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:
- 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: BAAI/bge-small-en-v1.5
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 7 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 |
---|---|
Lookup |
|
Aggregation |
|
Tablejoin |
|
Viewtables |
|
Lookup_1 |
|
Rejection |
|
Generalreply |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.9915 |
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("nazhan/bge-small-en-v1.5-brahmaputra-iter-10-2nd")
# Run inference
preds = model("Can I have data_asset_kpi_bs details.")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 1 | 8.8375 | 62 |
Label | Training Sample Count |
---|---|
Tablejoin | 122 |
Rejection | 69 |
Aggregation | 287 |
Lookup | 59 |
Generalreply | 71 |
Viewtables | 79 |
Lookup_1 | 156 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0000 | 1 | 0.2355 | - |
0.0014 | 50 | 0.2202 | - |
0.0028 | 100 | 0.1664 | - |
0.0042 | 150 | 0.216 | - |
0.0056 | 200 | 0.2341 | - |
0.0070 | 250 | 0.2279 | - |
0.0084 | 300 | 0.1786 | - |
0.0098 | 350 | 0.1603 | - |
0.0112 | 400 | 0.0821 | - |
0.0126 | 450 | 0.1498 | - |
0.0140 | 500 | 0.0942 | - |
0.0155 | 550 | 0.0999 | - |
0.0169 | 600 | 0.0895 | - |
0.0183 | 650 | 0.0841 | - |
0.0197 | 700 | 0.1433 | - |
0.0211 | 750 | 0.0808 | - |
0.0225 | 800 | 0.0346 | - |
0.0239 | 850 | 0.0556 | - |
0.0253 | 900 | 0.0755 | - |
0.0267 | 950 | 0.0346 | - |
0.0281 | 1000 | 0.0486 | - |
0.0295 | 1050 | 0.0207 | - |
0.0309 | 1100 | 0.0126 | - |
0.0323 | 1150 | 0.0113 | - |
0.0337 | 1200 | 0.0076 | - |
0.0351 | 1250 | 0.0082 | - |
0.0365 | 1300 | 0.0142 | - |
0.0379 | 1350 | 0.011 | - |
0.0393 | 1400 | 0.0034 | - |
0.0407 | 1450 | 0.0123 | - |
0.0421 | 1500 | 0.0062 | - |
0.0435 | 1550 | 0.0021 | - |
0.0449 | 1600 | 0.005 | - |
0.0464 | 1650 | 0.0124 | - |
0.0478 | 1700 | 0.0026 | - |
0.0492 | 1750 | 0.0029 | - |
0.0506 | 1800 | 0.0023 | - |
0.0520 | 1850 | 0.0017 | - |
0.0534 | 1900 | 0.0027 | - |
0.0548 | 1950 | 0.0017 | - |
0.0562 | 2000 | 0.0043 | - |
0.0576 | 2050 | 0.0018 | - |
0.0590 | 2100 | 0.0032 | - |
0.0604 | 2150 | 0.0022 | - |
0.0618 | 2200 | 0.0052 | - |
0.0632 | 2250 | 0.0025 | - |
0.0646 | 2300 | 0.0018 | - |
0.0660 | 2350 | 0.0016 | - |
0.0674 | 2400 | 0.0016 | - |
0.0688 | 2450 | 0.001 | - |
0.0702 | 2500 | 0.0015 | - |
0.0716 | 2550 | 0.0013 | - |
0.0730 | 2600 | 0.0012 | - |
0.0744 | 2650 | 0.0012 | - |
0.0759 | 2700 | 0.0017 | - |
0.0773 | 2750 | 0.0016 | - |
0.0787 | 2800 | 0.0018 | - |
0.0801 | 2850 | 0.0007 | - |
0.0815 | 2900 | 0.0008 | - |
0.0829 | 2950 | 0.0016 | - |
0.0843 | 3000 | 0.0008 | - |
0.0857 | 3050 | 0.0011 | - |
0.0871 | 3100 | 0.0013 | - |
0.0885 | 3150 | 0.0012 | - |
0.0899 | 3200 | 0.0006 | - |
0.0913 | 3250 | 0.0012 | - |
0.0927 | 3300 | 0.0009 | - |
0.0941 | 3350 | 0.0007 | - |
0.0955 | 3400 | 0.0006 | - |
