metadata
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: >-
Isolated 500kHz 1mΩ current sensor ICs. Allegro MicroSystems is exploiting
tunnel magnetoresistance to measure current with 500kHz bandwidth, and
less noise that Hall effect isolated current
- text: >-
Cruz Azul vs Club Leon Prediction and Betting Tips. Mexican Liga MX
returns with a fresh set of fixtures as Cruz Azul and Club Leon square off
at the Estadio Olímpico Universitario on Saturday.
- text: >-
Bavarian PM calls to stop refugee payments for Ukrainians Ambassador
responds. Read more
- text: >-
HDFC Bank Bonus Issue One Share For Every One Held Board Approves Plan.
The record date for determining the eligible shareholders to receive HDFC
Bank bonus equity shares is Wednesday, Aug. 27, 2025.
- text: >-
Finding Truth In Other Religions A Call For Openness. Christians do not
have to believe other religions are absolutely false, indeed, they can
believe they were inspired and lead to Christ.
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: intfloat/multilingual-e5-base
model-index:
- name: SetFit with intfloat/multilingual-e5-base
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.8222222222222222
name: Accuracy
SetFit with intfloat/multilingual-e5-base
This is a SetFit model that can be used for Text Classification. This SetFit model uses intfloat/multilingual-e5-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: intfloat/multilingual-e5-base
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 12 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 |
|---|---|
| Business |
|
| Politics |
|
| Entertainment |
|
| Crime |
|
| Science |
|
| Lifestyle |
|
| Education |
|
| Sports |
|
| Health |
|
| General News |
|
| Technology |
|
| Religion |
|
Evaluation
Metrics
| Label | Accuracy |
|---|---|
| all | 0.8222 |
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("Bavarian PM calls to stop refugee payments for Ukrainians Ambassador responds. Read more")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 2 | 42.6906 | 454 |
| Label | Training Sample Count |
|---|---|
| Business | 346 |
| Sports | 244 |
| Politics | 210 |
| Lifestyle | 186 |
| General News | 186 |
| Entertainment | 150 |
| Crime | 98 |
| Technology | 71 |
| Health | 70 |
| Science | 30 |
| Religion | 13 |
| Education | 12 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (5, 5)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 10
- 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: False
Training Results
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.0005 | 1 | 0.1861 | - |
| 0.0248 | 50 | 0.3204 | - |
| 0.0495 | 100 | 0.2765 | - |
| 0.0743 | 150 | 0.2462 | - |
| 0.0990 | 200 | 0.2192 | - |
| 0.1238 | 250 | 0.1755 | - |
| 0.1485 | 300 | 0.135 | - |
| 0.1733 | 350 | 0.1135 | - |
| 0.1980 | 400 | 0.092 | - |
| 0.2228 | 450 | 0.0885 | - |
| 0.2475 | 500 | 0.0739 | - |
| 0.2723 | 550 | 0.0762 | - |
| 0.2970 | 600 | 0.0688 | - |
