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https://github.com/pszemraj/textsum
CLI & Python API to easily summarize text-based files with transformers
https://github.com/pszemraj/textsum
batch-processing inference inference-api pipeline summarization summary text text-to-text-transformer transformer transformers
Last synced: 10 days ago
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CLI & Python API to easily summarize text-based files with transformers
- Host: GitHub
- URL: https://github.com/pszemraj/textsum
- Owner: pszemraj
- License: apache-2.0
- Created: 2022-12-18T01:06:09.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-02-18T22:52:10.000Z (9 months ago)
- Last Synced: 2024-10-23T06:55:29.493Z (18 days ago)
- Topics: batch-processing, inference, inference-api, pipeline, summarization, summary, text, text-to-text-transformer, transformer, transformers
- Language: Python
- Homepage:
- Size: 49.8 KB
- Stars: 124
- Watchers: 5
- Forks: 9
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Authors: AUTHORS.md
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README
# textsum
> a utility for using transformers summarization models on text docs 🖇
This package provides easy-to-use interfaces for using summarization models on text documents of arbitrary length. Currently implemented interfaces include a python API, CLI, and a shareable demo app.
For details, explanations, and docs, see the [wiki](https://github.com/pszemraj/textsum/wiki)
---
- [textsum](#textsum)
- [🔦 Quick Start Guide](#-quick-start-guide)
- [Installation](#installation)
- [Full Installation](#full-installation)
- [Extra Features](#extra-features)
- [Usage](#usage)
- [Python API](#python-api)
- [CLI](#cli)
- [Demo App](#demo-app)
- [Models](#models)
- [Advanced Configuration](#advanced-configuration)
- [Parameters](#parameters)
- [8-bit Quantization \& TensorFloat32](#8-bit-quantization--tensorfloat32)
- [Using Optimum ONNX Runtime](#using-optimum-onnx-runtime)
- [Force Cache](#force-cache)
- [Compile Model](#compile-model)
- [Contributing](#contributing)
- [Road Map](#road-map)
---
## 🔦 Quick Start Guide
1. Install the package with pip:
```bash
pip install textsum
```
2. Import the package and create a summarizer:
```python
from textsum.summarize import Summarizer
summarizer = Summarizer() # loads default model and parameters
```
3. Summarize a text string:
```python
text = "This is a long string of text that will be summarized."
summary = summarizer.summarize_string(text)
print(f'Summary: {summary}')
```
---
## Installation
Install using pip with Python 3.8 or later (_after creating a virtual environment_):
```bash
pip install textsum
```
The `textsum` package is now installed in your virtual environment. [CLI commands](#cli) are available in your terminal, and the [python API](#python-api) is available in your python environment.
### Full Installation
For a full installation, which includes additional features such as PDF OCR, Gradio UI demo, and Optimum, run the following commands:
```bash
git clone https://github.com/pszemraj/textsum.git
cd textsum
# create a virtual environment (optional)
pip install -e .[all]
```
### Extra Features
The package also supports a number of optional extra features, which can be installed as follows:
- `8bit`: Install with `pip install -e .[8bit]`
- `optimum`: Install with `pip install -e .[optimum]`
- `PDF`: Install with `pip install -e .[PDF]`
- `app`: Install with `pip install -e .[app]`
- `unidecode`: Install with `pip install -e .[unidecode]`
Read below for more details on how to use these features.
> _Note:_ The `unidecode` extra is a GPL-licensed dependency that is not included by default with the `clean-text` python package. While it can be used for text cleaning pre-summarization, it generally should not make a significant difference in most use cases.
## Usage
There are three ways to use this package:
1. [python API](#python-api)
2. [CLI](#cli)
3. [Demo App](#demo-app)
### Python API
To use the python API, import the `Summarizer` class and instantiate it. This will load the default model and parameters.
You can then use the `summarize_string` method to summarize a long text string.
```python
from textsum.summarize import Summarizer
summarizer = Summarizer() # loads default model and parameters
# summarize a long string
out_str = summarizer.summarize_string('This is a long string of text that will be summarized.')
print(f'summary: {out_str}')
```
you can also directly summarize a file:
```python
out_path = summarizer.summarize_file('/path/to/file.txt')
print(f'summary saved to {out_path}')
```
### CLI
To summarize a directory of text files, run the following command:
```bash
textsum-dir /path/to/dir
```
A full list:
Click to expand table
| Flag | Description |
| -------------------------------- | ---------------------------------------- |
| `--output_dir` | Specify the output directory |
| `--model` | Specify the model to use |
| `--no_cuda` | Disable CUDA |
| `--tf32` | Use TF32 precision |
| `--force_cache` | Force cache usage |
| `--load_in_8bit` | Load in 8-bit mode |
| `--compile` | Compile the model |
| `--optimum_onnx` | Use optimum ONNX |
| `--batch_length` | Specify the batch length |
| `--batch_stride` | Specify the batch stride |
| `--num_beams` | Specify the number of beams |
| `--length_penalty` | Specify the length penalty |
| `--repetition_penalty` | Specify the repetition penalty |
| `--max_length_ratio` | Specify the maximum length ratio |
| `--min_length` | Specify the minimum length |
| `--encoder_no_repeat_ngram_size` | Specify the encoder no repeat ngram size |
| `--no_repeat_ngram_size` | Specify the no repeat ngram size |
| `--early_stopping` | Enable early stopping |
| `--shuffle` | Shuffle the input data |
| `--lowercase` | Convert input to lowercase |
| `--loglevel` | Specify the log level |
| `--logfile` | Specify the log file |
| `--file_extension` | Specify the file extension |
| `--skip_completed` | Skip completed files |
Some useful options are:
Arguments:
- `input_dir`: The directory containing the input text files to be summarized.