0.0969 | 3450 | 0.0011 | - |
0.0983 | 3500 | 0.0012 | - |
0.0997 | 3550 | 0.0008 | - |
0.1011 | 3600 | 0.0009 | - |
0.1025 | 3650 | 0.0007 | - |
0.1039 | 3700 | 0.001 | - |
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0.1110 | 3950 | 0.0006 | - |
0.1124 | 4000 | 0.0004 | - |
0.1138 | 4050 | 0.001 | - |
0.1152 | 4100 | 0.001 | - |
0.1166 | 4150 | 0.0007 | - |
0.1180 | 4200 | 0.0006 | - |
0.1194 | 4250 | 0.0006 | - |
0.1208 | 4300 | 0.0004 | - |
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0.2247 | 8000 | 0.0003 | - |
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0.2472 | 8800 | 0.0004 | - |
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0.2556 | 9100 | 0.0003 | - |
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0.6504 | 23150 | 0.0002 | - |
0.6518 | 23200 | 0.0001 | - |
0.6532 | 23250 | 0.0002 | - |
0.6546 | 23300 | 0.0001 | - |
0.6560 | 23350 | 0.0002 | - |
0.6574 | 23400 | 0.0003 | - |
0.6588 | 23450 | 0.0002 | - |
0.6602 | 23500 | 0.0002 | - |
0.6616 | 23550 | 0.0001 | - |
0.6630 | 23600 | 0.0003 | - |
0.6644 | 23650 | 0.0002 | - |
0.6658 | 23700 | 0.0001 | - |
0.6672 | 23750 | 0.0002 | - |
0.6686 | 23800 | 0.0001 | - |
0.6700 | 23850 | 0.0001 | - |
0.6714 | 23900 | 0.0002 | - |
0.6728 | 23950 | 0.0002 | - |
0.6742 | 24000 | 0.0002 | - |
0.6756 | 24050 | 0.0002 | - |
0.6770 | 24100 | 0.0001 | - |
0.6784 | 24150 | 0.0002 | - |
0.6799 | 24200 | 0.0002 | - |
0.6813 | 24250 | 0.0002 | - |
0.6827 | 24300 | 0.0001 | - |
0.6841 | 24350 | 0.0002 | - |
0.6855 | 24400 | 0.0002 | - |
0.6869 | 24450 | 0.0001 | - |
0.6883 | 24500 | 0.0001 | - |
0.6897 | 24550 | 0.0002 | - |
0.6911 | 24600 | 0.0001 | - |
0.6925 | 24650 | 0.0002 | - |
0.6939 | 24700 | 0.0001 | - |
0.6953 | 24750 | 0.0003 | - |
0.6967 | 24800 | 0.0001 | - |
0.6981 | 24850 | 0.0002 | - |
0.6995 | 24900 | 0.0001 | - |
0.7009 | 24950 | 0.0001 | - |
0.7023 | 25000 | 0.0002 | - |
0.7037 | 25050 | 0.0001 | - |
0.7051 | 25100 | 0.0002 | - |
0.7065 | 25150 | 0.0001 | - |
0.7079 | 25200 | 0.0002 | - |
0.7093 | 25250 | 0.0002 | - |
0.7108 | 25300 | 0.0001 | - |
0.7122 | 25350 | 0.0002 | - |
0.7136 | 25400 | 0.0001 | - |
0.7150 | 25450 | 0.0001 | - |
0.7164 | 25500 | 0.0001 | - |
0.7178 | 25550 | 0.0001 | - |
0.7192 | 25600 | 0.0002 | - |
0.7206 | 25650 | 0.0002 | - |
0.7220 | 25700 | 0.0001 | - |
0.7234 | 25750 | 0.0001 | - |
0.7248 | 25800 | 0.0001 | - |
0.7262 | 25850 | 0.0002 | - |
0.7276 | 25900 | 0.0002 | - |
0.7290 | 25950 | 0.0001 | - |
0.7304 | 26000 | 0.0001 | - |
0.7318 | 26050 | 0.0002 | - |
0.7332 | 26100 | 0.0001 | - |
0.7346 | 26150 | 0.0001 | - |
0.7360 | 26200 | 0.0001 | - |
0.7374 | 26250 | 0.0001 | - |
0.7388 | 26300 | 0.0001 | - |
0.7403 | 26350 | 0.0002 | - |
0.7417 | 26400 | 0.0002 | - |
0.7431 | 26450 | 0.0001 | - |
0.7445 | 26500 | 0.0002 | - |
0.7459 | 26550 | 0.0001 | - |
0.7473 | 26600 | 0.0001 | - |
