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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: News footage from that day shows groups of young men marching through the
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capital, chanting “kill, kill, kill a poof”.
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- text: They are California, Florida, Illinois, Nebraska, New York, and Wyoming.
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- text: Or, are they actively trying to make sure they have a scapegoat for a drug-resistant
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form of the monkeypox?
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- text: Either way, she said, a public gathering of some kind would go ahead on Saturday.
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- text: White House officials have touted their efforts to cut down on the paperwork
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in order to get the drug through this so-called “compassionate use” channel.
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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: 0.9265060240963855
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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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| objective | <ul><li>'"I have never seen it this bad," said Dan Domenech, executive director of the School Superintendents Association.'</li><li>'There will be an enormous increase of public revenue, as there was after the war from the carry-over of the wartime taxes.'</li><li>'No cases have been spotted so far of a strain that can evade tecovirimat, though the ruling class is warning of a “low barrier to resistance” which poses a risk that a resistant variant could emerge and spread.'</li></ul> |
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| subjective | <ul><li>'But what of American individualism?'</li><li>'It’s a kind of brainwashing.'</li><li>'In theory, the problematic behavior parts of the New Mexico ruling could still prevent an illegal alien from being given authorization to practice law, but don’t count on it.'</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** | 0.9265 |
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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("They are California, Florida, Illinois, Nebraska, New York, and Wyoming.")
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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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### Out-of-Scope Use
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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 | 22.7637 | 97 |
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| Label | Training Sample Count |
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|:-----------|:----------------------|
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| objective | 256 |
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| subjective | 256 |
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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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| 0.0002 | 1 | 0.2779 | - |
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| 0.0122 | 50 | 0.2605 | - |
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| 0.0243 | 100 | 0.2721 | - |
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| 0.0365 | 150 | 0.2404 | - |
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| 0.0486 | 200 | 0.2468 | - |
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| 0.0608 | 250 | 0.1941 | - |
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| 0.0730 | 300 | 0.0574 | - |
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| 0.0851 | 350 | 0.0124 | - |
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| 0.0973 | 400 | 0.0019 | - |
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| 0.1094 | 450 | 0.0017 | - |
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| 0.1216 | 500 | 0.0028 | - |
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| 0.1338 | 550 | 0.0011 | - |
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| 0.1459 | 600 | 0.0011 | - |
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| 0.1581 | 650 | 0.0011 | - |
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| 0.1702 | 700 | 0.0316 | - |
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| 0.1824 | 750 | 0.0007 | - |
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| 0.1946 | 800 | 0.001 | - |
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| 0.2067 | 850 | 0.0009 | - |
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| 0.2189 | 900 | 0.0008 | - |
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| 0.2310 | 950 | 0.0007 | - |
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| 0.2432 | 1000 | 0.0006 | - |
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| 0.2554 | 1050 | 0.0006 | - |
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| 0.2675 | 1100 | 0.0005 | - |
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| 0.2797 | 1150 | 0.0005 | - |
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| 0.2918 | 1200 | 0.0006 | - |
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| 0.3040 | 1250 | 0.0006 | - |
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| 0.3161 | 1300 | 0.0005 | - |
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| 0.3283 | 1350 | 0.0005 | - |
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| 0.3405 | 1400 | 0.001 | - |
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| 0.3526 | 1450 | 0.0004 | - |
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| 0.3648 | 1500 | 0.0005 | - |
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| 0.3769 | 1550 | 0.0005 | - |
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| 0.3891 | 1600 | 0.0004 | - |
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| 0.4013 | 1650 | 0.0005 | - |
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| 0.4134 | 1700 | 0.0004 | - |
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| 0.4256 | 1750 | 0.0004 | - |
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| 0.4377 | 1800 | 0.0004 | - |
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| 0.4499 | 1850 | 0.0004 | - |
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| 0.4621 | 1900 | 0.0003 | - |
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| 0.4742 | 1950 | 0.0004 | - |
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| 0.4864 | 2000 | 0.0004 | - |
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| 0.4985 | 2050 | 0.0003 | - |
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| 0.5107 | 2100 | 0.0003 | - |
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| 0.5229 | 2150 | 0.0004 | - |
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| 0.5350 | 2200 | 0.0004 | - |
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| 0.5472 | 2250 | 0.0003 | - |
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| 0.5593 | 2300 | 0.0003 | - |
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| 0.5715 | 2350 | 0.0004 | - |
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| 0.5837 | 2400 | 0.0004 | - |
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| 0.5958 | 2450 | 0.0004 | - |
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| 0.6080 | 2500 | 0.0003 | - |
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| 0.6201 | 2550 | 0.0003 | - |
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| 0.6323 | 2600 | 0.0003 | - |
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| 0.6445 | 2650 | 0.0003 | - |
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| 0.6566 | 2700 | 0.0003 | - |
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| 0.6688 | 2750 | 0.0003 | - |
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| 0.6809 | 2800 | 0.0003 | - |
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| 0.6931 | 2850 | 0.0002 | - |
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| 0.7053 | 2900 | 0.0003 | - |
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| 0.7174 | 2950 | 0.0003 | - |
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| 0.7296 | 3000 | 0.0003 | - |
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| 0.7417 | 3050 | 0.0002 | - |
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| 0.7539 | 3100 | 0.0003 | - |
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| 0.7661 | 3150 | 0.0003 | - |
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| 0.7782 | 3200 | 0.0003 | - |
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| 0.7904 | 3250 | 0.0003 | - |
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| 0.8025 | 3300 | 0.0003 | - |
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| 0.8147 | 3350 | 0.0003 | - |
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| 0.8268 | 3400 | 0.0003 | - |
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| 0.8390 | 3450 | 0.0003 | - |
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| 0.8512 | 3500 | 0.0003 | - |
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| 0.8633 | 3550 | 0.0003 | - |
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| 0.8755 | 3600 | 0.0003 | - |
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| 0.8876 | 3650 | 0.0002 | - |
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| 0.8998 | 3700 | 0.0003 | - |
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| 0.9120 | 3750 | 0.0003 | - |
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| 0.9241 | 3800 | 0.0002 | - |
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| 0.9363 | 3850 | 0.0003 | - |
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| 0.9484 | 3900 | 0.0003 | - |
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| 0.9606 | 3950 | 0.0003 | - |
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| 0.9728 | 4000 | 0.0003 | - |
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| 0.9849 | 4050 | 0.0002 | - |
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| 0.9971 | 4100 | 0.0003 | - |
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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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