ilyankou/spatial-classifier

SetFit with BAAI/bge-small-en-v1.5

This is a SetFit model that can be used for Text Classification. This SetFit model uses BAAI/bge-small-en-v1.5 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:

1. Fine-tuning a Sentence Transformer 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: BAAI/bge-small-en-v1.5
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: 512 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
1	'how far is palms casino from the airport in las vegas' 'anarkali bazar lahore' 'what county is alma nebraska in?'
0	'what is symptom of bipolar disorder' 'early symptoms of shingles outbreak' 'bnsf total employees'

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("setfit_model_id")
# Run inference
preds = model("weather in erlanger ky")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	2	6.3028	21

Label	Training Sample Count
0	755
1	718

Training Hyperparameters

• batch_size: (64, 64)
• num_epochs: (1, 1)
• max_steps: -1
• sampling_strategy: oversampling
• body_learning_rate: (1e-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: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0001	1	0.2507	-
0.0294	500	0.1803	-
0.0589	1000	0.0135	-
0.0883	1500	0.0021	-
0.1178	2000	0.001	-
0.1472	2500	0.0007	-
0.1766	3000	0.0005	-
0.2061	3500	0.0004	-
0.2355	4000	0.0004	-
0.2649	4500	0.0003	-
0.2944	5000	0.0003	-
0.3238	5500	0.0003	-
0.3533	6000	0.0003	-
0.3827	6500	0.0002	-
0.4121	7000	0.0003	-
0.4416	7500	0.0002	-
0.4710	8000	0.0002	-
0.5004	8500	0.0002	-
0.5299	9000	0.0002	-
0.5593	9500	0.0002	-
0.5888	10000	0.0002	-
0.6182	10500	0.0002	-
0.6476	11000	0.0001	-
0.6771	11500	0.0001	-
0.7065	12000	0.0001	-
0.7359	12500	0.0001	-
0.7654	13000	0.0001	-
0.7948	13500	0.0001	-
0.8243	14000	0.0001	-
0.8537	14500	0.0001	-
0.8831	15000	0.0001	-
0.9126	15500	0.0001	-
0.9420	16000	0.0001	-
0.9714	16500	0.0001	-

Framework Versions

• Python: 3.11.5
• SetFit: 1.1.2
• Sentence Transformers: 4.0.2
• Transformers: 4.55.2
• PyTorch: 2.8.0
• Datasets: 2.15.0
• Tokenizers: 0.21.1

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}
}