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 |
---|---|
Generalreply |
|
Lookup_1 |
|
Lookup |
|
Aggregation |
|
Tablejoin |
|
Viewtables |
|
Rejection |
|
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")
# Run inference
preds = model("Can I have avg Cost_Efficiency")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 1 | 8.6563 | 62 |
Label | Training Sample Count |
---|---|
Tablejoin | 129 |
Rejection | 77 |
Aggregation | 282 |
Lookup | 60 |
Generalreply | 63 |
Viewtables | 74 |
Lookup_1 | 150 |
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 | 0.2038 | - |
0.0014 | 50 | 0.2019 | - |
0.0029 | 100 | 0.1983 | - |
0.0043 | 150 | 0.206 | - |
0.0057 | 200 | 0.2268 | - |
0.0071 | 250 | 0.2025 | - |
0.0086 | 300 | 0.2041 | - |
0.0100 | 350 | 0.1426 | - |
0.0114 | 400 | 0.1513 | - |
0.0129 | 450 | 0.1215 | - |
0.0143 | 500 | 0.1426 | - |
0.0157 | 550 | 0.0859 | - |
0.0172 | 600 | 0.0486 | - |
0.0186 | 650 | 0.0378 | - |
0.0200 | 700 | 0.0519 | - |
0.0214 | 750 | 0.0717 | - |
0.0229 | 800 | 0.1161 | - |
0.0243 | 850 | 0.0771 | - |
0.0257 | 900 | 0.074 | - |
0.0272 | 950 | 0.0567 | - |
0.0286 | 1000 | 0.0223 | - |
0.0300 | 1050 | 0.0266 | - |
0.0315 | 1100 | 0.0261 | - |
0.0329 | 1150 | 0.0333 | - |
0.0343 | 1200 | 0.0107 | - |
0.0357 | 1250 | 0.0123 | - |
0.0372 | 1300 | 0.0193 | - |
0.0386 | 1350 | 0.0039 | - |
0.0400 | 1400 | 0.0079 | - |
0.0415 | 1450 | 0.0035 | - |
0.0429 | 1500 | 0.003 | - |
0.0443 | 1550 | 0.0041 | - |
0.0457 | 1600 | 0.0038 | - |
0.0472 | 1650 | 0.002 | - |
0.0486 | 1700 | 0.0028 | - |
0.0500 | 1750 | 0.0056 | - |
0.0515 | 1800 | 0.0035 | - |
0.0529 | 1850 | 0.0027 | - |
0.0543 | 1900 | 0.0028 | - |
0.0558 | 1950 | 0.0028 | - |
0.0572 | 2000 | 0.0019 | - |
0.0586 | 2050 | 0.0046 | - |
0.0600 | 2100 | 0.0017 | - |
0.0615 | 2150 | 0.0016 | - |
0.0629 | 2200 | 0.0022 | - |
0.0643 | 2250 | 0.002 | - |
0.0658 | 2300 | 0.0029 | - |
0.0672 | 2350 | 0.0032 | - |
0.0686 | 2400 | 0.0018 | - |
0.0701 | 2450 | 0.0015 | - |
0.0715 | 2500 | 0.0015 | - |
0.0729 | 2550 | 0.0016 | - |
0.0743 | 2600 | 0.0012 | - |
0.0758 | 2650 | 0.0014 | - |
0.0772 | 2700 | 0.0015 | - |
0.0786 | 2750 | 0.0018 | - |
0.0801 | 2800 | 0.0012 | - |
0.0815 | 2850 | 0.0009 | - |
0.0829 | 2900 | 0.001 | - |
0.0843 | 2950 | 0.0011 | - |
0.0858 | 3000 | 0.0011 | - |
0.0872 | 3050 | 0.001 | - |
0.0886 | 3100 | 0.0012 | - |
0.0901 | 3150 | 0.0006 | - |
0.0915 | 3200 | 0.0013 | - |
0.0929 | 3250 | 0.0007 | - |
0.0944 | 3300 | 0.0007 | - |
0.0958 | 3350 | 0.0009 | - |
0.0972 | 3400 | 0.0008 | - |
0.0986 | 3450 | 0.0005 | - |