- `--model`: model name or path to use for summarization. (Optional)
- `--shuffle`: Shuffle the input files before processing. (Optional)
- `--skip_completed`: Skip already completed files in the output directory. (Optional)
- `--batch_length`: The maximum length of each input batch. Default is 4096. (Optional)
- `--output_dir`: The directory to write the summarized output files. Default is `./summarized/`. (Optional)
For more information, run the following:
```bash
textsum-dir --help
```
### Demo App
For convenience, a UI demo[^1] is provided using [gradio](https://gradio.app/). To ensure you have the dependencies installed, clone the repo and run the following command:
```bash
pip install textsum[app]
```
To run the demo, run the following command:
```bash
textsum-ui
```
This will start a local server that you can access in your browser & a shareable link will be printed to the console.
[^1]: The demo is minimal but will be expanded to accept other arguments and options.
## Models
Summarization is a memory-intensive task, and the [default model is relatively small and efficient](https://huggingface.co/pszemraj/long-t5-tglobal-base-16384-book-summary) for long-form text summarization. If you want to use a bigger model, you can specify the `model_name_or_path` argument when instantiating the `Summarizer` class.
```python
summarizer = Summarizer(model_name_or_path='pszemraj/long-t5-tglobal-xl-16384-book-summary')
```
You can also use the `-m` argument when using the CLI:
```bash
textsum-dir /path/to/dir -m pszemraj/long-t5-tglobal-xl-16384-book-summary
```
Any [text-to-text](https://huggingface.co/models?filter=text2text) or [summarization](https://huggingface.co/models?filter=summarization) model from the [HuggingFace model hub](https://huggingface.co/models) can be used. Models are automatically downloaded and cached in `~/.cache/huggingface/hub`.
---
## Advanced Configuration
### Parameters
Memory usage can also be reduced by adjusting the parameters for inference. This is discussed in detail in the [project wiki](https://github.com/pszemraj/textsum/wiki).
tl;dr for this README: use the `summarizer.set_inference_params()` and `summarizer.get_inference_params()` methods to adjust the parameters for inference from either a python `dict` or a JSON file.
Support for `GenerationConfig` as the primary method to adjust inference parameters is planned for a future release.
### 8-bit Quantization & TensorFloat32
Some methods of reducing memory usage _if you have compatible hardware_ include loading the model in 8-bit precision via [LLM.int8](https://arxiv.org/abs/2208.07339) and using the `--tf32` flag to use TensorFloat32 precision. See the [transformers docs](https://huggingface.co/docs/transformers/perf_infer_gpu_one#efficient-inference-on-a-single-gpu) for more details on how this works. Using LLM.int8 requires the [bitsandbytes](https://github.com/TimDettmers/bitsandbytes) package, which can either be installed directly or via the `textsum[8bit]` extra:
```bash
pip install textsum[8bit]
```
To use these options, use the `-8bit` and `--tf32` flags when using the CLI:
```bash
textsum-dir /path/to/dir -8bit --tf32
```
Or in python, using the `load_in_8bit` argument:
```python
summarizer = Summarizer(load_in_8bit=True)
```
If using the python API, it's better to initiate tf32 yourself; see [here](https://huggingface.co/docs/transformers/perf_train_gpu_one#tf32) for how.
### Using Optimum ONNX Runtime
> ⚠️ **Note:** This feature is experimental and might not work as expected. Use at your own risk. ⚠️🧪
ONNX Runtime is a performance-focused inference engine for ONNX models. It can be used to enhance the speed of model predictions, especially on Windows and in environments where GPU acceleration is not available. If you want to use ONNX runtime for inference, you need to set `optimum_onnx=True` when initializing the `Summarizer` class.
First, install with `pip install textsum[optimum]`. Then, you can use the following code to initialize the `Summarizer` class with ONNX runtime:
```python
summarizer = Summarizer(optimum_onnx=True)
```
**Notes:**
1. ONNX runtime+cuda needs an additional package. Manually install `onnxruntime-gpu` if you plan to use ONNX with GPU.
2. Using ONNX runtime might lead to different behavior in certain models. It is recommended to test the model with and without ONNX runtime **the same input text** before using it for anything important.
### Force Cache
By default, the summarization model uses past computations to speed up decoding. If you want to force the model to always use cache irrespective of the model's default behavior, you can set `force_cache=True` when initializing the `Summarizer` class.
```python
summarizer = Summarizer(force_cache=True)
```
**Note:** Setting `force_cache=True` might lead to different behavior in certain models.
### Compile Model
By default, the model isn't compiled for efficient inference. If you want to compile the model for faster inference times, you can set `compile_model=True` when initializing the `Summarizer` class.
```python
summarizer = Summarizer(compile_model=True)
```
**Note:** Compiling the model might not be supported on all platforms and requires pytorch > 2.0.0.
---
## Contributing
Contributions are welcome! Please open an issue or PR if you have any ideas or suggestions.
See the [CONTRIBUTING.md](CONTRIBUTING.md) file for details on how to contribute.
## Road Map
- [x] add CLI for summarization of all text files in a directory
- [x] python API for summarization of text docs
- [ ] add argparse CLI for UI demo
- [x] put on PyPI
- [x] LLM.int8 inference
- [x] optimum inference integration
- [ ] better documentation [in the wiki](https://github.com/pszemraj/textsum/wiki), details on improving performance (speed, quality, memory usage, etc.)
- [x] in-progress
- [ ] improvements to the PDF OCR helper module (_TBD - may focus more on being a summarization tool_)
_Other ideas? Open an issue or PR!_
---
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