mirror of
https://github.com/guillaume-be/rust-bert.git
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Update dependencies and fix convert_model (#458)
* feat: Update dependencies in Cargo.toml Update the dependencies in Cargo.toml to their latest versions: - rust_tokenizers: 8.1.1 - tch: 0.16.0 (with features = ["download-libtorch"]) - serde_json: 1 - serde: 1 (with features = ["derive"]) - ordered-float: 4.2.0 - uuid: 1 (with features = ["v4"]) - thiserror: 1 - half: 2 - regex: 1.6 - cached-path: 0.6 (with default-features = false and optional = true) - dirs: 5 (optional = true) - lazy_static: 1 (optional = true) - ort: 1.16.3 (optional = true, default-features = false, features = ["half"]) - ndarray: 0.15 (optional = true) - tokenizers: 0.19.1 (optional = true, default-features = false, features = ["onig"]) ``` * chore: Update .gitignore and requirements.txt, and improve convert_model.py Update .gitignore to exclude the /models/ and /.venv/ directories, and the convert_model.log file. Remove the requirements.txt file. In convert_model.py: - Add a new function, `zipfile_factory`, to handle zip file creation. - Update the logger configuration to log debug messages to a file named `convert_model.log`. * delete duplicate requirements file * update CI req file path * missing requests dependency --------- Co-authored-by: Abdulrhman Alkhodiry <aalkhodiry@jahez.net> Co-authored-by: Guillaume Becquin <guillaume.becquin@gmail.com>
This commit is contained in:
Abdulrhman Alkhodiry
GitHub
parent
f99bf51f53
commit
33b2944298
2
.github/workflows/continuous-integration.yml
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.github/workflows/continuous-integration.yml
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@ -174,7 +174,7 @@ jobs:
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with:
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python-version: '3.10'
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- run: |
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pip install -r requirements.txt --progress-bar off
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pip install -r ./utils/requirements.txt --progress-bar off
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python ./utils/download-dependencies_distilbert.py
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fmt:
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4
.gitignore
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.gitignore
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@ -17,4 +17,6 @@ Cargo.lock
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/target
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#**/*.rs.bk
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/resources/
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/models/
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/.venv/
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convert_model.log
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23
Cargo.toml
23
Cargo.toml
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@ -76,10 +76,10 @@ features = ["doc-only"]
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[dependencies]
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rust_tokenizers = "8.1.1"
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tch = "0.15.0"
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tch = { version = "0.16.0", features = ["download-libtorch"] }
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serde_json = "1"
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serde = { version = "1", features = ["derive"] }
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ordered-float = "3"
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ordered-float = "4.2.0"
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uuid = { version = "1", features = ["v4"] }
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thiserror = "1"
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half = "2"
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@ -88,9 +88,13 @@ regex = "1.6"
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cached-path = { version = "0.6", default-features = false, optional = true }
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dirs = { version = "5", optional = true }
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lazy_static = { version = "1", optional = true }
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ort = {version="~1.15.2", optional = true, default-features = false, features = ["half"]}
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ndarray = {version="0.15", optional = true}
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tokenizers = {version="0.15", optional=true, default-features = false, features = ["onig"]}
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ort = { version = "1.16.3", optional = true, default-features = false, features = [
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"half",
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] }
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ndarray = { version = "0.15", optional = true }
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tokenizers = { version = "0.19.1", optional = true, default-features = false, features = [
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"onig",
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] }
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[dev-dependencies]
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anyhow = "1"
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@ -98,9 +102,12 @@ csv = "1"
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criterion = "0.5"
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tokio = { version = "1.35", features = ["sync", "rt-multi-thread", "macros"] }
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tempfile = "3"
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itertools = "0.12"
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tracing-subscriber = { version = "0.3", default-features = false, features = [ "env-filter", "fmt" ] }
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ort = {version="~1.15.5", features = ["load-dynamic"]}
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itertools = "0.13.0"
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tracing-subscriber = { version = "0.3", default-features = false, features = [
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"env-filter",
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"fmt",
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] }
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ort = { version = "1.16.3", features = ["load-dynamic"] }
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[[example]]
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name = "onnx-masked-lm"
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434
README.md
434
README.md
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@ -5,10 +5,21 @@
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[](https://docs.rs/rust-bert)
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Rust-native state-of-the-art Natural Language Processing models and pipelines. Port of Hugging Face's [Transformers library](https://github.com/huggingface/transformers), using [tch-rs](https://github.com/LaurentMazare/tch-rs) or [onnxruntime bindings](https://github.com/pykeio/ort) and pre-processing from [rust-tokenizers](https://github.com/guillaume-be/rust-tokenizers). Supports multi-threaded tokenization and GPU inference.
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This repository exposes the model base architecture, task-specific heads (see below) and [ready-to-use pipelines](#ready-to-use-pipelines). [Benchmarks](#benchmarks) are available at the end of this document.
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Rust-native state-of-the-art Natural Language Processing models and pipelines.
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Port of Hugging Face's
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[Transformers library](https://github.com/huggingface/transformers), using
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[tch-rs](https://github.com/LaurentMazare/tch-rs) or
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[onnxruntime bindings](https://github.com/pykeio/ort) and pre-processing from
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[rust-tokenizers](https://github.com/guillaume-be/rust-tokenizers). Supports
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multi-threaded tokenization and GPU inference. This repository exposes the model
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base architecture, task-specific heads (see below) and
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[ready-to-use pipelines](#ready-to-use-pipelines). [Benchmarks](#benchmarks) are
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available at the end of this document.
