# Frame Semantic Transformer
[](https://github.com/chanind/frame-semantic-transformer)
[](https://pypi.org/project/frame-semantic-transformer/)
Frame-based semantic parsing library trained on [FrameNet](https://framenet2.icsi.berkeley.edu/) and built on HuggingFace's [T5 Transformer](https://huggingface.co/docs/transformers/model_doc/t5)
**Live Demo: [chanind.github.io/frame-semantic-transformer](https://chanind.github.io/frame-semantic-transformer)**
Full docs: [frame-semantic-transformer.readthedocs.io](https://frame-semantic-transformer.readthedocs.io/)
## About
This library draws heavily on [Open-Sesame](https://github.com/swabhs/open-sesame) ([paper](https://arxiv.org/abs/1706.09528)) for inspiration on training and evaluation on FrameNet 1.7, and uses ideas from the paper [Open-Domain Frame Semantic Parsing Using Transformers](https://arxiv.org/abs/2010.10998) for using T5 as a frame-semantic parser. [SimpleT5](https://github.com/Shivanandroy/simpleT5) was also used as a base for the initial training setup.
More details: [FrameNet Parsing with Transformers Blog Post](https://chanind.github.io/ai/2022/05/24/framenet-transformers.html)
## Performance
This library uses the same train/dev/test documents and evaluation methodology as Open-Sesame, so that the results should be comparable between the 2 libraries. There are 2 pretrained models available, `base` and `small`, corresponding to `t5-base` and `t5-small` in Huggingface, respectively.
| Task | Sesame F1 (dev/test) | Small Model F1 (dev/test) | Base Model F1 (dev/test) |
| ---------------------- | -------------------- | ------------------------- | ------------------------ |
| Trigger identification | 0.80 / 0.73 | 0.75 / 0.71 | 0.78 / 0.74 |
| Frame classification | 0.90 / 0.87 | 0.87 / 0.86 | 0.91 / 0.89 |
| Argument extraction | 0.61 / 0.61 | 0.76 / 0.73 | 0.78 / 0.75 |
The base model performs similarly to Open-Sesame on trigger identification and frame classification tasks, but outperforms it by a significant margin on argument extraction. The small pretrained model has lower F1 than base across the board, but is 1/4 the size and still outperforms Open-Sesame at argument extraction.
## Installation
```
pip install frame-semantic-transformer
```
## Usage
### Inference
The main entry to interacting with the library is the `FrameSemanticTransformer` class, as shown below. For inference the `detect_frames()` method is likely all that is needed to perform frame parsing.
```python
from frame_semantic_transformer import FrameSemanticTransformer
frame_transformer = FrameSemanticTransformer()
result = frame_transformer.detect_frames("The hallway smelt of boiled cabbage and old rag mats.")
print(f"Results found in: {result.sentence}")
for frame in result.frames:
print(f"FRAME: {frame.name}")
for element in frame.frame_elements:
print(f"{element.name}: {element.text}")
```
The result returned from `detect_frames()` is an object containing `sentence`, a parsed version of the original sentence text, `trigger_locations`, the indices within the sentence where frame triggers were detected, and `frames`, a list of all detected frames in the sentence. Within `frames`, each object containes `name` which corresponds to the FrameNet name of the frame, `trigger_location` corresponding to which trigger in the text this frame this frame uses, and `frame_elements` containing a list of all relevant frame elements found in the text.
For more efficient bulk processing of text, there's a `detect_frames_bulk` method which will process a list of sentences in batches. You can control the batch size using the `batch_size` param. By default this is `8`.
```python
frame_transformer = FrameSemanticTransformer(batch_size=16)
result = frame_transformer.detect_frames_bulk([
"I'm getting quite hungry, but I can wait a bit longer.",
"The chef gave the food to the customer.",
"The hallway smelt of boiled cabbage and old rag mats.",
])
```
**Note**: It's not recommended to pass more than a single sentence per string to `detect_frames()` or `detect_frames_bulk()`. If you have a paragraph of text to process, it's best to split the paragraph into a list of sentences and pass the sentences as a list to `detect_frames_bulk()`. Only single sentences per string were used during training, so it's not clear how the model will handle multiple sentences in the same string.
```python
# ❌ Bad, don't do this
frame_transformer.detect_frames("Fuzzy Wuzzy was a bear. Fuzzy Wuzzy had no hair.")
