metadata

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 model that can be used for Text Classification. This SetFit model uses nomic-ai/modernbert-embed-base as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

Fine-tuning a Sentence Transformer with contrastive learning.
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
Classification head: a LogisticRegression instance
Maximum Sequence Length: 8192 tokens
Number of Classes: 2 classes

Model Sources

Repository: SetFit on GitHub
Paper: Efficient Few-Shot Learning Without Prompts
Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Model Labels

Label	Examples
negative	'hollow tribute ' 'accompanied by the sketchiest of captions . ' "take a complete moron to foul up a screen adaptation of oscar wilde 's classic satire "
positive	'smart and newfangled ' 'wise and powerful ' 'while the importance of being earnest offers opportunities for occasional smiles and chuckles '

Evaluation

Metrics

Label	Accuracy
all	0.8977

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

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 . ")

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.

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

@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}
}