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--- |
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base_model: sentence-transformers/all-MiniLM-L6-v2 |
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library_name: setfit |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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tags: |
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- setfit |
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- absa |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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widget: |
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- text: world:Though Arthur skips to another world, he's clearly from our own |
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- text: attire:Among those are the army of doglike and winged creatures, all dressed |
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in attire befitting a civilization one hundred and fifty years ago |
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- text: Mister Monday:This is a 361 page book about a boy named Arthur Penhaligon |
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who is destined to die an early death, but is saved by a key given to him by a |
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mysterious man named Mister Monday |
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- text: parents:Do their parents understand or even care about them? Are they ready |
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for sex? Meanwhile can Maggie and Dennis learn to communicate enough to stay together? |
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- text: boy:This is a 361 page book about a boy named Arthur Penhaligon who is destined |
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to die an early death, but is saved by a key given to him by a mysterious man |
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named Mister Monday |
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inference: false |
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model-index: |
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- name: SetFit Aspect Model with sentence-transformers/all-MiniLM-L6-v2 |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: Unknown |
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type: unknown |
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split: test |
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metrics: |
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- type: accuracy |
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value: 0.8541666666666666 |
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name: Accuracy |
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--- |
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# SetFit Aspect Model with sentence-transformers/all-MiniLM-L6-v2 |
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. In particular, this model is in charge of filtering aspect span candidates. |
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The model has been trained using an efficient few-shot learning technique that involves: |
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
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2. Training a classification head with features from the fine-tuned Sentence Transformer. |
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This model was trained within the context of a larger system for ABSA, which looks like so: |
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1. Use a spaCy model to select possible aspect span candidates. |
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2. **Use this SetFit model to filter these possible aspect span candidates.** |
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3. Use a SetFit model to classify the filtered aspect span candidates. |
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## Model Details |
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### Model Description |
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- **Model Type:** SetFit |
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- **Sentence Transformer body:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) |
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- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
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- **spaCy Model:** en_core_web_lg |
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- **SetFitABSA Aspect Model:** [omymble/setfit-absa-books-aspect](https://huggingface.co/omymble/setfit-absa-books-aspect) |
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- **SetFitABSA Polarity Model:** [omymble/setfit-absa-books-polarity](https://huggingface.co/omymble/setfit-absa-books-polarity) |
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- **Maximum Sequence Length:** 256 tokens |
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- **Number of Classes:** 2 classes |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
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<!-- - **Language:** Unknown --> |
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### Model Sources |
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- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) |
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- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) |
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) |
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### Model Labels |
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| Label | Examples | |
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|:----------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| aspect | <ul><li>'Poirot:After reading nearly 30 novels, Poirot had been a part of life'</li><li>'Michael Dobbs:The cast of characters in this sweeping story by Michael Dobbs of political maneuvering, skullduggery, and backstabbing is an historical Who\'s Who of the times: the ailing, haughty, and pacifist Chamberlain, who personifies England\'s bitter memories of the Great War and the popular concept of "never again"; the ambitious and self-absorbed Churchill, whose pugnacity sometimes clouds prudence; the defeatist, philandering, and anti-Semitic U'</li><li>"Jack:Jack is a wonderful beleaguered hero who starts off by quickly realizing he don't know jack even about himself and as he investigates realizes each new clue proves he knows even less than he thought"</li></ul> | |
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| no aspect | <ul><li>'novels:After reading nearly 30 novels, Poirot had been a part of life'</li><li>'part:After reading nearly 30 novels, Poirot had been a part of life'</li><li>'life:After reading nearly 30 novels, Poirot had been a part of life'</li></ul> | |
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## Evaluation |
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### Metrics |
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| Label | Accuracy | |
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|:--------|:---------| |
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| **all** | 0.8542 | |
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## Uses |
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### Direct Use for Inference |
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First install the SetFit library: |
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```bash |
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pip install setfit |
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``` |
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Then you can load this model and run inference. |
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```python |
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from setfit import AbsaModel |
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# Download from the 🤗 Hub |
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model = AbsaModel.from_pretrained( |
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"omymble/setfit-absa-books-aspect", |
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"omymble/setfit-absa-books-polarity", |
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) |
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# Run inference |
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preds = model("The food was great, but the venue is just way too busy.") |
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``` |
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### Downstream Use |
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*List how someone could finetune this model on their own dataset.* |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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### Training Set Metrics |
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| Training set | Min | Median | Max | |
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|:-------------|:----|:--------|:----| |
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| Word count | 6 | 34.7122 | 79 | |
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| Label | Training Sample Count | |
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|:----------|:----------------------| |
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| no aspect | 280 | |
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| aspect | 57 | |
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### Training Hyperparameters |
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- batch_size: (256, 256) |
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- num_epochs: (2, 2) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- body_learning_rate: (2e-05, 1e-05) |
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- head_learning_rate: 0.01 |
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- loss: CosineSimilarityLoss |
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- distance_metric: cosine_distance |
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- margin: 0.25 |
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- end_to_end: False |
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- use_amp: True |
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- warmup_proportion: 0.1 |
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- seed: 42 |
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- eval_max_steps: -1 |
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- load_best_model_at_end: True |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:----------:|:-------:|:-------------:|:---------------:| |
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| 0.0031 | 1 | 0.3698 | - | |
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| 0.1558 | 50 | 0.3449 | 0.3303 | |
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| 0.3115 | 100 | 0.3032 | 0.294 | |
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| 0.4673 | 150 | 0.2878 | 0.266 | |
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| 0.6231 | 200 | 0.2414 | 0.2535 | |
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| 0.7788 | 250 | 0.2456 | 0.2494 | |
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| 0.9346 | 300 | 0.2374 | 0.2477 | |
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| 1.0903 | 350 | 0.2407 | 0.2472 | |
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| 1.2461 | 400 | 0.2406 | 0.2467 | |
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| 1.4019 | 450 | 0.2276 | 0.2465 | |
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| 1.5576 | 500 | 0.2248 | 0.2465 | |
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| 1.7134 | 550 | 0.2241 | 0.2464 | |
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| **1.8692** | **600** | **0.2245** | **0.2463** | |
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* The bold row denotes the saved checkpoint. |
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### Framework Versions |
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- Python: 3.10.12 |
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- SetFit: 1.0.3 |
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- Sentence Transformers: 3.0.1 |
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- spaCy: 3.7.4 |
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- Transformers: 4.39.0 |
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- PyTorch: 2.3.1+cu121 |
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- Datasets: 2.20.0 |
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- Tokenizers: 0.15.2 |
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## Citation |
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### BibTeX |
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```bibtex |
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@article{https://doi.org/10.48550/arxiv.2209.11055, |
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doi = {10.48550/ARXIV.2209.11055}, |
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url = {https://arxiv.org/abs/2209.11055}, |
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author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Efficient Few-Shot Learning Without Prompts}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
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