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Get started with tasks including question answering, named entity recognition,
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translation, summarization, text generation, conversational agents and more in
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just a few lines of code:
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Get started with tasks including question answering, named entity recognition, translation, summarization, text generation, conversational agents and more in just a few lines of code:
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```rust
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let qa_model = QuestionAnsweringModel::new(Default::default())?;
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@ -19,84 +30,104 @@ Get started with tasks including question answering, named entity recognition, t
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```
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Output:
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```
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[Answer { score: 0.9976, start: 13, end: 21, answer: "Amsterdam" }]
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```
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The tasks currently supported include:
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- Translation
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- Summarization
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- Multi-turn dialogue
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- Zero-shot classification
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- Sentiment Analysis
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- Named Entity Recognition
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- Part of Speech tagging
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- Question-Answering
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- Language Generation
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- Masked Language Model
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- Sentence Embeddings
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- Keywords extraction
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- Translation
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- Summarization
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- Multi-turn dialogue
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- Zero-shot classification
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- Sentiment Analysis
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- Named Entity Recognition
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- Part of Speech tagging
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- Question-Answering
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- Language Generation
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- Masked Language Model
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- Sentence Embeddings
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- Keywords extraction
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<details>
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<summary> <b>Expand to display the supported models/tasks matrix </b> </summary>
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| |**Sequence classification**|**Token classification**|**Question answering**|**Text Generation**|**Summarization**|**Translation**|**Masked LM**|**Sentence Embeddings**|
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:-----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:----:
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DistilBERT|✅|✅|✅| | | |✅| ✅|
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MobileBERT|✅|✅|✅| | | |✅| |
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DeBERTa|✅|✅|✅| | | |✅| |
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DeBERTa (v2)|✅|✅|✅| | | |✅| |
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FNet|✅|✅|✅| | | |✅| |
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BERT|✅|✅|✅| | | |✅| ✅|
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RoBERTa|✅|✅|✅| | | |✅| ✅|
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GPT| | | |✅ | | | | |
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GPT2| | | |✅ | | | | |
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GPT-Neo| | | |✅ | | | | |
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GPT-J| | | |✅ | | | | |
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BART|✅| | |✅ |✅| | | |
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Marian| | | | | |✅| | |
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MBart|✅| | |✅ | | | | |
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M2M100| | | |✅ | | | | |
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NLLB| | | |✅ | | | | |
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Electra | |✅| | | | |✅| |
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ALBERT |✅|✅|✅| | | |✅| ✅ |
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T5 | | | |✅ |✅|✅| | ✅ |
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LongT5 | | | |✅ |✅|| | |
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XLNet|✅|✅|✅|✅ | | |✅| |
|
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Reformer|✅| |✅|✅ | | |✅| |
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ProphetNet| | | |✅ |✅ | | | |
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Longformer|✅|✅|✅| | | |✅| |
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Pegasus| | | | |✅| | | |
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| | **Sequence classification** | **Token classification** | **Question answering** | **Text Generation** | **Summarization** | **Translation** | **Masked LM** | **Sentence Embeddings** |
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| :----------: | :-------------------------: | :----------------------: | :--------------------: | :-----------------: | :---------------: | :-------------: | :-----------: | :---------------------: |
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| DistilBERT | ✅ | ✅ | ✅ | | | | ✅ | ✅ |
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| MobileBERT | ✅ | ✅ | ✅ | | | | ✅ | |
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| DeBERTa | ✅ | ✅ | ✅ | | | | ✅ | |
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| DeBERTa (v2) | ✅ | ✅ | ✅ | | | | ✅ | |
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| FNet | ✅ | ✅ | ✅ | | | | ✅ | |
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| BERT | ✅ | ✅ | ✅ | | | | ✅ | ✅ |
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| RoBERTa | ✅ | ✅ | ✅ | | | | ✅ | ✅ |
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| GPT | | | | ✅ | | | | |
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| GPT2 | | | | ✅ | | | | |
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| GPT-Neo | | | | ✅ | | | | |
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| GPT-J | | | | ✅ | | | | |
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| BART | ✅ | | | ✅ | ✅ | | | |
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| Marian | | | | | | ✅ | | |
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| MBart | ✅ | | | ✅ | | | | |
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| M2M100 | | | | ✅ | | | | |
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| NLLB | | | | ✅ | | | | |
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| Electra | | ✅ | | | | | ✅ | |
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| ALBERT | ✅ | ✅ | ✅ | | | | ✅ | ✅ |
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| T5 | | | | ✅ | ✅ | ✅ | | ✅ |
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| LongT5 | | | | ✅ | ✅ | | | |
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| XLNet | ✅ | ✅ | ✅ | ✅ | | | ✅ | |
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| Reformer | ✅ | | ✅ | ✅ | | | ✅ | |
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| ProphetNet | | | | ✅ | ✅ | | | |
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| Longformer | ✅ | ✅ | ✅ | | | | ✅ | |
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| Pegasus | | | | | ✅ | | | |
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</details>
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## Getting started
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This library relies on the [tch](https://github.com/LaurentMazare/tch-rs) crate for bindings to the C++ Libtorch API.
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The libtorch library is required can be downloaded either automatically or manually. The following provides a reference on how to set-up your environment
|
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to use these bindings, please refer to the [tch](https://github.com/LaurentMazare/tch-rs) for detailed information or support.
|
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This library relies on the [tch](https://github.com/LaurentMazare/tch-rs) crate
|
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for bindings to the C++ Libtorch API. The libtorch library is required can be
|
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downloaded either automatically or manually. The following provides a reference
|
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on how to set-up your environment to use these bindings, please refer to the
|
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[tch](https://github.com/LaurentMazare/tch-rs) for detailed information or
|
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support.
|
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|
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Furthermore, this library relies on a cache folder for downloading pre-trained models.
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This cache location defaults to `~/.cache/.rustbert`, but can be changed by setting the `RUSTBERT_CACHE` environment variable. Note that the language models used by this library are in the order of the 100s of MBs to GBs.
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Furthermore, this library relies on a cache folder for downloading pre-trained
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models. This cache location defaults to `~/.cache/.rustbert`, but can be changed
|
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by setting the `RUSTBERT_CACHE` environment variable. Note that the language
|
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models used by this library are in the order of the 100s of MBs to GBs.
