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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: Dadon Hotel |
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- text: Joyi Homeo Hall |
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- text: Masum Egg Supplier |
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- text: Salam Automobiles |
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- text: Shoumik Enterprise |
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pipeline_tag: text-classification |
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inference: true |
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base_model: sentence-transformers/paraphrase-mpnet-base-v2 |
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model-index: |
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- name: SetFit with sentence-transformers/paraphrase-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: accuracy |
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value: 0.33 |
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name: Accuracy |
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--- |
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# SetFit with sentence-transformers/paraphrase-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 [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-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:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-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:** 512 tokens |
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- **Number of Classes:** 28 classes |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** 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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| Relegious | <ul><li>'Badc Jame Masjid'</li><li>'Modina Masjid'</li><li>'Baitul Ehsan Jame Masjid'</li></ul> | |
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| Food | <ul><li>'Bombay Biriyani Restaurant'</li><li>'Sanim Ghorowa Reatora'</li><li>'Attel Mati Restaurant'</li></ul> | |
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| Religious PLAce | <ul><li>'Darbar Sharif(Dorbeshe Badsha)'</li><li>'Mazar'</li></ul> | |
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| Education | <ul><li>'The English Academy'</li><li>'Economics Batch'</li><li>'Al Manar Model School'</li></ul> | |
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| Health Care | <ul><li>'Hope Haspital'</li><li>'North Para Community Clinic'</li><li>'Al Sami Medical Hall'</li></ul> | |
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| Office | <ul><li>'Nari Maitri Dholpur Branch'</li><li>'Techsam IT And Computer'</li><li>'Chandpur It'</li></ul> | |
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| Landmark | <ul><li>'Godaun Moar'</li><li>'Kuril Flyover U Turn Bridge'</li><li>'Manik Miya Avenue Moar'</li></ul> | |
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| Fuel | <ul><li>'Mimi Enterprise'</li><li>'Sariful Filling Station'</li><li>'M/s Aruja Enterprise'</li></ul> | |
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| Religious Place | <ul><li>'Kabbir Khan Jame Masjid'</li><li>'Sri Sri Nayanta Babar Mandir'</li><li>'Jordan Church of Christ'</li></ul> | |
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| Transportation | <ul><li>'Lala Khal Ferry Terminal'</li><li>'Porshuram Cng Stand'</li><li>'Riad Cycle Garage'</li></ul> | |
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| Agricultural | <ul><li>'Catlle Farm'</li><li>'Pushon Narsari'</li><li>'Vegetable garden'</li></ul> | |
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| Residential | <ul><li>'Ovinondon Chattrabas'</li><li>'TH Chattrabas'</li><li>'Seven Star Chattrabas'</li></ul> | |
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| shop | <ul><li>'Mayer Doya Store'</li></ul> | |
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| Bank | <ul><li>'Dutch Bangla Bank Limited Maijde (DBBL)'</li><li>'Jamuna Bank Limited Dholaikhal Branch'</li><li>'Prime Bank Limited Elephant Branch'</li></ul> | |
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| Utility | <ul><li>'Shahi Eidgah Water Tank'</li><li>'Pole No 31'</li><li>'Kalmilata Kacha Bazar'</li></ul> | |
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| Healthcare | <ul><li>'Oloukik'</li><li>'Burhanuddin Upazila Health Complex'</li><li>'Dr Nazmin Akter Najma'</li></ul> | |
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| Government | <ul><li>'Zilla Parishad Karjaloy Bhola'</li><li>"Sub Police Commissioner's Bhaban (Tejgaon Branch)"</li><li>'Family Planning Office Satkhira'</li></ul> | |
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| Recreation | <ul><li>'Shaikh Rasel Sriti Shongho'</li><li>'Beraid Camping And Kayaking Zone (BCKZ)'</li><li>'Shohag Palli Picnic Spot & Resort'</li></ul> | |
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| Religious | <ul><li>'Baitul Mamur Jame Masjid'</li><li>'Petrol Pump Jame Masjid'</li><li>'Opsonnin Pharma Ltd Jame Masjid'</li></ul> | |
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| Religious Place | <ul><li>'Jame Masjid'</li><li>'Hospital Masjid'</li><li>'Badar Mokam Jame Masjid'</li></ul> | |
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| Shop | <ul><li>'Nayeem General Store'</li><li>'Bazlu Engineering & Refrigeration'</li><li>'Mukta Dulal'</li></ul> | |
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| Commercial | <ul><li>'Mazar Kacha Bazar'</li><li>'Fall Bazar Kola Potti'</li><li>'Venus Autos'</li></ul> | |
