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---
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base_model: nomic-ai/modernbert-embed-base
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library_name: setfit
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metrics:
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- accuracy
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pipeline_tag: text-classification
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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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widget:
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- text: 'green might want to hang onto that ski mask , as robbery may be the only
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way to pay for his next project . '
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- text: 'even horror fans will most likely not find what they ''re seeking with trouble
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every day ; the movie lacks both thrills and humor . '
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- text: 'the acting , costumes , music , cinematography and sound are all astounding
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given the production ''s austere locales . '
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- text: 'byler reveals his characters in a way that intrigues and even fascinates
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us , and he never reduces the situation to simple melodrama . '
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- text: 'a sequence of ridiculous shoot - ''em - up scenes . '
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inference: true
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co2_eq_emissions:
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emissions: 3.166930971100679
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source: codecarbon
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training_type: fine-tuning
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on_cloud: false
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cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
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ram_total_size: 31.777088165283203
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hours_used: 0.023
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hardware_used: 1 x NVIDIA GeForce RTX 3090
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model-index:
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- name: SetFit with nomic-ai/modernbert-embed-base
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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: 0.8976683937823834
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name: Accuracy
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---
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# SetFit with nomic-ai/modernbert-embed-base
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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 [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) 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:** [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base)
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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:** 8192 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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| negative | <ul><li>'hollow tribute '</li><li>'accompanied by the sketchiest of captions . '</li><li>"take a complete moron to foul up a screen adaptation of oscar wilde 's classic satire "</li></ul> |
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| positive | <ul><li>'smart and newfangled '</li><li>'wise and powerful '</li><li>'while the importance of being earnest offers opportunities for occasional smiles and chuckles '</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** | 0.8977 |
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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("tomaarsen/modernbert-embed-base-sst2")
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# Run inference
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preds = model("a sequence of ridiculous shoot - 'em - up scenes . ")
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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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### 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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## 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 | 2 | 9.0312 | 29 |
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| Label | Training Sample Count |
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|:---------|:----------------------|
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| negative | 16 |
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| positive | 16 |
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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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- l2_weight: 0.01
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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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| 0.0588 | 1 | 0.2389 | - |
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| 1.0 | 17 | - | 0.2225 |
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| 2.0 | 34 | - | 0.1584 |
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| 2.9412 | 50 | 0.1076 | - |
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| 3.0 | 51 | - | 0.1304 |
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| 4.0 | 68 | - | 0.1293 |
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### Environmental Impact
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Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
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- **Carbon Emitted**: 0.003 kg of CO2
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- **Hours Used**: 0.023 hours
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### Training Hardware
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- **On Cloud**: No
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- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
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- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
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- **RAM Size**: 31.78 GB
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### Framework Versions
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- Python: 3.9.16
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- SetFit: 1.2.0.dev0
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- Sentence Transformers: 3.3.1
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- Transformers: 4.49.0.dev0
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- PyTorch: 2.4.1+cu121
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- Datasets: 2.15.0
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- Tokenizers: 0.21.0
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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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