| --- |
| library_name: setfit |
| tags: |
| - setfit |
| - sentence-transformers |
| - text-classification |
| - generated_from_setfit_trainer |
| datasets: |
| - HelgeKn/SATHAME-generator-train |
| metrics: |
| - accuracy |
| widget: |
| - text: '`` So crunch , crunch , crunch , bang , bang , bang -- here come the ringers |
| from above , making a very obvious exit while the congregation is at prayer , |
| `` he says . ' |
| - text: 'The others here today live elsewhere . ' |
| - text: 'Then , at a signal , the ringers begin varying the order in which the bells |
| sound without altering the steady rhythm of the striking . ' |
| - text: 'Mr. Hammond worries that old age and the flightiness of youth will diminish |
| the ranks of the East Anglian group that keeps the Aslacton bells pealing . ' |
| pipeline_tag: text-classification |
| inference: true |
| base_model: sentence-transformers/paraphrase-mpnet-base-v2 |
| --- |
| |
| # SetFit with sentence-transformers/paraphrase-mpnet-base-v2 |
|
|
| This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [HelgeKn/SATHAME-generator-train](https://huggingface.co/datasets/HelgeKn/SATHAME-generator-train) dataset 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 [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance is used for classification. |
|
|
| The model has been trained using an efficient few-shot learning technique that involves: |
|
|
| 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
| 2. Training a classification head with features from the fine-tuned Sentence Transformer. |
|
|
| ## Model Details |
|
|
| ### Model Description |
| - **Model Type:** SetFit |
| - **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) |
| - **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance |
| - **Maximum Sequence Length:** 512 tokens |
| - **Number of Classes:** 4 classes |
| - **Training Dataset:** [HelgeKn/SATHAME-generator-train](https://huggingface.co/datasets/HelgeKn/SATHAME-generator-train) |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### Model Sources |
|
|
| - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) |
| - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) |
| - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) |
|
|
| ### Model Labels |
| | Label | Examples | |
| |:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | 3 | <ul><li>'The art of change-ringing is peculiar to the English , and , like most English peculiarities , unintelligible to the rest of the world . '</li><li>'Of all scenes that evoke rural England , this is one of the loveliest : An ancient stone church stands amid the fields , the sound of bells cascading from its tower , calling the faithful to evensong . '</li><li>'In the tower , five men and women pull rhythmically on ropes attached to the same five bells that first sounded here in 1614 . '</li></ul> | |
| | 1 | <ul><li>'The parishioners of St. Michael and All Angels stop to chat at the church door , as members here always have . '</li><li>'History , after all , is not on his side . '</li><li>"According to a nationwide survey taken a year ago , nearly a third of England 's church bells are no longer rung on Sundays because there is no one to ring them . "</li></ul> | |
| | 2 | <ul><li>'Now , only one local ringer remains : 64-year-old Derek Hammond . '</li><li>'The others here today live elsewhere . '</li><li>'No one speaks , and the snaking of the ropes seems to make as much sound as the bells themselves , muffled by the ceiling . '</li></ul> | |
| | 0 | <ul><li>'`` To ring for even one service at this tower , we have to scrape , `` says Mr. Hammond , a retired water-authority worker . `` '</li><li>'When their changes are completed , and after they have worked up a sweat , ringers often skip off to the local pub , leaving worship for others below . '</li><li>"Two years ago , the Rev. Jeremy Hummerstone , vicar of Great Torrington , Devon , got so fed up with ringers who did n't attend service he sacked the entire band ; the ringers promptly set up a picket line in protest . "</li></ul> | |
|
|
| ## Uses |
|
|
| ### Direct Use for Inference |
|
|
| First install the SetFit library: |
|
|
| ```bash |
| pip install setfit |
| ``` |
|
|
| Then you can load this model and run inference. |
|
|
| ```python |
| from setfit import SetFitModel |
| |
| # Download from the 🤗 Hub |
| model = SetFitModel.from_pretrained("HelgeKn/Testing-blub") |
| # Run inference |
| preds = model("The others here today live elsewhere . ") |
| ``` |
|
|
| <!-- |
| ### Downstream Use |
|
|
| *List how someone could finetune this model on their own dataset.* |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| <!-- |
| ## Bias, Risks and Limitations |
|
|
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
|
|
| <!-- |
| ### Recommendations |
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|
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
|
|
| ## Training Details |
|
|
| ### Training Set Metrics |
| | Training set | Min | Median | Max | |
| |:-------------|:----|:-------|:----| |
| | Word count | 8 | 27.275 | 45 | |
|
|
| | Label | Training Sample Count | |
| |:------|:----------------------| |
| | 0 | 10 | |
| | 1 | 10 | |
| | 2 | 10 | |
| | 3 | 10 | |
|
|
| ### Training Hyperparameters |
| - batch_size: (16, 16) |
| - num_epochs: (2, 2) |
| - max_steps: -1 |
| - sampling_strategy: oversampling |
| - num_iterations: 20 |
| - body_learning_rate: (2e-05, 2e-05) |
| - head_learning_rate: 2e-05 |
| - loss: CosineSimilarityLoss |
| - distance_metric: cosine_distance |
| - margin: 0.25 |
| - end_to_end: False |
| - use_amp: False |
| - warmup_proportion: 0.1 |
| - seed: 42 |
| - eval_max_steps: -1 |
| - load_best_model_at_end: False |
| |
| ### Training Results |
| | Epoch | Step | Training Loss | Validation Loss | |
| |:-----:|:----:|:-------------:|:---------------:| |
| | 0.01 | 1 | 0.2799 | - | |
| | 0.5 | 50 | 0.1155 | - | |
| | 1.0 | 100 | 0.0023 | - | |
| | 1.5 | 150 | 0.0008 | - | |
| | 2.0 | 200 | 0.0017 | - | |
| |
| ### Framework Versions |
| - Python: 3.9.13 |
| - SetFit: 1.0.1 |
| - Sentence Transformers: 2.2.2 |
| - Transformers: 4.36.0 |
| - PyTorch: 2.1.1+cpu |
| - Datasets: 2.15.0 |
| - Tokenizers: 0.15.0 |
| |
| ## Citation |
| |
| ### BibTeX |
| ```bibtex |
| @article{https://doi.org/10.48550/arxiv.2209.11055, |
| doi = {10.48550/ARXIV.2209.11055}, |
| url = {https://arxiv.org/abs/2209.11055}, |
| author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
| keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
| title = {Efficient Few-Shot Learning Without Prompts}, |
| publisher = {arXiv}, |
| year = {2022}, |
| copyright = {Creative Commons Attribution 4.0 International} |
| } |
| ``` |
| |
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| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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| ## Model Card Contact |
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| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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