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### Manual installation (recommended)
|
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1. Download `libtorch` from https://pytorch.org/get-started/locally/. This package requires `v2.2`: if this version is no longer available on the "get started" page,
|
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the file should be accessible by modifying the target link, for example `https://download.pytorch.org/libtorch/cu121/libtorch-cxx11-abi-shared-with-deps-2.2.0%2Bcu121.zip` for a Linux version with CUDA12. **NOTE:** When using `rust-bert` as dependency from [crates.io](https://crates.io), please check the required `LIBTORCH` on the published package [readme](https://crates.io/crates/rust-bert) as it may differ from the version documented here (applying to the current repository version).
|
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1. Download `libtorch` from https://pytorch.org/get-started/locally/. This
|
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package requires `v2.2`: if this version is no longer available on the "get
|
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started" page, the file should be accessible by modifying the target link,
|
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for example
|
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`https://download.pytorch.org/libtorch/cu121/libtorch-cxx11-abi-shared-with-deps-2.2.0%2Bcu121.zip`
|
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for a Linux version with CUDA12. **NOTE:** When using `rust-bert` as
|
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dependency from [crates.io](https://crates.io), please check the required
|
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`LIBTORCH` on the published package
|
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[readme](https://crates.io/crates/rust-bert) as it may differ from the
|
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version documented here (applying to the current repository version).
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2. Extract the library to a location of your choice
|
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3. Set the following environment variables
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##### Linux:
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```bash
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export LIBTORCH=/path/to/libtorch
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export LD_LIBRARY_PATH=${LIBTORCH}/lib:$LD_LIBRARY_PATH
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```
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##### Windows
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```powershell
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$Env:LIBTORCH = "X:\path\to\libtorch"
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$Env:Path += ";X:\path\to\libtorch\lib"
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```
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#### macOS + Homebrew
|
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```bash
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brew install pytorch jq
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export LIBTORCH=$(brew --cellar pytorch)/$(brew info --json pytorch | jq -r '.[0].installed[0].version')
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@ -105,13 +136,19 @@ export LD_LIBRARY_PATH=${LIBTORCH}/lib:$LD_LIBRARY_PATH
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### Automatic installation
|
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|
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Alternatively, you can let the `build` script automatically download the `libtorch` library for you. The `download-libtorch` feature flag needs to be enabled.
|
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The CPU version of libtorch will be downloaded by default. To download a CUDA version, please set the environment variable `TORCH_CUDA_VERSION` to `cu118`.
|
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Note that the libtorch library is large (order of several GBs for the CUDA-enabled version) and the first build may therefore take several minutes to complete.
|
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Alternatively, you can let the `build` script automatically download the
|
||||
`libtorch` library for you. The `download-libtorch` feature flag needs to be
|
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enabled. The CPU version of libtorch will be downloaded by default. To download
|
||||
a CUDA version, please set the environment variable `TORCH_CUDA_VERSION` to
|
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`cu118`. Note that the libtorch library is large (order of several GBs for the
|
||||
CUDA-enabled version) and the first build may therefore take several minutes to
|
||||
complete.
|
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|
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### Verifying installation
|
||||
|
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Verify your installation (and linking with libtorch) by adding the `rust-bert` dependency to your `Cargo.toml` or by cloning the rust-bert source and running an example:
|
||||
Verify your installation (and linking with libtorch) by adding the `rust-bert`
|
||||
dependency to your `Cargo.toml` or by cloning the rust-bert source and running
|
||||
an example:
|
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|
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```bash
|
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git clone git@github.com:guillaume-be/rust-bert.git
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@ -121,41 +158,73 @@ cargo run --example sentence_embeddings
|
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|
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## ONNX Support (Optional)
|
||||
|
||||
ONNX support can be enabled via the optional `onnx` feature. This crate then leverages the [ort](https://github.com/pykeio/ort) crate with bindings to the onnxruntime C++ library. We refer the user to this page project for further installation instructions/support.
|
||||
1. Enable the optional `onnx` feature. The `rust-bert` crate does not include any optional dependencies for `ort`, the end user should select the set of features that would be adequate for pulling the required `onnxruntime` C++ library.
|
||||
2. The current recommended installation is to use dynamic linking by pointing to an existing library location. Use the `load-dynamic` cargo feature for `ort`.
|
||||
3. set the `ORT_DYLIB_PATH` to point to the location of downloaded onnxruntime library (`onnxruntime.dll`/`libonnxruntime.so`/`libonnxruntime.dylib` depending on the operating system). These can be downloaded from the [release page](https://github.com/microsoft/onnxruntime/releases) of the onnxruntime project
|
||||
ONNX support can be enabled via the optional `onnx` feature. This crate then
|
||||
leverages the [ort](https://github.com/pykeio/ort) crate with bindings to the
|
||||
onnxruntime C++ library. We refer the user to this page project for further
|
||||
installation instructions/support.
|
||||
|
||||
Most architectures (including encoders, decoders and encoder-decoders) are supported. the library aims at keeping compatibility with models exported using the [optimum](https://github.com/huggingface/optimum) library. A detailed guide on how to export a Transformer model to ONNX using optimum is available at https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model
|
||||
The resources used to create ONNX models are similar to those based on Pytorch, replacing the pytorch by the ONNX model. Since ONNX models are less flexible than their Pytorch counterparts in the handling of optional arguments, exporting a decoder or encoder-decoder model to ONNX will usually result in multiple files. These files are expected (but not all are necessary) for use in this library as per the table below:
|
||||
1. Enable the optional `onnx` feature. The `rust-bert` crate does not include
|
||||
any optional dependencies for `ort`, the end user should select the set of
|
||||
features that would be adequate for pulling the required `onnxruntime` C++
|
||||
library.
|
||||
2. The current recommended installation is to use dynamic linking by pointing to
|
||||
an existing library location. Use the `load-dynamic` cargo feature for `ort`.