0.7487 | 26650 | 0.0002 | - |
0.7501 | 26700 | 0.0001 | - |
0.7515 | 26750 | 0.0001 | - |
0.7529 | 26800 | 0.0001 | - |
0.7543 | 26850 | 0.0001 | - |
0.7557 | 26900 | 0.0001 | - |
0.7571 | 26950 | 0.0001 | - |
0.7585 | 27000 | 0.0002 | - |
0.7599 | 27050 | 0.0001 | - |
0.7613 | 27100 | 0.0002 | - |
0.7627 | 27150 | 0.0002 | - |
0.7641 | 27200 | 0.0001 | - |
0.7655 | 27250 | 0.0002 | - |
0.7669 | 27300 | 0.0001 | - |
0.7683 | 27350 | 0.0002 | - |
0.7697 | 27400 | 0.0001 | - |
0.7712 | 27450 | 0.0002 | - |
0.7726 | 27500 | 0.0001 | - |
0.7740 | 27550 | 0.0001 | - |
0.7754 | 27600 | 0.0001 | - |
0.7768 | 27650 | 0.0001 | - |
0.7782 | 27700 | 0.0001 | - |
0.7796 | 27750 | 0.0001 | - |
0.7810 | 27800 | 0.0001 | - |
0.7824 | 27850 | 0.0001 | - |
0.7838 | 27900 | 0.0001 | - |
0.7852 | 27950 | 0.0001 | - |
0.7866 | 28000 | 0.0001 | - |
0.7880 | 28050 | 0.0001 | - |
0.7894 | 28100 | 0.0001 | - |
0.7908 | 28150 | 0.0001 | - |
0.7922 | 28200 | 0.0001 | - |
0.7936 | 28250 | 0.0002 | - |
0.7950 | 28300 | 0.0002 | - |
0.7964 | 28350 | 0.0001 | - |
0.7978 | 28400 | 0.0002 | - |
0.7992 | 28450 | 0.0001 | - |
0.8007 | 28500 | 0.0001 | - |
0.8021 | 28550 | 0.0001 | - |
0.8035 | 28600 | 0.0001 | - |
0.8049 | 28650 | 0.0002 | - |
0.8063 | 28700 | 0.0001 | - |
0.8077 | 28750 | 0.0002 | - |
0.8091 | 28800 | 0.0001 | - |
0.8105 | 28850 | 0.0001 | - |
0.8119 | 28900 | 0.0001 | - |
0.8133 | 28950 | 0.0002 | - |
0.8147 | 29000 | 0.0001 | - |
0.8161 | 29050 | 0.0002 | - |
0.8175 | 29100 | 0.0002 | - |
0.8189 | 29150 | 0.0002 | - |
0.8203 | 29200 | 0.0001 | - |
0.8217 | 29250 | 0.0002 | - |
0.8231 | 29300 | 0.0001 | - |
0.8245 | 29350 | 0.0001 | - |
0.8259 | 29400 | 0.0001 | - |
0.8273 | 29450 | 0.0002 | - |
0.8287 | 29500 | 0.0001 | - |
0.8301 | 29550 | 0.0002 | - |
0.8316 | 29600 | 0.0001 | - |
0.8330 | 29650 | 0.0001 | - |
0.8344 | 29700 | 0.0001 | - |
0.8358 | 29750 | 0.0001 | - |
0.8372 | 29800 | 0.0001 | - |
0.8386 | 29850 | 0.0001 | - |
0.8400 | 29900 | 0.0001 | - |
0.8414 | 29950 | 0.0002 | - |
0.8428 | 30000 | 0.0002 | - |
0.8442 | 30050 | 0.0001 | - |
0.8456 | 30100 | 0.0001 | - |
0.8470 | 30150 | 0.0001 | - |
0.8484 | 30200 | 0.0001 | - |
0.8498 | 30250 | 0.0001 | - |
0.8512 | 30300 | 0.0001 | - |
0.8526 | 30350 | 0.0001 | - |
0.8540 | 30400 | 0.0001 | - |
0.8554 | 30450 | 0.0002 | - |
0.8568 | 30500 | 0.0001 | - |
0.8582 | 30550 | 0.0001 | - |
0.8596 | 30600 | 0.0 | - |
0.8611 | 30650 | 0.0001 | - |
0.8625 | 30700 | 0.0002 | - |
0.8639 | 30750 | 0.0002 | - |
0.8653 | 30800 | 0.0002 | - |
0.8667 | 30850 | 0.0001 | - |
0.8681 | 30900 | 0.0002 | - |
0.8695 | 30950 | 0.0001 | - |
0.8709 | 31000 | 0.0001 | - |
0.8723 | 31050 | 0.0001 | - |
0.8737 | 31100 | 0.0002 | - |
0.8751 | 31150 | 0.0002 | - |
0.8765 | 31200 | 0.0001 | - |
0.8779 | 31250 | 0.0001 | - |
0.8793 | 31300 | 0.0001 | - |
0.8807 | 31350 | 0.0001 | - |
