README.md

---

base_model: nomic-ai/modernbert-embed-base
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 'green might want to hang onto that ski mask , as robbery may be the only

    way to pay for his next project . '
- text: 'even horror fans will most likely not find what they ''re seeking with trouble

    every day ; the movie lacks both thrills and humor . '
- text: 'the acting , costumes , music , cinematography and sound are all astounding

    given the production ''s austere locales . '
- text: 'byler reveals his characters in a way that intrigues and even fascinates

    us , and he never reduces the situation to simple melodrama . '
- text: 'a sequence of ridiculous shoot - ''em - up scenes . '
inference: true
co2_eq_emissions:
  emissions: 3.166930971100679
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
  ram_total_size: 31.777088165283203
  hours_used: 0.023
  hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: SetFit with nomic-ai/modernbert-embed-base
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.8976683937823834
      name: Accuracy
---


# SetFit with nomic-ai/modernbert-embed-base

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.

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:** [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 8192 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
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### 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                                                                                                                                                                                    |
|:---------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 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> |
| 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>             |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.8977   |

## 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("tomaarsen/modernbert-embed-base-sst2")

# Run inference

preds = model("a sequence of ridiculous shoot - 'em - up scenes . ")

```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 2   | 9.0312 | 29  |

| Label    | Training Sample Count |
|:---------|:----------------------|
| negative | 16                    |
| positive | 16                    |

### Training Hyperparameters
- batch_size: (32, 32)

- num_epochs: (4, 4)
- max_steps: -1

- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False

- warmup_proportion: 0.1
- l2_weight: 0.01

- seed: 42

- eval_max_steps: -1

- load_best_model_at_end: True



### Training Results

| Epoch  | Step | Training Loss | Validation Loss |

|:------:|:----:|:-------------:|:---------------:|

| 0.0588 | 1    | 0.2389        | -               |

| 1.0    | 17   | -             | 0.2225          |

| 2.0    | 34   | -             | 0.1584          |

| 2.9412 | 50   | 0.1076        | -               |

| 3.0    | 51   | -             | 0.1304          |

| 4.0    | 68   | -             | 0.1293          |



### Environmental Impact

Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).

- **Carbon Emitted**: 0.003 kg of CO2

- **Hours Used**: 0.023 hours



### Training Hardware

- **On Cloud**: No

- **GPU Model**: 1 x NVIDIA GeForce RTX 3090

- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K

- **RAM Size**: 31.78 GB



### Framework Versions

- Python: 3.9.16

- SetFit: 1.2.0.dev0

- Sentence Transformers: 3.3.1

- Transformers: 4.49.0.dev0

- PyTorch: 2.4.1+cu121

- Datasets: 2.15.0

- Tokenizers: 0.21.0



## 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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