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 |
---|---|
General News |
|
Politics |
|
Sports |
|
Health |
|
Lifestyle |
|
Entertainment |
|
Business |
|
Technology |
|
Religion |
|
Crime |
|
Science |
|
Education |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.8421 |
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 ๐ค Hub
model = SetFitModel.from_pretrained("setfit_model_id")
# Run inference
preds = model("Ari Paparo on Google s Digital Dominance. Our guest is Ari Paparo.")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 2 | 46.3786 | 454 |
Label | Training Sample Count |
---|---|
Business | 302 |
Sports | 302 |
Politics | 302 |
Lifestyle | 302 |
General News | 302 |
Entertainment | 302 |
Crime | 302 |
Technology | 302 |
Health | 302 |
Science | 302 |
Religion | 302 |
Education | 302 |
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 |
---|---|---|---|
0.0002 | 1 | 0.2487 | - |
0.0110 | 50 | 0.3115 | - |
0.0221 | 100 | 0.2961 | - |
0.0331 | 150 | 0.2719 | - |
0.0442 | 200 | 0.2379 | - |
0.0552 | 250 | 0.222 | - |
0.0662 | 300 | 0.2096 | - |
0.0773 | 350 | 0.1889 | - |
0.0883 | 400 | 0.1645 | - |
0.0993 | 450 | 0.1465 | - |
0.1104 | 500 | 0.1197 | - |
0.1214 | 550 | 0.0931 | - |
0.1325 | 600 | 0.0885 | - |
0.1435 | 650 | 0.0695 | - |
0.1545 | 700 | 0.0673 | - |
0.1656 | 750 | 0.0648 | - |
0.1766 | 800 | 0.0538 | - |
0.1876 | 850 | 0.0485 | - |
0.1987 | 900 | 0.041 | - |
0.2097 | 950 | 0.0328 | - |
0.2208 | 1000 | 0.0285 | - |
0.2318 | 1050 | 0.0222 | - |
0.2428 | 1100 | 0.0192 | - |
0.2539 | 1150 | 0.0179 | - |
0.2649 | 1200 | 0.0144 | - |
0.2759 | 1250 | 0.0174 | - |
0.2870 | 1300 | 0.0119 | - |
0.2980 | 1350 | 0.0187 | - |
0.3091 | 1400 | 0.0156 | - |
0.3201 | 1450 | 0.0068 | - |
0.3311 | 1500 | 0.0068 | - |
0.3422 | 1550 | 0.0067 | - |
0.3532 | 1600 | 0.0061 | - |
0.3642 | 1650 | 0.0073 | - |
0.3753 | 1700 | 0.0047 | - |
0.3863 | 1750 | 0.0047 | - |
0.3974 | 1800 | 0.0054 | - |
0.4084 | 1850 | 0.0043 | - |
0.4194 | 1900 | 0.0022 | - |
0.4305 | 1950 | 0.0046 | - |
0.4415 | 2000 | 0.0018 | - |
0.4525 | 2050 | 0.0035 | - |
0.4636 | 2100 | 0.0007 | - |
0.4746 | 2150 | 0.003 | - |
0.4857 | 2200 | 0.0009 | - |
0.4967 | 2250 | 0.0042 | - |
0.5077 | 2300 | 0.0023 | - |
0.5188 | 2350 | 0.0005 | - |
0.5298 | 2400 | 0.0031 | - |
0.5408 | 2450 | 0.0016 | - |
0.5519 | 2500 | 0.001 | - |
0.5629 | 2550 | 0.0028 | - |
0.5740 | 2600 | 0.0011 | - |
0.5850 | 2650 | 0.0004 | - |
0.5960 | 2700 | 0.0003 | - |
0.6071 | 2750 | 0.0003 | - |
0.6181 | 2800 | 0.0017 | - |
0.6291 | 2850 | 0.001 | - |
0.6402 | 2900 | 0.0011 | - |
0.6512 | 2950 | 0.0004 | - |
0.6623 | 3000 | 0.0015 | - |
0.6733 | 3050 | 0.0006 | - |
0.6843 | 3100 | 0.0003 | - |
0.6954 | 3150 | 0.0002 | - |
0.7064 | 3200 | 0.0017 | - |
0.7174 | 3250 | 0.0005 | - |
0.7285 | 3300 | 0.0011 | - |