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| Industry | <ul><li>'Rn Integrated Argo'</li><li>'Fresh Dairy Firm'</li><li>'Hemple Rhee Mfg Limited'</li></ul> | |
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| Hotel | <ul><li>'Warisan'</li><li>'Hotel New London Palace Abashik'</li><li>'Sada Vat'</li></ul> | |
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| construction | <ul><li>'Fahim Hardware Store'</li><li>'O A Frame Gallery'</li></ul> | |
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| Construction | <ul><li>'Khalil Steel'</li><li>'Sanaullah Tiles And Sanitary House'</li><li>'Mukta Glass And Thai Aluminum'</li></ul> | |
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| Relegious Place | <ul><li>'Baitul Atiq Jam-E Masjid'</li><li>'Hathazari Bus Stand Baitussalam Jame Masjid'</li><li>'Osman Bin Affan Jame Masjid'</li></ul> | |
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| education | <ul><li>'Masum Electronic'</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** | 0.33 | |
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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("rafi138/setfit-paraphrase-mpnet-base-v2-type") |
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# Run inference |
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preds = model("Dadon Hotel") |
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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 | 1 | 3.5 | 7 | |
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| Label | Training Sample Count | |
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|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------| |
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| ShopCommercialGovernmentHealthcareEducationFoodOfficeReligious PlaceBankTransportationConstructionIndustryResidentialLandmarkRecreationFuelHotelUtilityAgricultural | 0 | |
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### Training Hyperparameters |
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- batch_size: (32, 32) |
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- num_epochs: (4, 4) |
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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: 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: 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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| 0.0006 | 1 | 0.1851 | - | |
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| 0.0282 | 50 | 0.1697 | - | |
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| 0.0564 | 100 | 0.1876 | - | |
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| 0.0032 | 1 | 0.169 | - | |
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| 0.1597 | 50 | 0.081 | - | |
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| 0.3195 | 100 | 0.0641 | - | |
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| 0.4792 | 150 | 0.033 | - | |
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| 0.6390 | 200 | 0.0128 | - | |
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| 0.7987 | 250 | 0.0089 | - | |
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| 0.9585 | 300 | 0.0106 | - | |
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| **1.0** | **313** | **-** | **0.3235** | |
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| 1.1182 | 350 | 0.0215 | - | |
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| 1.2780 | 400 | 0.017 | - | |
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| 1.4377 | 450 | 0.0057 | - | |
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| 1.5974 | 500 | 0.0047 | - | |
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| 1.7572 | 550 | 0.0064 | - | |
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| 1.9169 | 600 | 0.003 | - | |
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| 2.0 | 626 | - | 0.3481 | |
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| 2.0767 | 650 | 0.0043 | - | |
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| 2.2364 | 700 | 0.0022 | - | |
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| 2.3962 | 750 | 0.0014 | - | |
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| 2.5559 | 800 | 0.0028 | - | |
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| 2.7157 | 850 | 0.0018 | - | |
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| 2.8754 | 900 | 0.002 | - | |
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| 3.0 | 939 | - | 0.3393 | |
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| 3.0351 | 950 | 0.0294 | - | |
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| 3.1949 | 1000 | 0.002 | - | |
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| 3.3546 | 1050 | 0.0017 | - | |
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| 3.5144 | 1100 | 0.0017 | - | |
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| 3.6741 | 1150 | 0.0015 | - | |
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| 3.8339 | 1200 | 0.0013 | - | |
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| 3.9936 | 1250 | 0.0014 | - | |
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| 4.0 | 1252 | - | 0.348 | |
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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: 2.2.2 |
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- Transformers: 4.35.2 |
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- PyTorch: 2.1.0+cu121 |
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- Datasets: 2.16.1 |
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- Tokenizers: 0.15.0 |
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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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