|
||||
3. set the `ORT_DYLIB_PATH` to point to the location of downloaded onnxruntime
|
||||
library (`onnxruntime.dll`/`libonnxruntime.so`/`libonnxruntime.dylib`
|
||||
depending on the operating system). These can be downloaded from the
|
||||
[release page](https://github.com/microsoft/onnxruntime/releases) of the
|
||||
onnxruntime project
|
||||
|
||||
| Architecture | Encoder file | Decoder without past file | Decoder with past file |
|
||||
|-----------------------------|---------------|---------------------------|-------------------------|
|
||||
| Encoder (e.g. BERT) | required | not used | not used |
|
||||
| Decoder (e.g. GPT2) | not used | required | optional |
|
||||
| Encoder-decoder (e.g. BART) | required | required | optional |
|
||||
Most architectures (including encoders, decoders and encoder-decoders) are
|
||||
supported. the library aims at keeping compatibility with models exported using
|
||||
the [optimum](https://github.com/huggingface/optimum) library. A detailed guide
|
||||
on how to export a Transformer model to ONNX using optimum is available at
|
||||
https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model
|
||||
The resources used to create ONNX models are similar to those based on Pytorch,
|
||||
replacing the pytorch by the ONNX model. Since ONNX models are less flexible
|
||||
than their Pytorch counterparts in the handling of optional arguments, exporting
|
||||
a decoder or encoder-decoder model to ONNX will usually result in multiple
|
||||
files. These files are expected (but not all are necessary) for use in this
|
||||
library as per the table below:
|
||||
|
||||
Note that the computational efficiency will drop when the `decoder with past` file is optional but not provided
|
||||
since the model will not used cached past keys and values for the attention mechanism, leading to a high number of
|
||||
redundant computations. The Optimum library offers export options to ensure such a `decoder with past` model file is created.
|
||||
he base encoder and decoder model architecture are available (and exposed for convenience) in the `encoder` and `decoder` modules, respectively.
|
||||
| Architecture | Encoder file | Decoder without past file | Decoder with past file |
|
||||
| --------------------------- | ------------ | ------------------------- | ---------------------- |
|
||||
| Encoder (e.g. BERT) | required | not used | not used |
|
||||
| Decoder (e.g. GPT2) | not used | required | optional |
|
||||
| Encoder-decoder (e.g. BART) | required | required | optional |
|
||||
|
||||
Generation models (pure decoder or encoder/decoder architectures) are available in the `models` module.
|
||||
ost pipelines are available for ONNX model checkpoints, including sequence classification, zero-shot classification,
|
||||
token classification (including named entity recognition and part-of-speech tagging), question answering, text generation, summarization and translation.
|
||||
These models use the same configuration and tokenizer files as their Pytorch counterparts when used in a pipeline. Examples leveraging ONNX models are given in the `./examples` directory
|
||||
Note that the computational efficiency will drop when the `decoder with past`
|
||||
file is optional but not provided since the model will not used cached past keys
|
||||
and values for the attention mechanism, leading to a high number of redundant
|
||||
computations. The Optimum library offers export options to ensure such a
|
||||
`decoder with past` model file is created. he base encoder and decoder model
|
||||
architecture are available (and exposed for convenience) in the `encoder` and
|
||||
`decoder` modules, respectively.
|
||||
|
||||
Generation models (pure decoder or encoder/decoder architectures) are available
|
||||
in the `models` module. ost pipelines are available for ONNX model checkpoints,
|
||||
including sequence classification, zero-shot classification, token
|
||||
classification (including named entity recognition and part-of-speech tagging),
|
||||
question answering, text generation, summarization and translation. These models
|
||||
use the same configuration and tokenizer files as their Pytorch counterparts
|
||||
when used in a pipeline. Examples leveraging ONNX models are given in the
|
||||
`./examples` directory
|
||||
|
||||
## Ready-to-use pipelines
|
||||
|
||||
Based on Hugging Face's pipelines, ready to use end-to-end NLP pipelines are available as part of this crate. The following capabilities are currently available:
|
||||
|
||||
**Disclaimer**
|
||||
The contributors of this repository are not responsible for any generation from the 3rd party utilization of the pretrained systems proposed herein.
|
||||
Based on Hugging Face's pipelines, ready to use end-to-end NLP pipelines are
|
||||
available as part of this crate. The following capabilities are currently
|
||||
available:
|
||||
|
||||
**Disclaimer** The contributors of this repository are not responsible for any
|
||||
generation from the 3rd party utilization of the pretrained systems proposed
|
||||
herein.
|
||||
|
||||
<details>
|
||||
<summary> <b>1. Question Answering</b> </summary>
|
||||
|
||||
Extractive question answering from a given question and context. DistilBERT model fine-tuned on SQuAD (Stanford Question Answering Dataset)
|
||||
Extractive question answering from a given question and context. DistilBERT
|
||||
model fine-tuned on SQuAD (Stanford Question Answering Dataset)
|
||||
|
||||
```rust
|
||||
let qa_model = QuestionAnsweringModel::new(Default::default())?;
|
||||
|
|
@ -167,20 +236,27 @@ Extractive question answering from a given question and context. DistilBERT mode
|
|||
```
|
||||
|
||||
Output:
|
||||
|
||||
```
|
||||
[Answer { score: 0.9976, start: 13, end: 21, answer: "Amsterdam" }]
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
<details>
|
||||
<summary> <b>2. Translation </b> </summary>
|
||||
|
||||
Translation pipeline supporting a broad range of source and target languages. Leverages two main architectures for translation tasks:
|
||||
- Marian-based models, for specific source/target combinations
|
||||
- M2M100 models allowing for direct translation between 100 languages (at a higher computational cost and lower performance for some selected languages)
|
||||
Translation pipeline supporting a broad range of source and target languages.