0.7395 | 3350 | 0.0006 | - |
0.7506 | 3400 | 0.0015 | - |
0.7616 | 3450 | 0.0004 | - |
0.7726 | 3500 | 0.0009 | - |
0.7837 | 3550 | 0.0016 | - |
0.7947 | 3600 | 0.0008 | - |
0.8057 | 3650 | 0.0004 | - |
0.8168 | 3700 | 0.0016 | - |
0.8278 | 3750 | 0.0003 | - |
0.8389 | 3800 | 0.0002 | - |
0.8499 | 3850 | 0.0001 | - |
0.8609 | 3900 | 0.0027 | - |
0.8720 | 3950 | 0.0029 | - |
0.8830 | 4000 | 0.0019 | - |
0.8940 | 4050 | 0.0036 | - |
0.9051 | 4100 | 0.0018 | - |
0.9161 | 4150 | 0.0018 | - |
0.9272 | 4200 | 0.0021 | - |
0.9382 | 4250 | 0.0003 | - |
0.9492 | 4300 | 0.0002 | - |
0.9603 | 4350 | 0.0001 | - |
0.9713 | 4400 | 0.0002 | - |
0.9823 | 4450 | 0.0016 | - |
0.9934 | 4500 | 0.0003 | - |
1.0044 | 4550 | 0.0015 | - |
1.0155 | 4600 | 0.0008 | - |
1.0265 | 4650 | 0.0002 | - |
1.0375 | 4700 | 0.0001 | - |
1.0486 | 4750 | 0.0007 | - |
1.0596 | 4800 | 0.0007 | - |
1.0706 | 4850 | 0.0001 | - |
1.0817 | 4900 | 0.0001 | - |
1.0927 | 4950 | 0.0001 | - |
1.1038 | 5000 | 0.0001 | - |
1.1148 | 5050 | 0.0001 | - |
1.1258 | 5100 | 0.0001 | - |
1.1369 | 5150 | 0.0001 | - |
1.1479 | 5200 | 0.0001 | - |
1.1589 | 5250 | 0.0001 | - |
1.1700 | 5300 | 0.0001 | - |
1.1810 | 5350 | 0.0001 | - |
1.1921 | 5400 | 0.0015 | - |
1.2031 | 5450 | 0.0045 | - |
1.2141 | 5500 | 0.0037 | - |
1.2252 | 5550 | 0.005 | - |
1.2362 | 5600 | 0.0006 | - |
1.2472 | 5650 | 0.0001 | - |
1.2583 | 5700 | 0.001 | - |
1.2693 | 5750 | 0.0001 | - |
1.2804 | 5800 | 0.0001 | - |
1.2914 | 5850 | 0.0022 | - |
1.3024 | 5900 | 0.0003 | - |
1.3135 | 5950 | 0.0016 | - |
1.3245 | 6000 | 0.0003 | - |
1.3355 | 6050 | 0.0001 | - |
1.3466 | 6100 | 0.0001 | - |
1.3576 | 6150 | 0.0001 | - |
1.3687 | 6200 | 0.0001 | - |
1.3797 | 6250 | 0.0002 | - |
1.3907 | 6300 | 0.0001 | - |
1.4018 | 6350 | 0.0001 | - |
1.4128 | 6400 | 0.0011 | - |
1.4238 | 6450 | 0.0003 | - |
1.4349 | 6500 | 0.0004 | - |
1.4459 | 6550 | 0.0001 | - |
1.4570 | 6600 | 0.0021 | - |
1.4680 | 6650 | 0.0013 | - |
1.4790 | 6700 | 0.0038 | - |
1.4901 | 6750 | 0.0002 | - |
1.5011 | 6800 | 0.0007 | - |
1.5121 | 6850 | 0.0001 | - |
1.5232 | 6900 | 0.0002 | - |
1.5342 | 6950 | 0.0014 | - |
1.5453 | 7000 | 0.0003 | - |
1.5563 | 7050 | 0.0001 | - |
1.5673 | 7100 | 0.0001 | - |
1.5784 | 7150 | 0.0001 | - |
1.5894 | 7200 | 0.0011 | - |
1.6004 | 7250 | 0.0001 | - |
1.6115 | 7300 | 0.0001 | - |
1.6225 | 7350 | 0.0001 | - |
1.6336 | 7400 | 0.0001 | - |
1.6446 | 7450 | 0.0 | - |
1.6556 | 7500 | 0.0 | - |
1.6667 | 7550 | 0.0 | - |
1.6777 | 7600 | 0.0 | - |
1.6887 | 7650 | 0.0 | - |
1.6998 | 7700 | 0.0 | - |
1.7108 | 7750 | 0.0 | - |
1.7219 | 7800 | 0.0 | - |
1.7329 | 7850 | 0.0 | - |
1.7439 | 7900 | 0.0001 | - |
1.7550 | 7950 | 0.0 | - |
1.7660 | 8000 | 0.0 | - |
1.7770 | 8050 | 0.0 | - |
1.7881 | 8100 | 0.0 | - |
1.7991 | 8150 | 0.0 | - |
1.8102 | 8200 | 0.0 | - |
1.8212 | 8250 | 0.0 | - |
