-ai/mxbai-embed-large-v1")
embeddings = model.(input)

thenlper/gte-small

Docs

sentences = ["That is a happy person", "That is a very happy person"]

model = Transformers.pipeline("embedding", "thenlper/gte-small")
embeddings = model.(sentences)

intfloat/e5-base-v2

Docs

doc_prefix = "passage: "
query_prefix = "query: "

input = [
  doc_prefix + "Ruby is a programming language created by Matz",
  query_prefix + "Ruby creator"
]

model = Transformers.pipeline("embedding", "intfloat/e5-base-v2")
embeddings = model.(input)

BAAI/bge-base-en-v1.5

Docs

query_prefix = "Represent this sentence for searching relevant passages: "

input = [
  "The dog is barking",
  "The cat is purring",
  query_prefix + "puppy"
]

model = Transformers.pipeline("embedding", "BAAI/bge-base-en-v1.5")
embeddings = model.(input)

Snowflake/snowflake-arctic-embed-m-v1.5

Docs

query_prefix = "Represent this sentence for searching relevant passages: "

input = [
  "The dog is barking",
  "The cat is purring",
  query_prefix + "puppy"
]

model = Transformers.pipeline("embedding", "Snowflake/snowflake-arctic-embed-m-v1.5")
embeddings = model.(input, pooling: "cls")

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

mixedbread-ai/mxbai-rerank-base-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.pipeline("reranking", "mixedbread-ai/mxbai-rerank-base-v1")
result = model.(query, docs)

BAAI/bge-reranker-base

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.pipeline("reranking", "BAAI/bge-reranker-base")
result = model.(query, docs)

Pipelines

• Text
• Vision

Text

Embedding

embed = Transformers.pipeline("embedding")
embed.("We are very happy to show you the 🤗 Transformers library.")

Reranking

rerank = Informers.pipeline("reranking")
rerank.("Who created Ruby?", ["Matz created Ruby", "Another doc"])

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.")

Vision

Image classification

classifier = Transformers.pipeline("image-classification")
classifier.("image.jpg")

Image feature extraction

extractor = Transformers.pipeline("image-feature-extraction")
extractor.("image.jpg")

API

This library follows the Transformers Python API. The following model architectures are currently supported:

• BERT
• DeBERTa-v2
• DistilBERT
• MPNet
• ViT
• XLM-RoBERTa

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