|
||||
Leverages two main architectures for translation tasks:
|
||||
|
||||
- Marian-based models, for specific source/target combinations
|
||||
- M2M100 models allowing for direct translation between 100 languages (at a
|
||||
higher computational cost and lower performance for some selected languages)
|
||||
|
||||
Marian-based pretrained models for the following language pairs are readily
|
||||
available in the library - but the user can import any Pytorch-based model for
|
||||
predictions
|
||||
|
||||
Marian-based pretrained models for the following language pairs are readily available in the library - but the user can import any Pytorch-based
|
||||
model for predictions
|
||||
- English <-> French
|
||||
- English <-> Spanish
|
||||
- English <-> Portuguese
|
||||
|
|
@ -196,30 +272,36 @@ model for predictions
|
|||
- English <-> Hindi
|
||||
- French <-> German
|
||||
|
||||
For languages not supported by the proposed pretrained Marian models, the user can leverage a M2M100 model supporting direct translation between 100 languages (without intermediate English translation)
|
||||
The full list of supported languages is available in the [crate documentation](https://docs.rs/rust-bert/latest/rust_bert/pipelines/translation/enum.Language.html)
|
||||
For languages not supported by the proposed pretrained Marian models, the user
|
||||
can leverage a M2M100 model supporting direct translation between 100 languages
|
||||
(without intermediate English translation) The full list of supported languages
|
||||
is available in the
|
||||
[crate documentation](https://docs.rs/rust-bert/latest/rust_bert/pipelines/translation/enum.Language.html)
|
||||
|
||||
```rust
|
||||
use rust_bert::pipelines::translation::{Language, TranslationModelBuilder};
|
||||
fn main() -> anyhow::Result<()> {
|
||||
let model = TranslationModelBuilder::new()
|
||||
.with_source_languages(vec![Language::English])
|
||||
.with_target_languages(vec![Language::Spanish, Language::French, Language::Italian])
|
||||
.create_model()?;
|
||||
let input_text = "This is a sentence to be translated";
|
||||
let output = model.translate(&[input_text], None, Language::Spanish)?;
|
||||
for sentence in output {
|
||||
println!("{}", sentence);
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
```
|
||||
|
||||
```rust
|
||||
use rust_bert::pipelines::translation::{Language, TranslationModelBuilder};
|
||||
fn main() -> anyhow::Result<()> {
|
||||
let model = TranslationModelBuilder::new()
|
||||
.with_source_languages(vec![Language::English])
|
||||
.with_target_languages(vec![Language::Spanish, Language::French, Language::Italian])
|
||||
.create_model()?;
|
||||
let input_text = "This is a sentence to be translated";
|
||||
let output = model.translate(&[input_text], None, Language::Spanish)?;
|
||||
for sentence in output {
|
||||
println!("{}", sentence);
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
```
|
||||
Output:
|
||||
|
||||
```
|
||||
Il s'agit d'une phrase à traduire
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
<details>
|
||||
<summary> <b>3. Summarization </b> </summary>
|
||||
|
||||
|
|
@ -252,26 +334,35 @@ about exoplanets like K2-18b."];
|
|||
|
||||
let output = summarization_model.summarize(&input);
|
||||
```
|
||||
(example from: [WikiNews](https://en.wikinews.org/wiki/Astronomers_find_water_vapour_in_atmosphere_of_exoplanet_K2-18b))
|
||||
|
||||
(example from:
|
||||
[WikiNews](https://en.wikinews.org/wiki/Astronomers_find_water_vapour_in_atmosphere_of_exoplanet_K2-18b))
|
||||
|
||||
Output:
|
||||
|
||||
```
|
||||
"Scientists have found water vapour on K2-18b, a planet 110 light-years from Earth.
|
||||
This is the first such discovery in a planet in its star's habitable zone.
|
||||
The planet is not too hot and not too cold for liquid water to exist."
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
<details>
|
||||
<summary> <b>4. Dialogue Model </b> </summary>
|
||||
|
||||
Conversation model based on Microsoft's [DialoGPT](https://github.com/microsoft/DialoGPT).
|
||||
This pipeline allows the generation of single or multi-turn conversations between a human and a model.
|
||||
Conversation model based on Microsoft's
|
||||
[DialoGPT](https://github.com/microsoft/DialoGPT). This pipeline allows the
|
||||
generation of single or multi-turn conversations between a human and a model.
|
||||
The DialoGPT's page states that
|
||||
> The human evaluation results indicate that the response generated from DialoGPT is comparable to human response quality
|
||||
> under a single-turn conversation Turing test. ([DialoGPT repository](https://github.com/microsoft/DialoGPT))
|
||||
|
||||
The model uses a `ConversationManager` to keep track of active conversations and generate responses to them.
|
||||
> The human evaluation results indicate that the response generated from
|
||||
> DialoGPT is comparable to human response quality under a single-turn
|
||||
> conversation Turing test.
|
||||
> ([DialoGPT repository](https://github.com/microsoft/DialoGPT))
|
||||
|
||||
The model uses a `ConversationManager` to keep track of active conversations and
|
||||
generate responses to them.
|
||||
|
||||
```rust
|
||||
use rust_bert::pipelines::conversation::{ConversationModel, ConversationManager};
|
||||
|
|
@ -282,19 +373,24 @@ let mut conversation_manager = ConversationManager::new();
|
|||
let conversation_id = conversation_manager.create("Going to the movies tonight - any suggestions?");
|
||||
let output = conversation_model.generate_responses(&mut conversation_manager);
|
||||
```
|
||||
|
||||
Example output:
|
||||
|
||||
```
|
||||
"The Big Lebowski."