1.8322 | 8300 | 0.0 | - |
1.8433 | 8350 | 0.0001 | - |
1.8543 | 8400 | 0.0018 | - |
1.8653 | 8450 | 0.0017 | - |
1.8764 | 8500 | 0.0001 | - |
1.8874 | 8550 | 0.0001 | - |
1.8985 | 8600 | 0.0001 | - |
1.9095 | 8650 | 0.0 | - |
1.9205 | 8700 | 0.0 | - |
1.9316 | 8750 | 0.0 | - |
1.9426 | 8800 | 0.0001 | - |
1.9536 | 8850 | 0.0001 | - |
1.9647 | 8900 | 0.0007 | - |
1.9757 | 8950 | 0.0015 | - |
1.9868 | 9000 | 0.0012 | - |
1.9978 | 9050 | 0.0015 | - |
2.0088 | 9100 | 0.0017 | - |
2.0199 | 9150 | 0.0021 | - |
2.0309 | 9200 | 0.0008 | - |
2.0419 | 9250 | 0.0033 | - |
2.0530 | 9300 | 0.0019 | - |
2.0640 | 9350 | 0.0002 | - |
2.0751 | 9400 | 0.0001 | - |
2.0861 | 9450 | 0.0 | - |
2.0971 | 9500 | 0.0 | - |
2.1082 | 9550 | 0.0 | - |
2.1192 | 9600 | 0.0 | - |
2.1302 | 9650 | 0.0 | - |
2.1413 | 9700 | 0.0 | - |
2.1523 | 9750 | 0.0 | - |
2.1634 | 9800 | 0.0001 | - |
2.1744 | 9850 | 0.0 | - |
2.1854 | 9900 | 0.0008 | - |
2.1965 | 9950 | 0.0143 | - |
2.2075 | 10000 | 0.0043 | - |
2.2185 | 10050 | 0.0067 | - |
2.2296 | 10100 | 0.0043 | - |
2.2406 | 10150 | 0.0017 | - |
2.2517 | 10200 | 0.0002 | - |
2.2627 | 10250 | 0.0022 | - |
2.2737 | 10300 | 0.0024 | - |
2.2848 | 10350 | 0.0004 | - |
2.2958 | 10400 | 0.0001 | - |
2.3068 | 10450 | 0.002 | - |
2.3179 | 10500 | 0.0001 | - |
2.3289 | 10550 | 0.001 | - |
2.3400 | 10600 | 0.0002 | - |
2.3510 | 10650 | 0.0002 | - |
2.3620 | 10700 | 0.0001 | - |
2.3731 | 10750 | 0.0 | - |
2.3841 | 10800 | 0.0016 | - |
2.3951 | 10850 | 0.0002 | - |
2.4062 | 10900 | 0.0012 | - |
2.4172 | 10950 | 0.0 | - |
2.4283 | 11000 | 0.0001 | - |
2.4393 | 11050 | 0.0002 | - |
2.4503 | 11100 | 0.0001 | - |
2.4614 | 11150 | 0.0001 | - |
2.4724 | 11200 | 0.0 | - |
2.4834 | 11250 | 0.0 | - |
2.4945 | 11300 | 0.0001 | - |
2.5055 | 11350 | 0.0 | - |
2.5166 | 11400 | 0.0 | - |
2.5276 | 11450 | 0.0 | - |
2.5386 | 11500 | 0.0 | - |
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2.6490 | 12000 | 0.0 | - |
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2.6932 | 12200 | 0.0 | - |
2.7042 | 12250 | 0.0 | - |
2.7152 | 12300 | 0.0 | - |
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2.7373 | 12400 | 0.0 | - |
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2.7594 | 12500 | 0.0 | - |
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2.7815 | 12600 | 0.0 | - |
2.7925 | 12650 | 0.0 | - |
2.8035 | 12700 | 0.0 | - |
2.8146 | 12750 | 0.0 | - |
2.8256 | 12800 | 0.0 | - |
2.8366 | 12850 | 0.0 | - |
2.8477 | 12900 | 0.0 | - |
2.8587 | 12950 | 0.0 | - |
2.8698 | 13000 | 0.0 | - |
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2.8918 | 13100 | 0.0 | - |
2.9029 | 13150 | 0.0 | - |
2.9139 | 13200 | 0.0 | - |
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2.9470 | 13350 | 0.0 | - |
2.9581 | 13400 | 0.0 | - |