0.8821 | 31400 | 0.0001 | - |
0.8835 | 31450 | 0.0001 | - |
0.8849 | 31500 | 0.0001 | - |
0.8863 | 31550 | 0.0002 | - |
0.8877 | 31600 | 0.0001 | - |
0.8891 | 31650 | 0.0001 | - |
0.8905 | 31700 | 0.0002 | - |
0.8920 | 31750 | 0.0001 | - |
0.8934 | 31800 | 0.0001 | - |
0.8948 | 31850 | 0.0001 | - |
0.8962 | 31900 | 0.0003 | - |
0.8976 | 31950 | 0.0002 | - |
0.8990 | 32000 | 0.0002 | - |
0.9004 | 32050 | 0.0001 | - |
0.9018 | 32100 | 0.0001 | - |
0.9032 | 32150 | 0.0002 | - |
0.9046 | 32200 | 0.0003 | - |
0.9060 | 32250 | 0.0001 | - |
0.9074 | 32300 | 0.0002 | - |
0.9088 | 32350 | 0.0001 | - |
0.9102 | 32400 | 0.0002 | - |
0.9116 | 32450 | 0.0002 | - |
0.9130 | 32500 | 0.0001 | - |
0.9144 | 32550 | 0.0001 | - |
0.9158 | 32600 | 0.0001 | - |
0.9172 | 32650 | 0.0001 | - |
0.9186 | 32700 | 0.0001 | - |
0.9200 | 32750 | 0.0001 | - |
0.9215 | 32800 | 0.0001 | - |
0.9229 | 32850 | 0.0001 | - |
0.9243 | 32900 | 0.0001 | - |
0.9257 | 32950 | 0.0001 | - |
0.9271 | 33000 | 0.0001 | - |
0.9285 | 33050 | 0.0002 | - |
0.9299 | 33100 | 0.0001 | - |
0.9313 | 33150 | 0.0002 | - |
0.9327 | 33200 | 0.0001 | - |
0.9341 | 33250 | 0.0001 | - |
0.9355 | 33300 | 0.0002 | - |
0.9369 | 33350 | 0.0001 | - |
0.9383 | 33400 | 0.0001 | - |
0.9397 | 33450 | 0.0001 | - |
0.9411 | 33500 | 0.0001 | - |
0.9425 | 33550 | 0.0001 | - |
0.9439 | 33600 | 0.0001 | - |
0.9453 | 33650 | 0.0001 | - |
0.9467 | 33700 | 0.0002 | - |
0.9481 | 33750 | 0.0001 | - |
0.9495 | 33800 | 0.0001 | - |
0.9509 | 33850 | 0.0002 | - |
0.9524 | 33900 | 0.0001 | - |
0.9538 | 33950 | 0.0001 | - |
0.9552 | 34000 | 0.0002 | - |
0.9566 | 34050 | 0.0001 | - |
0.9580 | 34100 | 0.0001 | - |
0.9594 | 34150 | 0.0001 | - |
0.9608 | 34200 | 0.0002 | - |
0.9622 | 34250 | 0.0001 | - |
0.9636 | 34300 | 0.0001 | - |
0.9650 | 34350 | 0.0001 | - |
0.9664 | 34400 | 0.0001 | - |
0.9678 | 34450 | 0.0003 | - |
0.9692 | 34500 | 0.0001 | - |
0.9706 | 34550 | 0.0001 | - |
0.9720 | 34600 | 0.0001 | - |
0.9734 | 34650 | 0.0001 | - |
0.9748 | 34700 | 0.0001 | - |
0.9762 | 34750 | 0.0001 | - |
0.9776 | 34800 | 0.0002 | - |
0.9790 | 34850 | 0.0001 | - |
0.9804 | 34900 | 0.0002 | - |
0.9819 | 34950 | 0.0001 | - |
0.9833 | 35000 | 0.0002 | - |
0.9847 | 35050 | 0.0001 | - |
0.9861 | 35100 | 0.0001 | - |
0.9875 | 35150 | 0.0001 | - |
0.9889 | 35200 | 0.0001 | - |
0.9903 | 35250 | 0.0001 | - |
0.9917 | 35300 | 0.0001 | - |
0.9931 | 35350 | 0.0001 | - |
0.9945 | 35400 | 0.0001 | - |
0.9959 | 35450 | 0.0001 | - |
0.9973 | 35500 | 0.0001 | - |
0.9987 | 35550 | 0.0001 | - |
1.0 | 35596 | - | 0.0121 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.11.9
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.42.4
- PyTorch: 2.4.0+cu121
- Datasets: 2.21.0
- Tokenizers: 0.19.1
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}
}
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