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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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base_model: avsolatorio/GIST-small-Embedding-v0
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metrics:
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- accuracy
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widget:
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- text: In Florida, some military veterans are now eligible for temporary teaching
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certificates even if they haven't completed a bachelor's degree.
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- text: As the total national income falls, the proportion of it absorbed by government
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will rise.
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- text: And while local far-right activists appear to have quietly accepted defeat
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over Belgrade Pride, a tame and small-scale annual event, the ferocity of their
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opposition to EuroPride reveals that social attitudes are not much different from
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2001.
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- text: 'In return for this extraordinary gift, corporate shareholders owed an implicit
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obligation back to society: namely, that corporations ought to consider not only
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shareholder interests but broader societal interests when making decisions.'
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- text: Nonetheless I believe it falls short for legal and historical reasons that
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I lay out in “Woke, Inc”, my book published last year.
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pipeline_tag: text-classification
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inference: true
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model-index:
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- name: SetFit with avsolatorio/GIST-small-Embedding-v0
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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: 0.844578313253012
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name: Accuracy
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---
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# SetFit with avsolatorio/GIST-small-Embedding-v0
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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 [avsolatorio/GIST-small-Embedding-v0](https://huggingface.co/avsolatorio/GIST-small-Embedding-v0) 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:** [avsolatorio/GIST-small-Embedding-v0](https://huggingface.co/avsolatorio/GIST-small-Embedding-v0)
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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:** 2 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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| subjective | <ul><li>'Stakeholder capitalism poisons democracy and partisan politics poisons capitalism.'</li><li>'There is yet everywhere a deficit in the public revenue because the shrinkage in everything taxable was so sudden and violent.'</li><li>'Our system of unbridled profit-focused capitalism used to serve as perhaps the most important of those sanctuaries, but no longer.'</li></ul> |
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| objective | <ul><li>'But a top buying agent tells me that access to 13 can be gained if you know the right people.'</li><li>'A portion of positive tests around the country is being forwarded to the agency for genetic sequencing, according to a report by CBS News.'</li><li>'asked American Federation of Teachers President Randi Weingarten.'</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** | 0.8446 |
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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("setfit_model_id")
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# Run inference
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preds = model("As the total national income falls, the proportion of it absorbed by government will rise.")
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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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### 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 | 1 | 22.9219 | 77 |
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| Label | Training Sample Count |
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|:-----------|:----------------------|
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| objective | 128 |
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| subjective | 128 |
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### Training Hyperparameters
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- batch_size: (32, 32)
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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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- 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: 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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| 0.0010 | 1 | 0.2715 | - |
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| 0.0484 | 50 | 0.2469 | - |
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| 0.0969 | 100 | 0.2247 | - |
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| 0.1453 | 150 | 0.0501 | - |
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| 0.1938 | 200 | 0.0039 | - |
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| 0.2422 | 250 | 0.0014 | - |
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| 0.2907 | 300 | 0.0011 | - |
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| 0.3391 | 350 | 0.0014 | - |
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| 0.3876 | 400 | 0.001 | - |
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| 0.4360 | 450 | 0.0009 | - |
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| 0.4845 | 500 | 0.0008 | - |
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| 0.5329 | 550 | 0.0008 | - |
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| 0.5814 | 600 | 0.0008 | - |
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| 0.6298 | 650 | 0.0007 | - |
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| 0.6783 | 700 | 0.0007 | - |
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| 0.7267 | 750 | 0.0006 | - |
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| 0.7752 | 800 | 0.0007 | - |
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| 0.8236 | 850 | 0.0006 | - |
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| 0.8721 | 900 | 0.0005 | - |
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| 0.9205 | 950 | 0.0007 | - |
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| 0.9690 | 1000 | 0.0007 | - |
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### Framework Versions
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- Python: 3.11.9
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- SetFit: 1.0.3
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- Sentence Transformers: 3.0.0
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- Transformers: 4.40.2
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- PyTorch: 2.1.2
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- Datasets: 2.19.1
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- Tokenizers: 0.19.1
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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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