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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: Someone comes out of the shack and shoves one of the kids to the ground. He |
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- text: He approaches the object and reads a plaque on its side. Someone |
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- text: Later at someone's family farm, someone sees the lights on in the hangar. |
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Someone |
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- text: Someone stands looking over some of the old photographs as someone goes through |
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the mess on the desk. Someone |
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- text: Snow blows around a city of towering crystalline structures. A warrior |
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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: 0.11475409836065574 |
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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 [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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 [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance |
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- **Maximum Sequence Length:** 512 tokens |
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- **Number of Classes:** 9 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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| 6 | <ul><li>'The girl does 2 perfect flips. The girls'</li><li>'Emerging in open water, he does a breaststroke toward the murky. He'</li><li>'A young child is moving back and fourth on a swing while laughing and smiling to the camera. The child'</li></ul> | |
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| 4 | <ul><li>'He turns away and covers his face with one hand. Someone'</li><li>'With a nod, the man hands it over to the defeated boy. Someone'</li><li>"On the shop floor, his little helper helps himself to an expensive handbag from a display cabinet, then some women's designer shoes, all of which are detailed on a list. He"</li></ul> | |
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| 1 | <ul><li>'A lot of people are sitting on terraces in a big field and people is walking in the entrance of a big stadium. men'</li><li>'The water gets rough as the past through some rocks. Several people'</li><li>'We see a man dunk the ball twice. We'</li></ul> | |
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| 5 | <ul><li>'A man wearing a safari hat leads a group of camels with riders in a single file. The camera'</li><li>'Someone stirs the cookie dough in a bowl. The dough'</li><li>'A logo for a sports even is shown. There'</li></ul> | |
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| 8 | <ul><li>'A girl is shown several times running on a track. She'</li><li>'Someone peers out from the cabin. As she emerges, someone'</li><li>'Someone and someone swap looks. She'</li></ul> | |
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| 2 | <ul><li>'In slow motion, both the Russians and Americans celebrate. Someone'</li><li>'Through a window, we watch someone raise his teacup to his companions. At home, someone'</li><li>'People stand by the wall, laughing. He'</li></ul> | |
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| 7 | <ul><li>'Someone turns at the sound of the distant horns. 6000 horsemen, lead by people,'</li><li>"The man plays the video in reverse to look as if he's putting shaving cream on with the razor. The men then"</li><li>'He eyes someone with a furrowed brow, then springs up and hurries after her. Someone and someone'</li></ul> | |
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| 0 | <ul><li>'She opens a small metal box on a desk and pushes a button inside. Someone'</li><li>'Someone starts to say something then thinks better of it, and remains silent. Someone'</li><li>"Someone changes into a Spanish policeman's outfit and heads down an outside staircase with the packed up rifle. As someone leaves, someone"</li></ul> | |
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| 3 | <ul><li>'He is shown playing a game with a virtual sumo wrestler. The shorter man'</li><li>'The girls flips, then runs, flips and dismounts. The cloud'</li><li>'The official extends a red flag. As Master someone'</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** | 0.1148 | |
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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("HelgeKn/Swag-multi-class-4") |
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# Run inference |
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preds = model("He approaches the object and reads a plaque on its side. Someone") |
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``` |
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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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--> |
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<!-- |
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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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--> |
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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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--> |
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<!-- |
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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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--> |
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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 | 13.25 | 31 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 4 | |
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| 1 | 4 | |
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| 2 | 4 | |
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| 3 | 4 | |
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| 4 | 4 | |
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| 5 | 4 | |
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| 6 | 4 | |
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| 7 | 4 | |
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| 8 | 4 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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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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- 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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| 0.0111 | 1 | 0.2285 | - | |
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| 0.5556 | 50 | 0.0567 | - | |
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| 1.1111 | 100 | 0.0083 | - | |
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| 1.6667 | 150 | 0.0084 | - | |
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### Framework Versions |
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- Python: 3.9.13 |
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- SetFit: 1.0.1 |
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- Sentence Transformers: 2.2.2 |
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- Transformers: 4.36.0 |
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- PyTorch: 2.1.1+cpu |
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- Datasets: 2.15.0 |
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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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