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
<details>
|
||||
<summary> <b>5. Natural Language Generation </b> </summary>
|
||||
|
||||
Generate language based on a prompt. GPT2 and GPT available as base models.
|
||||
Include techniques such as beam search, top-k and nucleus sampling, temperature setting and repetition penalty.
|
||||
Supports batch generation of sentences from several prompts. Sequences will be left-padded with the model's padding token if present, the unknown token otherwise.
|
||||
This may impact the results, it is recommended to submit prompts of similar length for best results
|
||||
Include techniques such as beam search, top-k and nucleus sampling, temperature
|
||||
setting and repetition penalty. Supports batch generation of sentences from
|
||||
several prompts. Sequences will be left-padded with the model's padding token if
|
||||
present, the unknown token otherwise. This may impact the results, it is
|
||||
recommended to submit prompts of similar length for best results
|
||||
|
||||
```rust
|
||||
let model = GPT2Generator::new(Default::default())?;
|
||||
|
|
@ -309,7 +405,9 @@ This may impact the results, it is recommended to submit prompts of similar leng
|
|||
|
||||
let output = model.generate(Some(&[input_context_1, input_context_2]), generate_options);
|
||||
```
|
||||
|
||||
Example output:
|
||||
|
||||
```
|
||||
[
|
||||
"The dog's owners, however, did not want to be named. According to the lawsuit, the animal's owner, a 29-year"
|
||||
|
|
@ -320,12 +418,15 @@ Example output:
|
|||
"The cat was attacked by two stray dogs and was taken to a hospital. Two other cats were also injured in the attack and are being treated."
|
||||
]
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
<details>
|
||||
<summary> <b>6. Zero-shot classification </b> </summary>
|
||||
|
||||
Performs zero-shot classification on input sentences with provided labels using a model fine-tuned for Natural Language Inference.
|
||||
Performs zero-shot classification on input sentences with provided labels using
|
||||
a model fine-tuned for Natural Language Inference.
|
||||
|
||||
```rust
|
||||
let sequence_classification_model = ZeroShotClassificationModel::new(Default::default())?;
|
||||
|
||||
|
|
@ -342,18 +443,22 @@ Performs zero-shot classification on input sentences with provided labels using
|
|||
```
|
||||
|
||||
Output:
|
||||
|
||||
```
|
||||
[
|
||||
[ Label { "politics", score: 0.972 }, Label { "public health", score: 0.032 }, Label {"economics", score: 0.006 }, Label {"sports", score: 0.004 } ],
|
||||
[ Label { "politics", score: 0.975 }, Label { "public health", score: 0.0818 }, Label {"economics", score: 0.852 }, Label {"sports", score: 0.001 } ],
|
||||
]
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
<details>
|
||||
<summary> <b>7. Sentiment analysis </b> </summary>
|
||||
|
||||
Predicts the binary sentiment for a sentence. DistilBERT model fine-tuned on SST-2.
|
||||
Predicts the binary sentiment for a sentence. DistilBERT model fine-tuned on
|
||||
SST-2.
|
||||
|
||||
```rust
|
||||
let sentiment_classifier = SentimentModel::new(Default::default())?;
|
||||
|
||||
|
|
@ -365,9 +470,11 @@ Predicts the binary sentiment for a sentence. DistilBERT model fine-tuned on SST
|
|||
|
||||
let output = sentiment_classifier.predict(&input);
|
||||
```
|
||||
|
||||
(Example courtesy of [IMDb](http://www.imdb.com))
|
||||
|
||||
Output:
|
||||
|
||||
```
|
||||
[
|
||||
Sentiment { polarity: Positive, score: 0.9981985493795946 },
|
||||
|
|
@ -375,13 +482,17 @@ Output:
|
|||
Sentiment { polarity: Positive, score: 0.9997248985164333 }
|
||||
]
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
<details>
|
||||
<summary> <b>8. Named Entity Recognition </b> </summary>
|
||||
|
||||
Extracts entities (Person, Location, Organization, Miscellaneous) from text. BERT cased large model fine-tuned on CoNNL03, contributed by the [MDZ Digital Library team at the Bavarian State Library](https://github.com/dbmdz).
|
||||
Extracts entities (Person, Location, Organization, Miscellaneous) from text.
|
||||
BERT cased large model fine-tuned on CoNNL03, contributed by the
|
||||
[MDZ Digital Library team at the Bavarian State Library](https://github.com/dbmdz).
|
||||
Models are currently available for English, German, Spanish and Dutch.
|
||||
|
||||
```rust
|
||||
let ner_model = NERModel::new(default::default())?;
|
||||
|
||||
|
|
@ -392,7 +503,9 @@ Models are currently available for English, German, Spanish and Dutch.
|
|||
|
||||
let output = ner_model.predict(&input);
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
```
|
||||
[
|
||||
[
|
||||
|
|
@ -405,8 +518,9 @@ Output:
|
|||
]
|
||||
]
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
<details>
|
||||
<summary> <b>9. Keywords/keyphrases extraction</b> </summary>
|
||||
|
||||
|
|
@ -427,7 +541,9 @@ fn main() -> anyhow::Result<()> {
|
|||
let output = keyword_extraction_model.predict(&[input])?;
|
||||
}
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
```
|
||||
"rust" - 0.50910604
|
||||
"programming" - 0.35731024
|
||||
|
|
@ -435,12 +551,14 @@ Output:
|
|||
"concurrent" - 0.31229728
|
||||
"program" - 0.29115444
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
<details>
|
||||
<summary> <b>10. Part of Speech tagging </b> </summary>
|
||||
|
||||
Extracts Part of Speech tags (Noun, Verb, Adjective...) from text.
|
||||
|
||||
```rust
|
||||
let pos_model = POSModel::new(default::default())?;
|
||||
|
||||
|
|
@ -448,7 +566,9 @@ Extracts Part of Speech tags (Noun, Verb, Adjective...) from text.
|
|||
|
||||
let output = pos_model.predict(&input);
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
```
|
||||
[
|
||||
Entity { word: "My", score: 0.1560, label: "PRP" }
|
||||
|
|
@ -457,12 +577,15 @@ Output:
|
|||
Entity { word: "Bob", score: 0.7460, label: "NNP" }
|
||||
]
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
<details>
|
||||
<summary> <b>11. Sentence embeddings </b> </summary>
|
||||
|
||||
Generate sentence embeddings (vector representation). These can be used for applications including dense information retrieval.
|
||||
Generate sentence embeddings (vector representation). These can be used for
|
||||
applications including dense information retrieval.
