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--- |
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base_model: sentence-transformers/all-mpnet-base-v2 |
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library_name: setfit |
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metrics: |
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- f1 |
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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: bargain:Monday nights are a bargain at the $28 prix fix - this includes a |
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three course meal plus *three* glasses of wine paired with each course. |
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- text: seated:We walked in on a Wednesday night and were seated promptly. |
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- text: drinks:While most people can attest to spending over $50 on drinks in New |
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York bars and hardly feeling a thing, the drinks here are plentiful and unique. |
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- text: Lassi:I ordered a Lassi and asked 4 times for it but never got it. |
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- text: stomach:Check it out, it won't hurt your stomach or your wallet. |
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inference: false |
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model-index: |
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- name: SetFit Aspect Model with sentence-transformers/all-mpnet-base-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: f1 |
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value: 0.923076923076923 |
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name: F1 |
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--- |
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# SetFit Aspect Model with sentence-transformers/all-mpnet-base-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) |
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- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance |
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- **spaCy Model:** en_core_web_trf |
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- **SetFitABSA Aspect Model:** [MattiaTintori/Final_aspect_Colab](https://huggingface.co/MattiaTintori/Final_aspect_Colab) |
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- **SetFitABSA Polarity Model:** [setfit-absa-polarity](https://huggingface.co/setfit-absa-polarity) |
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- **Maximum Sequence Length:** 384 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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### 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>'price:The price is reasonable although the service is poor.'</li><li>'service:The price is reasonable although the service is poor.'</li><li>'service:The place is so cool and the service is prompt and curtious.'</li></ul> | |
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| no aspect | <ul><li>'stomach:The food was delicious but do not come here on a empty stomach.'</li><li>'place:I grew up eating Dosa and have yet to find a place in NY to satisfy my taste buds.'</li><li>'NY:I grew up eating Dosa and have yet to find a place in NY to satisfy my taste buds.'</li></ul> | |
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## Evaluation |
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### Metrics |
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| Label | F1 | |
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|:--------|:-------| |
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| **all** | 0.9231 | |
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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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"MattiaTintori/Final_aspect_Colab", |
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"setfit-absa-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 | 3 | 19.4137 | 62 | |
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| Label | Training Sample Count | |
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|:----------|:----------------------| |
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| no aspect | 430 | |
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| aspect | 711 | |
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### Training Hyperparameters |
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- batch_size: (64, 4) |
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- num_epochs: (5, 32) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- num_iterations: 10 |
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- body_learning_rate: (8e-05, 8e-05) |
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- head_learning_rate: 0.04 |
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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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- l2_weight: 0.01 |
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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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| 0.0028 | 1 | 0.2878 | - | |
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| 0.0560 | 20 | 0.2409 | 0.2515 | |
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| 0.1120 | 40 | 0.2291 | 0.2319 | |
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| 0.1681 | 60 | 0.1354 | 0.1835 | |
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| **0.2241** | **80** | **0.0654** | **0.1389** | |
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| 0.2801 | 100 | 0.0334 | 0.1818 | |
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| 0.3361 | 120 | 0.0535 | 0.1408 | |
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| 0.3922 | 140 | 0.014 | 0.1564 | |
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| 0.4482 | 160 | 0.0119 | 0.1453 | |
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| 0.5042 | 180 | 0.0158 | 0.1511 | |
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| 0.5602 | 200 | 0.0157 | 0.1393 | |
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| 0.6162 | 220 | 0.005 | 0.1536 | |
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| 0.6723 | 240 | 0.0002 | 0.1546 | |
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| 0.7283 | 260 | 0.0002 | 0.1673 | |
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| 0.7843 | 280 | 0.0004 | 0.1655 | |
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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: 3.7.6 |
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- Transformers: 4.39.0 |
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- PyTorch: 2.3.1+cu121 |
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- Datasets: 2.21.0 |
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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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