Transformers.rb

🙂 State-of-the-art transformers for Ruby

For fast inference, check out Informers 🔥

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]=0embeddings=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