# 👍 Do this instead
frame_transformer.detect_frames_bulk([
"Fuzzy Wuzzy was a bear.",
"Fuzzy Wuzzy had no hair.",
])
```
### Running on GPU vs CPU
By default, `FrameSemanticTransformer` will attempt to use a GPU if one is available. If you'd like to explictly set whether to run on GPU vs CPU, you can pass the `use_gpu` param.
```python
# force the model to run on the CPU
frame_transformer = FrameSemanticTransformer(use_gpu=False)
```
### Loading Models
There are currently 2 available pre-trained models for inference, called `base` and `small`, fine-tuned from HuggingFace's [t5-base](https://huggingface.co/t5-base) and [t5-small](https://huggingface.co/t5-small) model respectively. If a local fine-tuned t5 model exists that can be loaded as well. If no model is specified, the `base` model will be used.
```
base_transformer = FrameSemanticTransformer("base") # this is also the default
small_transformer = FrameSemanticTransformer("small") # a smaller pretrained model which is faster to run
custom_transformer = FrameSemanticTransformer("/path/to/model") # load a custom t5 model
```
By default, models are lazily loaded when `detect_frames()` is first called. If you want to load the model sooner, you can call `setup()` on a `FrameSemanticTransformer` instance to load models immediately.
```
frame_transformer = FrameSemanticTransformer()
frame_transformer.setup() # load models immediately
```
## Contributing
Any contributions to improve this project are welcome! Please open an issue or pull request in this repo with any bugfixes / changes / improvements you have!
This project uses [Black](https://github.com/psf/black) for code formatting, [Flake8](https://flake8.pycqa.org/en/latest/) for linting, and [Pytest](https://docs.pytest.org/) for tests. Make sure any changes you submit pass these code checks in your PR. If you have trouble getting these to run feel free to open a pull-request regardless and we can discuss further in the PR.
## License
The code contained in this repo is released under a MIT license, however the pretrained models are released under an Apache 2.0 license in accordance with FrameNet training data and HuggingFace's T5 base models.
## Citation
If you use Frame semantic transformer in your work, please cite the following:
```bibtex
@article{chanin2023opensource,
title={Open-source Frame Semantic Parsing},
author={Chanin, David},
journal={arXiv preprint arXiv:2303.12788},
year={2023}
}
```
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"description": "# Frame Semantic Transformer\n\n[](https://github.com/chanind/frame-semantic-transformer)\n[](https://pypi.org/project/frame-semantic-transformer/)\n\nFrame-based semantic parsing library trained on [FrameNet](https://framenet2.icsi.berkeley.edu/) and built on HuggingFace's [T5 Transformer](https://huggingface.co/docs/transformers/model_doc/t5)\n\n**Live Demo: [chanind.github.io/frame-semantic-transformer](https://chanind.github.io/frame-semantic-transformer)**\n\nFull docs: [frame-semantic-transformer.readthedocs.io](https://frame-semantic-transformer.readthedocs.io/)\n\n## About\n\nThis library draws heavily on [Open-Sesame](https://github.com/swabhs/open-sesame) ([paper](https://arxiv.org/abs/1706.09528)) for inspiration on training and evaluation on FrameNet 1.7, and uses ideas from the paper [Open-Domain Frame Semantic Parsing Using Transformers](https://arxiv.org/abs/2010.10998) for using T5 as a frame-semantic parser. [SimpleT5](https://github.com/Shivanandroy/simpleT5) was also used as a base for the initial training setup.\n\nMore details: [FrameNet Parsing with Transformers Blog Post](https://chanind.github.io/ai/2022/05/24/framenet-transformers.html)\n\n## Performance\n\nThis library uses the same train/dev/test documents and evaluation methodology as Open-Sesame, so that the results should be comparable between the 2 libraries. There are 2 pretrained models available, `base` and `small`, corresponding to `t5-base` and `t5-small` in Huggingface, respectively.\n\n| Task | Sesame F1 (dev/test) | Small Model F1 (dev/test) | Base Model F1 (dev/test) |\n| ---------------------- | -------------------- | ------------------------- | ------------------------ |\n| Trigger identification | 0.80 / 0.73 | 0.75 / 0.71 | 0.78 / 0.74 |\n| Frame classification | 0.90 / 0.87 | 0.87 / 0.86 | 0.91 / 0.89 |\n| Argument extraction | 0.61 / 0.61 | 0.76 / 0.73 | 0.78 / 0.75 |\n\nThe base model performs similarly to Open-Sesame on trigger identification and frame classification tasks, but outperforms it by a significant margin on argument extraction. The small pretrained model has lower F1 than base across the board, but is 1/4 the size and still outperforms Open-Sesame at argument extraction.