|
||||
|
||||
```rust
|
||||
let model = SentenceEmbeddingsBuilder::remote(
|
||||
SentenceEmbeddingsModelType::AllMiniLmL12V2
|
||||
|
|
@ -475,19 +598,23 @@ Generate sentence embeddings (vector representation). These can be used for appl
|
|||
|
||||
let output = model.encode(&sentences)?;
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
```
|
||||
[
|
||||
[-0.000202666, 0.08148022, 0.03136178, 0.002920636 ...],
|
||||
[0.064757116, 0.048519745, -0.01786038, -0.0479775 ...]
|
||||
]
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
<details>
|
||||
<summary> <b>12. Masked Language Model </b> </summary>
|
||||
|
||||
Predict masked words in input sentences.
|
||||
|
||||
```rust
|
||||
let model = MaskedLanguageModel::new(Default::default())?;
|
||||
|
||||
|
|
@ -498,7 +625,9 @@ Predict masked words in input sentences.
|
|||
|
||||
let output = model.predict(&sentences);
|
||||
```
|
||||
|
||||
Output:
|
||||
|
||||
```
|
||||
[
|
||||
[MaskedToken { text: "college", id: 2267, score: 8.091}],
|
||||
|
|
@ -508,29 +637,61 @@ Output:
|
|||
]
|
||||
]
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
## Benchmarks
|
||||
|
||||
For simple pipelines (sequence classification, tokens classification, question answering) the performance between Python and Rust is expected to be comparable. This is because the most expensive part of these pipeline is the language model itself, sharing a common implementation in the Torch backend. The [End-to-end NLP Pipelines in Rust](https://www.aclweb.org/anthology/2020.nlposs-1.4/) provides a benchmarks section covering all pipelines.
|
||||
For simple pipelines (sequence classification, tokens classification, question
|
||||
answering) the performance between Python and Rust is expected to be comparable.
|
||||
This is because the most expensive part of these pipeline is the language model
|
||||
itself, sharing a common implementation in the Torch backend. The
|
||||
[End-to-end NLP Pipelines in Rust](https://www.aclweb.org/anthology/2020.nlposs-1.4/)
|
||||
provides a benchmarks section covering all pipelines.
|
||||
|
||||
For text generation tasks (summarization, translation, conversation, free text generation), significant benefits can be expected (up to 2 to 4 times faster processing depending on the input and application). The article [Accelerating text generation with Rust](https://guillaume-be.github.io/2020-11-21/generation_benchmarks) focuses on these text generation applications and provides more details on the performance comparison to Python.
|
||||
For text generation tasks (summarization, translation, conversation, free text
|
||||
generation), significant benefits can be expected (up to 2 to 4 times faster
|
||||
processing depending on the input and application). The article
|
||||
[Accelerating text generation with Rust](https://guillaume-be.github.io/2020-11-21/generation_benchmarks)
|
||||
focuses on these text generation applications and provides more details on the
|
||||
performance comparison to Python.
|
||||
|
||||
## Loading pretrained and custom model weights
|
||||
|
||||
The base model and task-specific heads are also available for users looking to expose their own transformer based models.
|
||||
Examples on how to prepare the date using a native tokenizers Rust library are available in `./examples` for BERT, DistilBERT, RoBERTa, GPT, GPT2 and BART.
|
||||
Note that when importing models from Pytorch, the convention for parameters naming needs to be aligned with the Rust schema. Loading of the pre-trained weights will fail if any of the model parameters weights cannot be found in the weight files.
|
||||
If this quality check is to be skipped, an alternative method `load_partial` can be invoked from the variables store.
|
||||
The base model and task-specific heads are also available for users looking to
|
||||
expose their own transformer based models. Examples on how to prepare the date
|
||||
using a native tokenizers Rust library are available in `./examples` for BERT,
|
||||
DistilBERT, RoBERTa, GPT, GPT2 and BART. Note that when importing models from
|
||||
Pytorch, the convention for parameters naming needs to be aligned with the Rust
|
||||
schema. Loading of the pre-trained weights will fail if any of the model
|
||||
parameters weights cannot be found in the weight files. If this quality check is
|
||||
to be skipped, an alternative method `load_partial` can be invoked from the
|
||||
variables store.
|
||||
|
||||
Pretrained models are available on Hugging face's [model hub](https://huggingface.co/models?filter=rust) and can be loaded using `RemoteResources` defined in this library.
|
||||
A conversion utility script is included in `./utils` to convert Pytorch weights to a set of weights compatible with this library. This script requires Python and `torch` to be set-up, and can be used as follows:
|
||||
`python ./utils/convert_model.py path/to/pytorch_model.bin` where `path/to/pytorch_model.bin` is the location of the original Pytorch weights.
|
||||
Pretrained models are available on Hugging face's
|
||||
[model hub](https://huggingface.co/models?filter=rust) and can be loaded using
|
||||
`RemoteResources` defined in this library.
|
||||
|
||||
A conversion utility script is included in `./utils` to convert Pytorch weights
|
||||
to a set of weights compatible with this library. This script requires Python
|
||||
and `torch` to be set-up, and can be used as follows:
|
||||
`python ./utils/convert_model.py path/to/pytorch_model.bin` where
|
||||
`path/to/pytorch_model.bin` is the location of the original Pytorch weights.
|
||||
|
||||
```bash
|
||||
python3 -m venv .venv
|
||||
source .venv/bin/activate
|
||||
|
||||
pip install -r requirements.txt
|
||||
|
||||
python utils/convert_model.py path/to/pytorch_model.bin
|
||||
```
|
||||
|
||||
## Citation
|
||||
|
||||
If you use `rust-bert` for your work, please cite [End-to-end NLP Pipelines in Rust](https://www.aclweb.org/anthology/2020.nlposs-1.4/):
|
||||
If you use `rust-bert` for your work, please cite
|
||||
[End-to-end NLP Pipelines in Rust](https://www.aclweb.org/anthology/2020.nlposs-1.4/):
|
||||
|
||||
```bibtex
|
||||
@inproceedings{becquin-2020-end,
|
||||
title = "End-to-end {NLP} Pipelines in Rust",
|
||||
|
|
@ -545,6 +706,7 @@ If you use `rust-bert` for your work, please cite [End-to-end NLP Pipelines in R
|
|||
|
||||
## Acknowledgements
|
||||
|
||||
Thank you to [Hugging Face](https://huggingface.co) for hosting a set of weights compatible with this Rust library.