0.1001 | 3500 | 0.001 | - |
0.1015 | 3550 | 0.001 | - |
0.1029 | 3600 | 0.0008 | - |
0.1044 | 3650 | 0.0007 | - |
0.1058 | 3700 | 0.0006 | - |
0.1072 | 3750 | 0.0009 | - |
0.1086 | 3800 | 0.0012 | - |
0.1101 | 3850 | 0.0007 | - |
0.1115 | 3900 | 0.0008 | - |
0.1129 | 3950 | 0.0009 | - |
0.1144 | 4000 | 0.0007 | - |
0.1158 | 4050 | 0.0007 | - |
0.1172 | 4100 | 0.0007 | - |
0.1187 | 4150 | 0.0006 | - |
0.1201 | 4200 | 0.0006 | - |
0.1215 | 4250 | 0.0011 | - |
0.1229 | 4300 | 0.0012 | - |
0.1244 | 4350 | 0.0007 | - |
0.1258 | 4400 | 0.0007 | - |
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0.1301 | 4550 | 0.0008 | - |
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0.1773 | 6200 | 0.0007 | - |
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0.1973 | 6900 | 0.0003 | - |
0.1987 | 6950 | 0.0004 | - |
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0.2016 | 7050 | 0.0003 | - |
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0.2287 | 8000 | 0.0003 | - |
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0.2316 | 8100 | 0.0003 | - |
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0.2345 | 8200 | 0.0002 | - |
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0.2516 | 8800 | 0.0003 | - |
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0.2573 | 9000 | 0.0002 | - |
0.2588 | 9050 | 0.0003 | - |
0.2602 | 9100 | 0.0003 | - |
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0.2630 | 9200 | 0.0003 | - |
0.2645 | 9250 | 0.0002 | - |
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0.2688 | 9400 | 0.0552 | - |
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0.6633 | 23200 | 0.0002 | - |
0.6648 | 23250 | 0.0002 | - |
0.6662 | 23300 | 0.0002 | - |
0.6676 | 23350 | 0.0001 | - |
0.6690 | 23400 | 0.0002 | - |
0.6705 | 23450 | 0.0002 | - |
0.6719 | 23500 | 0.0001 | - |
0.6733 | 23550 | 0.0002 | - |
0.6748 | 23600 | 0.0001 | - |
0.6762 | 23650 | 0.0002 | - |
0.6776 | 23700 | 0.0002 | - |
0.6791 | 23750 | 0.0002 | - |
0.6805 | 23800 | 0.0001 | - |
0.6819 | 23850 | 0.0002 | - |
0.6833 | 23900 | 0.0003 | - |
0.6848 | 23950 | 0.0002 | - |
0.6862 | 24000 | 0.0002 | - |
0.6876 | 24050 | 0.0001 | - |
0.6891 | 24100 | 0.0002 | - |
0.6905 | 24150 | 0.0001 | - |
0.6919 | 24200 | 0.0003 | - |
0.6934 | 24250 | 0.0002 | - |
0.6948 | 24300 | 0.0001 | - |
0.6962 | 24350 | 0.0001 | - |
0.6976 | 24400 | 0.0001 | - |
0.6991 | 24450 | 0.0001 | - |
0.7005 | 24500 | 0.0001 | - |
0.7019 | 24550 | 0.0002 | - |
0.7034 | 24600 | 0.0001 | - |
0.7048 | 24650 | 0.0002 | - |
0.7062 | 24700 | 0.0001 | - |