\n\n## Installation\n\n```\npip install frame-semantic-transformer\n```\n\n## Usage\n\n### Inference\n\nThe main entry to interacting with the library is the `FrameSemanticTransformer` class, as shown below. For inference the `detect_frames()` method is likely all that is needed to perform frame parsing.\n\n```python\nfrom frame_semantic_transformer import FrameSemanticTransformer\n\nframe_transformer = FrameSemanticTransformer()\n\nresult = frame_transformer.detect_frames(\"The hallway smelt of boiled cabbage and old rag mats.\")\n\nprint(f\"Results found in: {result.sentence}\")\nfor frame in result.frames:\n print(f\"FRAME: {frame.name}\")\n for element in frame.frame_elements:\n print(f\"{element.name}: {element.text}\")\n```\n\nThe result returned from `detect_frames()` is an object containing `sentence`, a parsed version of the original sentence text, `trigger_locations`, the indices within the sentence where frame triggers were detected, and `frames`, a list of all detected frames in the sentence. Within `frames`, each object containes `name` which corresponds to the FrameNet name of the frame, `trigger_location` corresponding to which trigger in the text this frame this frame uses, and `frame_elements` containing a list of all relevant frame elements found in the text.\n\nFor more efficient bulk processing of text, there's a `detect_frames_bulk` method which will process a list of sentences in batches. You can control the batch size using the `batch_size` param. By default this is `8`.\n\n```python\nframe_transformer = FrameSemanticTransformer(batch_size=16)\n\nresult = frame_transformer.detect_frames_bulk([\n \"I'm getting quite hungry, but I can wait a bit longer.\",\n \"The chef gave the food to the customer.\",\n \"The hallway smelt of boiled cabbage and old rag mats.\",\n])\n```\n\n**Note**: It's not recommended to pass more than a single sentence per string to `detect_frames()` or `detect_frames_bulk()`. If you have a paragraph of text to process, it's best to split the paragraph into a list of sentences and pass the sentences as a list to `detect_frames_bulk()`. Only single sentences per string were used during training, so it's not clear how the model will handle multiple sentences in the same string.\n\n```python\n# \u274c Bad, don't do this\nframe_transformer.detect_frames(\"Fuzzy Wuzzy was a bear. Fuzzy Wuzzy had no hair.\")\n\n# \ud83d\udc4d Do this instead\nframe_transformer.detect_frames_bulk([\n \"Fuzzy Wuzzy was a bear.\",\n \"Fuzzy Wuzzy had no hair.\",\n])\n```\n\n### Running on GPU vs CPU\n\nBy default, `FrameSemanticTransformer` will attempt to use a GPU if one is available. If you'd like to explictly set whether to run on GPU vs CPU, you can pass the `use_gpu` param.\n\n```python\n# force the model to run on the CPU\nframe_transformer = FrameSemanticTransformer(use_gpu=False)\n```\n\n### Loading Models\n\nThere are currently 2 available pre-trained models for inference, called `base` and `small`, fine-tuned from HuggingFace's [t5-base](https://huggingface.co/t5-base) and [t5-small](https://huggingface.co/t5-small) model respectively. If a local fine-tuned t5 model exists that can be loaded as well. If no model is specified, the `base` model will be used.\n\n```\nbase_transformer = FrameSemanticTransformer(\"base\") # this is also the default\nsmall_transformer = FrameSemanticTransformer(\"small\") # a smaller pretrained model which is faster to run\ncustom_transformer = FrameSemanticTransformer(\"/path/to/model\") # load a custom t5 model\n```\n\nBy default, models are lazily loaded when `detect_frames()` is first called. If you want to load the model sooner, you can call `setup()` on a `FrameSemanticTransformer` instance to load models immediately.\n\n```\nframe_transformer = FrameSemanticTransformer()\nframe_transformer.setup() # load models immediately\n```\n\n## Contributing\n\nAny contributions to improve this project are welcome! Please open an issue or pull request in this repo with any bugfixes / changes / improvements you have!\n\nThis project uses [Black](https://github.com/psf/black) for code formatting, [Flake8](https://flake8.pycqa.org/en/latest/) for linting, and [Pytest](https://docs.pytest.org/) for tests. Make sure any changes you submit pass these code checks in your PR. 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