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https://github.com/ankane/informers
Fast transformer inference for Ruby
https://github.com/ankane/informers
named-entity-recognition question-answering sentiment-analysis
Last synced: 13 days ago
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Fast transformer inference for Ruby
- Host: GitHub
- URL: https://github.com/ankane/informers
- Owner: ankane
- License: apache-2.0
- Created: 2020-10-02T03:20:32.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2024-10-20T23:36:42.000Z (14 days ago)
- Last Synced: 2024-10-21T03:15:17.554Z (14 days ago)
- Topics: named-entity-recognition, question-answering, sentiment-analysis
- Language: Ruby
- Homepage:
- Size: 2.65 MB
- Stars: 508
- Watchers: 13
- Forks: 10
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE.txt
Awesome Lists containing this project
README
# Informers
:fire: Fast [transformer](https://github.com/xenova/transformers.js) inference for Ruby
For non-ONNX models, check out [Transformers.rb](https://github.com/ankane/transformers-ruby) :slightly_smiling_face:
[](https://github.com/ankane/informers/actions)
## Installation
Add this line to your application’s Gemfile:
```ruby
gem "informers"
```
## Getting Started
- [Models](#models)
- [Pipelines](#pipelines)
## Models
Embedding
- [sentence-transformers/all-MiniLM-L6-v2](#sentence-transformersall-MiniLM-L6-v2)
- [sentence-transformers/multi-qa-MiniLM-L6-cos-v1](#sentence-transformersmulti-qa-MiniLM-L6-cos-v1)
- [sentence-transformers/all-mpnet-base-v2](#sentence-transformersall-mpnet-base-v2)
- [sentence-transformers/paraphrase-MiniLM-L6-v2](#sentence-transformersparaphrase-minilm-l6-v2)
- [mixedbread-ai/mxbai-embed-large-v1](#mixedbread-aimxbai-embed-large-v1)
- [Supabase/gte-small](#supabasegte-small)
- [intfloat/e5-base-v2](#intfloate5-base-v2)
- [nomic-ai/nomic-embed-text-v1](#nomic-ainomic-embed-text-v1)
- [BAAI/bge-base-en-v1.5](#baaibge-base-en-v15)
- [jinaai/jina-embeddings-v2-base-en](#jinaaijina-embeddings-v2-base-en)
- [Snowflake/snowflake-arctic-embed-m-v1.5](#snowflakesnowflake-arctic-embed-m-v15)
Reranking
- [mixedbread-ai/mxbai-rerank-base-v1](#mixedbread-aimxbai-rerank-base-v1)
- [jinaai/jina-reranker-v1-turbo-en](#jinaaijina-reranker-v1-turbo-en)
- [BAAI/bge-reranker-base](#baaibge-reranker-base)
- [Xenova/ms-marco-MiniLM-L-6-v2](#xenovams-marco-minilm-l-6-v2)
### sentence-transformers/all-MiniLM-L6-v2
[Docs](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
```ruby
sentences = ["This is an example sentence", "Each sentence is converted"]
model = Informers.pipeline("embedding", "sentence-transformers/all-MiniLM-L6-v2")
embeddings = model.(sentences)
```
For a quantized version, use:
```ruby
model = Informers.pipeline("embedding", "Xenova/all-MiniLM-L6-v2", quantized: true)
```
### sentence-transformers/multi-qa-MiniLM-L6-cos-v1
[Docs](https://huggingface.co/Xenova/multi-qa-MiniLM-L6-cos-v1)
```ruby
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 }
```
### sentence-transformers/all-mpnet-base-v2
[Docs](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
```ruby
sentences = ["This is an example sentence", "Each sentence is converted"]
model = Informers.pipeline("embedding", "sentence-transformers/all-mpnet-base-v2")
embeddings = model.(sentences)
```
### sentence-transformers/paraphrase-MiniLM-L6-v2
[Docs](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-v2)
```ruby
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)
```
### mixedbread-ai/mxbai-embed-large-v1
[Docs](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1)
```ruby
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)
```
### Supabase/gte-small
[Docs](https://huggingface.co/Supabase/gte-small)
```ruby
sentences = ["That is a happy person", "That is a very happy person"]
model = Informers.pipeline("embedding", "Supabase/gte-small")
embeddings = model.(sentences)
```
### intfloat/e5-base-v2
[Docs](https://huggingface.co/intfloat/e5-base-v2)
```ruby
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)
```
### nomic-ai/nomic-embed-text-v1
[Docs](https://huggingface.co/nomic-ai/nomic-embed-text-v1)
```ruby
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)
```
### BAAI/bge-base-en-v1.5
[Docs](https://huggingface.co/BAAI/bge-base-en-v1.5)
```ruby
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)
```
### jinaai/jina-embeddings-v2-base-en
[Docs](https://huggingface.co/jinaai/jina-embeddings-v2-base-en)
```ruby
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)
```
### Snowflake/snowflake-arctic-embed-m-v1.5
[Docs](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1.5)
```ruby
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")
```
### mixedbread-ai/mxbai-rerank-base-v1
[Docs](https://huggingface.co/mixedbread-ai/mxbai-rerank-base-v1)
```ruby
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)
```
### jinaai/jina-reranker-v1-turbo-en
[Docs](https://huggingface.co/jinaai/jina-reranker-v1-turbo-en)
```ruby
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)
```
### BAAI/bge-reranker-base
[Docs](https://huggingface.co/BAAI/bge-reranker-base)
```ruby
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)
```
### Xenova/ms-marco-MiniLM-L-6-v2
[Docs](https://huggingface.co/Xenova/ms-marco-MiniLM-L-6-v2)
```ruby
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)
```
### Other
The model must include a `.onnx` file ([example](https://huggingface.co/Xenova/all-MiniLM-L6-v2/tree/main/onnx)). If the file is not at `onnx/model.onnx` or `onnx/model_quantized.onnx`, use the `model_file_name` option to specify the location.