|
||||
The list of ready-to-use pretrained models is listed at [https://huggingface.co/models?filter=rust](https://huggingface.co/models?filter=rust).
|
||||
|
||||
Thank you to [Hugging Face](https://huggingface.co) for hosting a set of weights
|
||||
compatible with this Rust library. The list of ready-to-use pretrained models is
|
||||
listed at
|
||||
[https://huggingface.co/models?filter=rust](https://huggingface.co/models?filter=rust).
|
||||
|
|
|
|||
|
|
@ -1,3 +0,0 @@
|
|||
torch == 1.13.1
|
||||
requests == 2.32.0
|
||||
numpy == 1.23.4
|
||||
|
|
@ -340,6 +340,7 @@ impl BartDecoder {
|
|||
}
|
||||
}
|
||||
|
||||
#[allow(dead_code)]
|
||||
///Container holding a BART decoder output
|
||||
pub struct BartDecoderOutput {
|
||||
/// last decoder layer hidden state
|
||||
|
|
|
|||
|
|
@ -210,6 +210,7 @@ impl ProphetNetEncoder {
|
|||
}
|
||||
}
|
||||
|
||||
#[allow(dead_code)]
|
||||
/// Container for the ProphetNet encoder output.
|
||||
pub struct ProphetNetEncoderOutput {
|
||||
/// Last hidden states from the model
|
||||
|
|
|
|||
|
|
@ -143,6 +143,7 @@ impl ChunkReformerFeedForward {
|
|||
}
|
||||
}
|
||||
|
||||
#[allow(dead_code)]
|
||||
pub struct ReformerLayerOutput {
|
||||
pub attention_output: Tensor,
|
||||
pub hidden_states: Tensor,
|
||||
|
|
|
|||
|
|
@ -207,6 +207,7 @@ impl ReformerLMHead {
|
|||
}
|
||||
}
|
||||
|
||||
#[allow(dead_code)]
|
||||
pub struct PaddedReformerInput {
|
||||
pub input_ids: Option<Tensor>,
|
||||
pub input_embeds: Option<Tensor>,
|
||||
|
|
|
|||
|
|
@ -541,6 +541,7 @@ impl T5Stack {
|
|||
}
|
||||
}
|
||||
|
||||
#[allow(dead_code)]
|
||||
pub struct T5BlockOutput {
|
||||
pub hidden_states: Tensor,
|
||||
pub self_attention_weights: Option<Tensor>,
|
||||
|
|
|
|||
|
|
@ -50,13 +50,20 @@ import sys
|
|||
import zipfile
|
||||
from pathlib import Path
|
||||
from typing import Dict
|
||||
|
||||
import os
|
||||
import numpy as np
|
||||
import torch
|
||||
from numpy.lib.format import write_array
|
||||
from numpy.lib.npyio import zipfile_factory
|
||||
# from numpy.lib.npyio import zipfile_factory
|
||||
from torch import Tensor
|
||||
|
||||
def zipfile_factory(file, *args, **kwargs):
|
||||
if not hasattr(file, 'read'):
|
||||
file = os.fspath(file)
|
||||
import zipfile
|
||||
kwargs['allowZip64'] = True
|
||||
kwargs['compresslevel'] = 4
|
||||
return zipfile.ZipFile(file, *args, **kwargs)
|
||||
|
||||
def get_bf16_repr(input_tensor: torch.Tensor) -> np.ndarray:
|
||||
"""Convert a bfloat16 tensor to an equivalent byte representation in Numpy.
|
||||
|
|
@ -125,6 +132,12 @@ if __name__ == "__main__":
|
|||
help="Use this flag to enable automatic download of the libtorch library.",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
logger = logging.getLogger('convert_model')
|
||||
logger.setLevel(logging.DEBUG)
|
||||
fh = logging.FileHandler('convert_model.log')
|
||||
fh.setLevel(logging.DEBUG)
|
||||
logger.addHandler(fh)
|
||||
|
||||
target_folder = Path(args.source_file[0]).parent
|
||||
with zipfile_factory(
|
||||
|
|
@ -133,7 +146,7 @@ if __name__ == "__main__":
|
|||
for source_file_or_pattern in args.source_file:
|
||||
source_files = glob.glob(source_file_or_pattern)
|
||||
for source_file in source_files:
|
||||
logging.info(f"Processing source file {source_file}...")
|
||||
logger.info(f"Processing source file {source_file}")
|
||||
nps = {}
|
||||
source_file = Path(source_file)
|
||||
weights = torch.load(str(source_file), map_location="cpu")
|
||||
|
|
@ -168,11 +181,11 @@ if __name__ == "__main__":
|
|||
)
|
||||
else:
|
||||
nps[k] = np.ascontiguousarray(tensor)
|
||||
logging.info(
|
||||
logger.info(
|
||||
f"converted {k} - {str(sys.getsizeof(nps[k]))} bytes"
|
||||
)
|
||||
else:
|
||||
logging.info(f"skipped non-tensor object: {k}")
|
||||
logger.info(f"skipped non-tensor object: {k}")
|
||||
append_to_zipf(nps, output_zipfile)
|
||||
|
||||
source = str(target_folder / "model.npz")
|
||||
|
|
|
|||
11
utils/requirements.txt
Normal file
11
utils/requirements.txt
Normal file
|
|
@ -0,0 +1,11 @@
|
|||
filelock==3.15.3
|
||||
fsspec==2024.6.0
|
||||
Jinja2==3.1.4
|
||||
MarkupSafe==2.1.5
|
||||
mpmath==1.3.0
|
||||
networkx==3.3
|
||||
numpy==2.0.0
|
||||
sympy==1.12.1
|
||||
torch==2.3.1
|
||||
typing_extensions==4.12.2
|
||||
requests==2.32.0
|
||||
Loading…
Reference in New Issue
Block a user