0.7076 | 24750 | 0.0002 | - |
0.7091 | 24800 | 0.0002 | - |
0.7105 | 24850 | 0.0002 | - |
0.7119 | 24900 | 0.0002 | - |
0.7134 | 24950 | 0.0001 | - |
0.7148 | 25000 | 0.0002 | - |
0.7162 | 25050 | 0.0001 | - |
0.7177 | 25100 | 0.0002 | - |
0.7191 | 25150 | 0.0001 | - |
0.7205 | 25200 | 0.0001 | - |
0.7219 | 25250 | 0.0002 | - |
0.7234 | 25300 | 0.0002 | - |
0.7248 | 25350 | 0.0002 | - |
0.7262 | 25400 | 0.0001 | - |
0.7277 | 25450 | 0.0002 | - |
0.7291 | 25500 | 0.0002 | - |
0.7305 | 25550 | 0.0002 | - |
0.7320 | 25600 | 0.0001 | - |
0.7334 | 25650 | 0.0002 | - |
0.7348 | 25700 | 0.0002 | - |
0.7362 | 25750 | 0.0002 | - |
0.7377 | 25800 | 0.0002 | - |
0.7391 | 25850 | 0.0001 | - |
0.7405 | 25900 | 0.0002 | - |
0.7420 | 25950 | 0.0002 | - |
0.7434 | 26000 | 0.0001 | - |
0.7448 | 26050 | 0.0001 | - |
0.7462 | 26100 | 0.0001 | - |
0.7477 | 26150 | 0.0001 | - |
0.7491 | 26200 | 0.0001 | - |
0.7505 | 26250 | 0.0002 | - |
0.7520 | 26300 | 0.0001 | - |
0.7534 | 26350 | 0.0001 | - |
0.7548 | 26400 | 0.0001 | - |
0.7563 | 26450 | 0.0002 | - |
0.7577 | 26500 | 0.0001 | - |
0.7591 | 26550 | 0.0002 | - |
0.7605 | 26600 | 0.0003 | - |
0.7620 | 26650 | 0.0002 | - |
0.7634 | 26700 | 0.0002 | - |
0.7648 | 26750 | 0.0001 | - |
0.7663 | 26800 | 0.0001 | - |
0.7677 | 26850 | 0.0002 | - |
0.7691 | 26900 | 0.0002 | - |
0.7706 | 26950 | 0.0001 | - |
0.7720 | 27000 | 0.0001 | - |
0.7734 | 27050 | 0.0001 | - |
0.7748 | 27100 | 0.0001 | - |
0.7763 | 27150 | 0.0001 | - |
0.7777 | 27200 | 0.0002 | - |
0.7791 | 27250 | 0.0001 | - |
0.7806 | 27300 | 0.0001 | - |
0.7820 | 27350 | 0.0001 | - |
0.7834 | 27400 | 0.0002 | - |
0.7848 | 27450 | 0.0001 | - |
0.7863 | 27500 | 0.0001 | - |
0.7877 | 27550 | 0.0001 | - |
0.7891 | 27600 | 0.0001 | - |
0.7906 | 27650 | 0.0001 | - |
0.7920 | 27700 | 0.0001 | - |
0.7934 | 27750 | 0.0001 | - |
0.7949 | 27800 | 0.0001 | - |
0.7963 | 27850 | 0.0001 | - |
0.7977 | 27900 | 0.0001 | - |
0.7991 | 27950 | 0.0003 | - |
0.8006 | 28000 | 0.0001 | - |
0.8020 | 28050 | 0.0002 | - |
0.8034 | 28100 | 0.0001 | - |
0.8049 | 28150 | 0.0002 | - |
0.8063 | 28200 | 0.0 | - |
0.8077 | 28250 | 0.0001 | - |
0.8091 | 28300 | 0.0001 | - |
0.8106 | 28350 | 0.0001 | - |
0.8120 | 28400 | 0.0001 | - |
0.8134 | 28450 | 0.0002 | - |
0.8149 | 28500 | 0.0001 | - |
0.8163 | 28550 | 0.0001 | - |
0.8177 | 28600 | 0.0001 | - |
0.8192 | 28650 | 0.0001 | - |
0.8206 | 28700 | 0.0001 | - |
0.8220 | 28750 | 0.0002 | - |
0.8234 | 28800 | 0.0002 | - |
0.8249 | 28850 | 0.0002 | - |
0.8263 | 28900 | 0.0001 | - |
0.8277 | 28950 | 0.0002 | - |
0.8292 | 29000 | 0.0001 | - |
