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--- |
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library_name: setfit |
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tags: |
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- setfit |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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metrics: |
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- accuracy |
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widget: |
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- text: delivery and food preparation was suoer fast. nice |
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- text: Hard, stale cookies that were probably sitting out for days. |
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- text: I ordered an extra side of guacamole that never arrived with my meal. |
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- text: Kulang talaga ang mga sangkap, mukhang hindi kumpleto ang aking order. |
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- text: The steak was so overcooked and tough, I couldn't even cut through it with |
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a knife. |
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pipeline_tag: text-classification |
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inference: true |
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base_model: meedan/paraphrase-filipino-mpnet-base-v2 |
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--- |
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# SetFit with meedan/paraphrase-filipino-mpnet-base-v2 |
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [meedan/paraphrase-filipino-mpnet-base-v2](https://huggingface.co/meedan/paraphrase-filipino-mpnet-base-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. |
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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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## Model Details |
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### Model Description |
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- **Model Type:** SetFit |
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- **Sentence Transformer body:** [meedan/paraphrase-filipino-mpnet-base-v2](https://huggingface.co/meedan/paraphrase-filipino-mpnet-base-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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- **Maximum Sequence Length:** 128 tokens |
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- **Number of Classes:** 6 classes |
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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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| 0 | <ul><li>'I specifically asked for no onions, yet my sandwich was loaded with them when delivered.'</li><li>'The delivery driver spilled half my order all over the bag. What a mess!'</li><li>'Two hour wait only for my pizza to arrive burnt on the bottom from sitting too long.'</li></ul> | |
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| 2 | <ul><li>'Found a long strand of hair hanging out of my sealed takeout burger container.'</li><li>'Bits of plastic were baked into the crust of the takeout pizza I received.'</li><li>'The takeout container for my soup was leaking and left a trail of foul-smelling liquid.'</li></ul> | |
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| 1 | <ul><li>'Sobrang luto at tigas na para bang kahoy ang aking karne.'</li><li>'Sobrang lata ng pagkaluto, hindi na makain ang aking litsong manok.'</li><li>'Pizza crust was burnt black on the bottom yet still doughy raw on top.'</li></ul> | |
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| 3 | <ul><li>'Half the ingredients were missing from my order like they forgot to include them.'</li><li>'Binayaran ko ang dami, pero napakaliit lang ng portion size na naibigay sa akin.'</li><li>'The plate looked full but it was all rice, with small paltry portions of the main items.'</li></ul> | |
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| 4 | <ul><li>'Bland, overcooked chicken, soggy vegetables and hard, stale naan bread.'</li><li>'Tiny portion sizes, freezing cold plates, and a hair baked into the bread.'</li><li>'Every single thing I tried to order was met with confusion, attitude and mistakes.'</li></ul> | |
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| 5 | <ul><li>'From the appetizer to dessert, everything was prepared flawlessly. 10/10!'</li><li>"The chilaquiles were authentic, flavor-packed and easily the best I've had."</li><li>'You can really taste the freshness of the local ingredients in every bite.'</li></ul> | |
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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 SetFitModel |
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# Download from the 🤗 Hub |
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model = SetFitModel.from_pretrained("bsen26/eyeR-classification-model-1.0") |
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# Run inference |
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preds = model("delivery and food preparation was suoer fast. nice") |
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``` |
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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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*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 | 12.6833 | 17 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 20 | |
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| 1 | 20 | |
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| 2 | 20 | |
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| 3 | 20 | |
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| 4 | 20 | |
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| 5 | 20 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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- num_epochs: (1, 1) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- num_iterations: 20 |
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- body_learning_rate: (2e-05, 2e-05) |
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- head_learning_rate: 2e-05 |
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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: False |
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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: False |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:------:|:----:|:-------------:|:---------------:| |
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| 0.0033 | 1 | 0.2048 | - | |
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| 0.1667 | 50 | 0.048 | - | |
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| 0.3333 | 100 | 0.0148 | - | |
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| 0.5 | 150 | 0.0011 | - | |
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| 0.6667 | 200 | 0.0009 | - | |
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| 0.8333 | 250 | 0.0005 | - | |
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| 1.0 | 300 | 0.0008 | - | |
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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: 2.6.1 |
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- Transformers: 4.38.2 |
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- PyTorch: 2.2.1+cu121 |
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- Datasets: 2.18.0 |
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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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