| 0.3218 | 650 | 0.0633 | - |
| 0.3465 | 700 | 0.0535 | - |
| 0.3713 | 750 | 0.0363 | - |
| 0.3960 | 800 | 0.0388 | - |
| 0.4208 | 850 | 0.0339 | - |
| 0.4455 | 900 | 0.0265 | - |
| 0.4703 | 950 | 0.0344 | - |
| 0.4950 | 1000 | 0.016 | - |
| 0.5198 | 1050 | 0.0231 | - |
| 0.5446 | 1100 | 0.0152 | - |
| 0.5693 | 1150 | 0.0118 | - |
| 0.5941 | 1200 | 0.0102 | - |
| 0.6188 | 1250 | 0.0089 | - |
| 0.6436 | 1300 | 0.0125 | - |
| 0.6683 | 1350 | 0.0082 | - |
| 0.6931 | 1400 | 0.004 | - |
| 0.7178 | 1450 | 0.004 | - |
| 0.7426 | 1500 | 0.0062 | - |
| 0.7673 | 1550 | 0.004 | - |
| 0.7921 | 1600 | 0.0039 | - |
| 0.8168 | 1650 | 0.0111 | - |
| 0.8416 | 1700 | 0.0024 | - |
| 0.8663 | 1750 | 0.0047 | - |
| 0.8911 | 1800 | 0.0013 | - |
| 0.9158 | 1850 | 0.0023 | - |
| 0.9406 | 1900 | 0.0039 | - |
| 0.9653 | 1950 | 0.0036 | - |
| 0.9901 | 2000 | 0.004 | - |
| 1.0149 | 2050 | 0.0007 | - |
| 1.0396 | 2100 | 0.001 | - |
| 1.0644 | 2150 | 0.0029 | - |
| 1.0891 | 2200 | 0.0005 | - |
| 1.1139 | 2250 | 0.0005 | - |
| 1.1386 | 2300 | 0.0006 | - |
| 1.1634 | 2350 | 0.0003 | - |
| 1.1881 | 2400 | 0.0002 | - |
| 1.2129 | 2450 | 0.0018 | - |
| 1.2376 | 2500 | 0.0013 | - |
| 1.2624 | 2550 | 0.0039 | - |
| 1.2871 | 2600 | 0.0025 | - |
| 1.3119 | 2650 | 0.0025 | - |
| 1.3366 | 2700 | 0.0013 | - |
| 1.3614 | 2750 | 0.0017 | - |
| 1.3861 | 2800 | 0.0005 | - |
| 1.4109 | 2850 | 0.0012 | - |
| 1.4356 | 2900 | 0.0002 | - |
| 1.4604 | 2950 | 0.0006 | - |
| 1.4851 | 3000 | 0.0017 | - |
| 1.5099 | 3050 | 0.0004 | - |
| 1.5347 | 3100 | 0.0002 | - |
| 1.5594 | 3150 | 0.0015 | - |
| 1.5842 | 3200 | 0.0002 | - |
| 1.6089 | 3250 | 0.0002 | - |
| 1.6337 | 3300 | 0.0023 | - |
| 1.6584 | 3350 | 0.0025 | - |
| 1.6832 | 3400 | 0.0002 | - |
| 1.7079 | 3450 | 0.0006 | - |
| 1.7327 | 3500 | 0.0006 | - |
| 1.7574 | 3550 | 0.0014 | - |
| 1.7822 | 3600 | 0.0003 | - |
| 1.8069 | 3650 | 0.0024 | - |
| 1.8317 | 3700 | 0.0003 | - |
| 1.8564 | 3750 | 0.001 | - |
| 1.8812 | 3800 | 0.0005 | - |
| 1.9059 | 3850 | 0.0014 | - |
| 1.9307 | 3900 | 0.0007 | - |
| 1.9554 | 3950 | 0.0016 | - |
| 1.9802 | 4000 | 0.0013 | - |
| 2.0050 | 4050 | 0.0007 | - |
| 2.0297 | 4100 | 0.001 | - |
| 2.0545 | 4150 | 0.0005 | - |
| 2.0792 | 4200 | 0.0002 | - |
| 2.1040 | 4250 | 0.0001 | - |
| 2.1287 | 4300 | 0.0003 | - |
| 2.1535 | 4350 | 0.0001 | - |
| 2.1782 | 4400 | 0.0009 | - |
| 2.2030 | 4450 | 0.0002 | - |
| 2.2277 | 4500 | 0.0004 | - |
| 2.2525 | 4550 | 0.0003 | - |
| 2.2772 | 4600 | 0.0001 | - |
| 2.3020 | 4650 | 0.0001 | - |
| 2.3267 | 4700 | 0.0011 | - |
| 2.3515 | 4750 | 0.0016 | - |
| 2.3762 | 4800 | 0.0004 | - |
| 2.4010 | 4850 | 0.0002 | - |
| 2.4257 | 4900 | 0.0001 | - |
| 2.4505 | 4950 | 0.0004 | - |
| 2.4752 | 5000 | 0.0001 | - |
| 2.5 | 5050 | 0.0002 | - |
| 2.5248 | 5100 | 0.0017 | - |
| 2.5495 | 5150 | 0.0002 | - |
| 2.5743 | 5200 | 0.0001 | - |
| 2.5990 | 5250 | 0.0013 | - |
| 2.6238 | 5300 | 0.0014 | - |
| 2.6485 | 5350 | 0.0001 | - |
| 2.6733 | 5400 | 0.0001 | - |