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2.9801 | 13500 | 0.0 | - |
2.9912 | 13550 | 0.0 | - |
3.0022 | 13600 | 0.0 | - |
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3.0353 | 13750 | 0.0 | - |
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3.0905 | 14000 | 0.0 | - |
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3.1236 | 14150 | 0.0 | - |
3.1347 | 14200 | 0.0 | - |
3.1457 | 14250 | 0.0 | - |
3.1567 | 14300 | 0.0 | - |
3.1678 | 14350 | 0.0 | - |
3.1788 | 14400 | 0.0 | - |
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3.2009 | 14500 | 0.0 | - |
3.2119 | 14550 | 0.0 | - |
3.2230 | 14600 | 0.0 | - |
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3.3333 | 15100 | 0.0 | - |
3.3444 | 15150 | 0.0 | - |
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3.5099 | 15900 | 0.0 | - |
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3.5541 | 16100 | 0.0 | - |
3.5651 | 16150 | 0.0 | - |
3.5762 | 16200 | 0.0 | - |
3.5872 | 16250 | 0.0 | - |
3.5982 | 16300 | 0.0 | - |
3.6093 | 16350 | 0.0 | - |
3.6203 | 16400 | 0.0 | - |
3.6313 | 16450 | 0.0 | - |
3.6424 | 16500 | 0.0 | - |
3.6534 | 16550 | 0.0 | - |
3.6645 | 16600 | 0.0 | - |
3.6755 | 16650 | 0.0 | - |
3.6865 | 16700 | 0.0 | - |
3.6976 | 16750 | 0.0 | - |
3.7086 | 16800 | 0.0 | - |
3.7196 | 16850 | 0.0 | - |
3.7307 | 16900 | 0.0 | - |
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3.7528 | 17000 | 0.0 | - |
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3.7748 | 17100 | 0.0 | - |
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3.7969 | 17200 | 0.0 | - |
3.8079 | 17250 | 0.0 | - |
3.8190 | 17300 | 0.0 | - |
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3.9073 | 17700 | 0.0 | - |
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4.5475 | 20600 | 0.0 | - |
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4.5916 | 20800 | 0.0 | - |
4.6026 | 20850 | 0.0 | - |
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4.6909 | 21250 | 0.0 | - |
4.7020 | 21300 | 0.0 | - |
4.7130 | 21350 | 0.0 | - |
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4.8234 | 21850 | 0.0 | - |
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4.8455 | 21950 | 0.0 | - |
4.8565 | 22000 | 0.0 | - |
4.8675 | 22050 | 0.0 | - |
4.8786 | 22100 | 0.0 | - |
4.8896 | 22150 | 0.0 | - |
4.9007 | 22200 | 0.0 | - |
4.9117 | 22250 | 0.0 | - |
4.9227 | 22300 | 0.0 | - |
4.9338 | 22350 | 0.0 | - |
4.9448 | 22400 | 0.0 | - |
4.9558 | 22450 | 0.0 | - |
4.9669 | 22500 | 0.0 | - |
4.9779 | 22550 | 0.0 | - |
4.9890 | 22600 | 0.0 | - |
5.0 | 22650 | 0.0 | - |
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}
}
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Model tree for alunadiderot/setfit-e5-base-category-classifier-oversampled-validated
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intfloat/multilingual-e5-base