🔥 Fast transformer inference for Ruby
For non-ONNX models, check out Transformers.rb 🙂
Add this line to your application’s Gemfile:
gem"informers"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
- Supabase/gte-small
- intfloat/e5-base-v2
- nomic-ai/nomic-embed-text-v1
- BAAI/bge-base-en-v1.5
- jinaai/jina-embeddings-v2-base-en
- Snowflake/snowflake-arctic-embed-m-v1.5
Reranking
- mixedbread-ai/mxbai-rerank-base-v1
- jinaai/jina-reranker-v1-turbo-en
- BAAI/bge-reranker-base
- Xenova/ms-marco-MiniLM-L-6-v2
sentences=["This is an example sentence","Each sentence is converted"]model=Informers.pipeline("embedding","sentence-transformers/all-MiniLM-L6-v2")embeddings=model.(sentences)query="How many people live in London?"docs=["Around 9 Million people live in London","London is known for its financial district"]model=Informers.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}sentences=["This is an example sentence","Each sentence is converted"]model=Informers.pipeline("embedding","sentence-transformers/all-mpnet-base-v2")embeddings=model.(sentences)sentences=["This is an example sentence","Each sentence is converted"]model=Informers.pipeline("embedding","sentence-transformers/paraphrase-MiniLM-L6-v2")embeddings=model.(sentences,normalize: false)query_prefix="Represent this sentence for searching relevant passages: "input=["The dog is barking","The cat is purring",query_prefix + "puppy"]model=Informers.pipeline("embedding","mixedbread-ai/mxbai-embed-large-v1")embeddings=model.(input)sentences=["That is a happy person","That is a very happy person"]model=Informers.pipeline("embedding","Supabase/gte-small")embeddings=model.(sentences)doc_prefix="passage: "query_prefix="query: "input=[doc_prefix + "Ruby is a programming language created by Matz",query_prefix + "Ruby creator"]model=Informers.pipeline("embedding","intfloat/e5-base-v2")embeddings=model.(input)doc_prefix="search_document: "query_prefix="search_query: "input=[doc_prefix + "The dog is barking",doc_prefix + "The cat is purring",query_prefix + "puppy"]model=Informers.pipeline("embedding","nomic-ai/nomic-embed-text-v1")embeddings=model.(input)query_prefix="Represent this sentence for searching relevant passages: "input=["The dog is barking","The cat is purring",query_prefix + "puppy"]model=Informers.pipeline("embedding","BAAI/bge-base-en-v1.5")embeddings=model.(input)sentences=["How is the weather today?","What is the current weather like today?"]model=Informers.pipeline("embedding","jinaai/jina-embeddings-v2-base-en",model_file_name: "../model")embeddings=model.(sentences)query_prefix="Represent this sentence for searching relevant passages: "input=["The dog is barking","The cat is purring",query_prefix + "puppy"]model=Informers.pipeline("embedding","Snowflake/snowflake-arctic-embed-m-v1.5")embeddings=model.(input,model_output: "sentence_embedding",pooling: "none")query="How many people live in London?"docs=["Around 9 Million people live in London","London is known for its financial district"]model=Informers.pipeline("reranking","mixedbread-ai/mxbai-rerank-base-v1")result=model.(query,docs)query="How many people live in London?"docs=["Around 9 Million people live in London","London is known for its financial district"]model=Informers.pipeline("reranking","jinaai/jina-reranker-v1-turbo-en")result=model.(query,docs)query="How many people live in London?"docs=["Around 9 Million people live in London","London is known for its financial district"]model=Informers.pipeline("reranking","BAAI/bge-reranker-base")result=model.(query,docs)query="How many people live in London?"docs=["Around 9 Million people live in London","London is known for its financial district"]model=Informers.pipeline("reranking","Xenova/ms-marco-MiniLM-L-6-v2")result=model.(query,docs)The model must include a .onnx file (example). If the file is not at onnx/model.onnx, use the model_file_name option to specify the location.
Embedding
embed=Informers.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=Informers.pipeline("ner")ner.("Ruby is a programming language created by Matz")Sentiment analysis
classifier=Informers.pipeline("sentiment-analysis")classifier.("We are very happy to show you the 🤗 Transformers library.")Question answering
qa=Informers.pipeline("question-answering")qa.("Who invented Ruby?","Ruby is a programming language created by Matz")Zero-shot classification
classifier=Informers.pipeline("zero-shot-classification")classifier.("text",["label1","label2","label3"])Text generation
generator=Informers.pipeline("text-generation")generator.("I enjoy walking with my cute dog,")Text-to-text generation
text2text=Informers.pipeline("text2text-generation")text2text.("translate from English to French: I'm very happy")Translation
translator=Informers.pipeline("translation","Xenova/nllb-200-distilled-600M")translator.("जीवन एक चॉकलेट बॉक्स की तरह है।",src_lang: "hin_Deva",tgt_lang: "fra_Latn")Summarization
summarizer=Informers.pipeline("summarization")summarizer.("Many paragraphs of text")Fill mask
unmasker=Informers.pipeline("fill-mask")unmasker.("Paris is the [MASK] of France.")Feature extraction
extractor=Informers.pipeline("feature-extraction")extractor.("We are very happy to show you the 🤗 Transformers library.")Note: ruby-vips is required to load images
Image classification
classifier=Informers.pipeline("image-classification")classifier.("image.jpg")Zero-shot image classification
classifier=Informers.pipeline("zero-shot-image-classification")classifier.("image.jpg",["label1","label2","label3"])Image segmentation
segmenter=Informers.pipeline("image-segmentation")segmenter.("image.jpg")Object detection
detector=Informers.pipeline("object-detection")detector.("image.jpg")Zero-shot object detection
detector=Informers.pipeline("zero-shot-object-detection")detector.("image.jpg",["label1","label2","label3"])Depth estimation
estimator=Informers.pipeline("depth-estimation")estimator.("image.jpg")Image-to-image
upscaler=Informers.pipeline("image-to-image")upscaler.("image.jpg")Image feature extraction
extractor=Informers.pipeline("image-feature-extraction")extractor.("image.jpg")Note: ffmpeg is required to load audio files
Audio classification
classifier=Informers.pipeline("audio-classification")classifier.("audio.wav")Image captioning
captioner=Informers.pipeline("image-to-text")captioner.("image.jpg")Document question answering
qa=Informers.pipeline("document-question-answering")qa.("image.jpg","What is the invoice number?")Specify a variant of the model if available (fp32, fp16, int8, uint8, q8, q4, q4f16, or bnb4)
Informers.pipeline("embedding","Xenova/all-MiniLM-L6-v2",dtype: "fp16")Specify a device (cpu, cuda, or coreml)
Informers.pipeline("embedding",device: "cuda")Note: Follow these instructions for cuda
Specify ONNX Runtime session options
Informers.pipeline("embedding",session_options: {log_severity_level: 2})This library was ported from Transformers.js and is available under the same license.
View the changelog
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/informers.git
cd informers
bundle install
bundle exec rake download:files
bundle exec rake test