| 2.6980 | 5450 | 0.0001 | - |
| 2.7228 | 5500 | 0.0001 | - |
| 2.7475 | 5550 | 0.0001 | - |
| 2.7723 | 5600 | 0.0001 | - |
| 2.7970 | 5650 | 0.0001 | - |
| 2.8218 | 5700 | 0.0 | - |
| 2.8465 | 5750 | 0.0 | - |
| 2.8713 | 5800 | 0.0012 | - |
| 2.8960 | 5850 | 0.0001 | - |
| 2.9208 | 5900 | 0.0001 | - |
| 2.9455 | 5950 | 0.0003 | - |
| 2.9703 | 6000 | 0.0001 | - |
| 2.9950 | 6050 | 0.0001 | - |
| 3.0198 | 6100 | 0.0 | - |
| 3.0446 | 6150 | 0.0 | - |
| 3.0693 | 6200 | 0.0 | - |
| 3.0941 | 6250 | 0.0 | - |
| 3.1188 | 6300 | 0.0 | - |
| 3.1436 | 6350 | 0.0 | - |
| 3.1683 | 6400 | 0.0 | - |
| 3.1931 | 6450 | 0.0001 | - |
| 3.2178 | 6500 | 0.0001 | - |
| 3.2426 | 6550 | 0.0001 | - |
| 3.2673 | 6600 | 0.0 | - |
| 3.2921 | 6650 | 0.0 | - |
| 3.3168 | 6700 | 0.0 | - |
| 3.3416 | 6750 | 0.0 | - |
| 3.3663 | 6800 | 0.0 | - |
| 3.3911 | 6850 | 0.0012 | - |
| 3.4158 | 6900 | 0.0013 | - |
| 3.4406 | 6950 | 0.0001 | - |
| 3.4653 | 7000 | 0.001 | - |
| 3.4901 | 7050 | 0.0001 | - |
| 3.5149 | 7100 | 0.0002 | - |
| 3.5396 | 7150 | 0.0002 | - |
| 3.5644 | 7200 | 0.0001 | - |
| 3.5891 | 7250 | 0.0001 | - |
| 3.6139 | 7300 | 0.0002 | - |
| 3.6386 | 7350 | 0.0001 | - |
| 3.6634 | 7400 | 0.0001 | - |
| 3.6881 | 7450 | 0.0013 | - |
| 3.7129 | 7500 | 0.0001 | - |
| 3.7376 | 7550 | 0.0 | - |
| 3.7624 | 7600 | 0.0 | - |
| 3.7871 | 7650 | 0.0 | - |
| 3.8119 | 7700 | 0.0 | - |
| 3.8366 | 7750 | 0.0 | - |
| 3.8614 | 7800 | 0.0 | - |
| 3.8861 | 7850 | 0.0 | - |
| 3.9109 | 7900 | 0.0 | - |
| 3.9356 | 7950 | 0.0 | - |
| 3.9604 | 8000 | 0.0 | - |
| 3.9851 | 8050 | 0.0 | - |
| 4.0099 | 8100 | 0.0001 | - |
| 4.0347 | 8150 | 0.0 | - |
| 4.0594 | 8200 | 0.0 | - |
| 4.0842 | 8250 | 0.0 | - |
| 4.1089 | 8300 | 0.0 | - |
| 4.1337 | 8350 | 0.0 | - |
| 4.1584 | 8400 | 0.0 | - |
| 4.1832 | 8450 | 0.0 | - |
| 4.2079 | 8500 | 0.0 | - |
| 4.2327 | 8550 | 0.0 | - |
| 4.2574 | 8600 | 0.0 | - |
| 4.2822 | 8650 | 0.0 | - |
| 4.3069 | 8700 | 0.0 | - |
| 4.3317 | 8750 | 0.0 | - |
| 4.3564 | 8800 | 0.0 | - |
| 4.3812 | 8850 | 0.0 | - |
| 4.4059 | 8900 | 0.0 | - |
| 4.4307 | 8950 | 0.0 | - |
| 4.4554 | 9000 | 0.0 | - |
| 4.4802 | 9050 | 0.0 | - |
| 4.5050 | 9100 | 0.0 | - |
| 4.5297 | 9150 | 0.0 | - |
| 4.5545 | 9200 | 0.0 | - |
| 4.5792 | 9250 | 0.0008 | - |
| 4.6040 | 9300 | 0.0001 | - |
| 4.6287 | 9350 | 0.0 | - |
| 4.6535 | 9400 | 0.0 | - |
| 4.6782 | 9450 | 0.0 | - |
| 4.7030 | 9500 | 0.0 | - |
| 4.7277 | 9550 | 0.0013 | - |
| 4.7525 | 9600 | 0.0001 | - |
| 4.7772 | 9650 | 0.0 | - |
| 4.8020 | 9700 | 0.0001 | - |
| 4.8267 | 9750 | 0.0 | - |
| 4.8515 | 9800 | 0.0 | - |
| 4.8762 | 9850 | 0.0 | - |
| 4.9010 | 9900 | 0.0 | - |
| 4.9257 | 9950 | 0.0001 | - |
| 4.9505 | 10000 | 0.0 | - |
| 4.9752 | 10050 | 0.0 | - |
| 5.0 | 10100 | 0.0001 | - |
Framework Versions
- Python: 3.10.12
- SetFit: 1.1.3
- Sentence Transformers: 5.1.0
- Transformers: 4.55.2
- PyTorch: 2.7.1+cu118
- Datasets: 4.0.0
- Tokenizers: 0.21.4
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}
}