## Pipelines
- [Text](#text)
- [Vision](#vision)
- [Audio](#audio)
- [Multimodel](#multimodal)
### Text
Embedding
```ruby
embed = Informers.pipeline("embedding")
embed.("We are very happy to show you the 🤗 Transformers library.")
```
Reranking
```ruby
rerank = Informers.pipeline("reranking")
rerank.("Who created Ruby?", ["Matz created Ruby", "Another doc"])
```
Named-entity recognition
```ruby
ner = Informers.pipeline("ner")
ner.("Ruby is a programming language created by Matz")
```
Sentiment analysis
```ruby
classifier = Informers.pipeline("sentiment-analysis")
classifier.("We are very happy to show you the 🤗 Transformers library.")
```
Question answering
```ruby
qa = Informers.pipeline("question-answering")
qa.("Who invented Ruby?", "Ruby is a programming language created by Matz")
```
Zero-shot classification
```ruby
classifier = Informers.pipeline("zero-shot-classification")
classifier.("text", ["label1", "label2", "label3"])
```
Text generation
```ruby
generator = Informers.pipeline("text-generation")
generator.("I enjoy walking with my cute dog,")
```
Text-to-text generation
```ruby
text2text = Informers.pipeline("text2text-generation")
text2text.("translate from English to French: I'm very happy")
```
Translation
```ruby
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
```ruby
summarizer = Informers.pipeline("summarization")
summarizer.("Many paragraphs of text")
```
Fill mask
```ruby
unmasker = Informers.pipeline("fill-mask")
unmasker.("Paris is the [MASK] of France.")
```
Feature extraction
```ruby
extractor = Informers.pipeline("feature-extraction")
extractor.("We are very happy to show you the 🤗 Transformers library.")
```
### Vision
Note: [ruby-vips](https://github.com/libvips/ruby-vips) is required to load images
Image classification
```ruby
classifier = Informers.pipeline("image-classification")
classifier.("image.jpg")
```
Zero-shot image classification
```ruby
classifier = Informers.pipeline("zero-shot-image-classification")
classifier.("image.jpg", ["label1", "label2", "label3"])
```
Image segmentation
```ruby
segmenter = Informers.pipeline("image-segmentation")
segmenter.("image.jpg")
```
Object detection
```ruby
detector = Informers.pipeline("object-detection")
detector.("image.jpg")
```
Zero-shot object detection
```ruby
detector = Informers.pipeline("zero-shot-object-detection")
detector.("image.jpg", ["label1", "label2", "label3"])
```
Depth estimation
```ruby
estimator = Informers.pipeline("depth-estimation")
estimator.("image.jpg")
```
Image-to-image
```ruby
upscaler = Informers.pipeline("image-to-image")
upscaler.("image.jpg")
```
Image feature extraction
```ruby
extractor = Informers.pipeline("image-feature-extraction")
extractor.("image.jpg")
```
### Audio
Note: [ffmpeg](https://www.ffmpeg.org/) is required to load audio files
Audio classification
```ruby
classifier = Informers.pipeline("audio-classification")
classifier.("audio.wav")
```
### Multimodal
Image captioning
```ruby
captioner = Informers.pipeline("image-to-text")
captioner.("image.jpg")
```
Document question answering
```ruby
qa = Informers.pipeline("document-question-answering")
qa.("image.jpg", "What is the invoice number?")
```
## Credits
This library was ported from [Transformers.js](https://github.com/xenova/transformers.js) and is available under the same license.
## Upgrading
### 1.0
Task classes have been replaced with the `pipeline` method.
```ruby
# before
model = Informers::SentimentAnalysis.new("sentiment-analysis.onnx")
model.predict("This is super cool")
# after
model = Informers.pipeline("sentiment-analysis")
model.("This is super cool")
```
## History
View the [changelog](https://github.com/ankane/informers/blob/master/CHANGELOG.md)
## Contributing
Everyone is encouraged to help improve this project. Here are a few ways you can help:
- [Report bugs](https://github.com/ankane/informers/issues)
- Fix bugs and [submit pull requests](https://github.com/ankane/informers/pulls)
- Write, clarify, or fix documentation
- Suggest or add new features
To get started with development:
```sh
git clone https://github.com/ankane/informers.git
cd informers
bundle install
bundle exec rake download:files
bundle exec rake test
```