0.8306 | 29050 | 0.0002 | - |
0.8320 | 29100 | 0.0001 | - |
0.8335 | 29150 | 0.0001 | - |
0.8349 | 29200 | 0.0001 | - |
0.8363 | 29250 | 0.0001 | - |
0.8377 | 29300 | 0.0001 | - |
0.8392 | 29350 | 0.0001 | - |
0.8406 | 29400 | 0.0001 | - |
0.8420 | 29450 | 0.0002 | - |
0.8435 | 29500 | 0.0001 | - |
0.8449 | 29550 | 0.0001 | - |
0.8463 | 29600 | 0.0001 | - |
0.8477 | 29650 | 0.0001 | - |
0.8492 | 29700 | 0.0001 | - |
0.8506 | 29750 | 0.0002 | - |
0.8520 | 29800 | 0.0002 | - |
0.8535 | 29850 | 0.0001 | - |
0.8549 | 29900 | 0.0002 | - |
0.8563 | 29950 | 0.0002 | - |
0.8578 | 30000 | 0.0002 | - |
0.8592 | 30050 | 0.0001 | - |
0.8606 | 30100 | 0.0002 | - |
0.8620 | 30150 | 0.0002 | - |
0.8635 | 30200 | 0.0003 | - |
0.8649 | 30250 | 0.0001 | - |
0.8663 | 30300 | 0.0001 | - |
0.8678 | 30350 | 0.0001 | - |
0.8692 | 30400 | 0.0001 | - |
0.8706 | 30450 | 0.0002 | - |
0.8721 | 30500 | 0.0001 | - |
0.8735 | 30550 | 0.0001 | - |
0.8749 | 30600 | 0.0001 | - |
0.8763 | 30650 | 0.0002 | - |
0.8778 | 30700 | 0.0002 | - |
0.8792 | 30750 | 0.0001 | - |
0.8806 | 30800 | 0.0002 | - |
0.8821 | 30850 | 0.0002 | - |
0.8835 | 30900 | 0.0001 | - |
0.8849 | 30950 | 0.0002 | - |
0.8863 | 31000 | 0.0002 | - |
0.8878 | 31050 | 0.0002 | - |
0.8892 | 31100 | 0.0001 | - |
0.8906 | 31150 | 0.0001 | - |
0.8921 | 31200 | 0.0001 | - |
0.8935 | 31250 | 0.0001 | - |
0.8949 | 31300 | 0.0002 | - |
0.8964 | 31350 | 0.0002 | - |
0.8978 | 31400 | 0.0001 | - |
0.8992 | 31450 | 0.0001 | - |
0.9006 | 31500 | 0.0002 | - |
0.9021 | 31550 | 0.0002 | - |
0.9035 | 31600 | 0.0001 | - |
0.9049 | 31650 | 0.0002 | - |
0.9064 | 31700 | 0.0001 | - |
0.9078 | 31750 | 0.0001 | - |
0.9092 | 31800 | 0.0001 | - |
0.9107 | 31850 | 0.0002 | - |
0.9121 | 31900 | 0.0002 | - |
0.9135 | 31950 | 0.0001 | - |
0.9149 | 32000 | 0.0001 | - |
0.9164 | 32050 | 0.0001 | - |
0.9178 | 32100 | 0.0001 | - |
0.9192 | 32150 | 0.0001 | - |
0.9207 | 32200 | 0.0001 | - |
0.9221 | 32250 | 0.0001 | - |
0.9235 | 32300 | 0.0002 | - |
0.9249 | 32350 | 0.0001 | - |
0.9264 | 32400 | 0.0001 | - |
0.9278 | 32450 | 0.0002 | - |
0.9292 | 32500 | 0.0001 | - |
0.9307 | 32550 | 0.0001 | - |
0.9321 | 32600 | 0.0002 | - |
0.9335 | 32650 | 0.0001 | - |
0.9350 | 32700 | 0.0001 | - |
0.9364 | 32750 | 0.0001 | - |
0.9378 | 32800 | 0.0001 | - |
0.9392 | 32850 | 0.0001 | - |
0.9407 | 32900 | 0.0002 | - |
0.9421 | 32950 | 0.0002 | - |
0.9435 | 33000 | 0.0 | - |
0.9450 | 33050 | 0.0001 | - |
0.9464 | 33100 | 0.0001 | - |
0.9478 | 33150 | 0.0001 | - |
0.9492 | 33200 | 0.0001 | - |
0.9507 | 33250 | 0.0001 | - |
0.9521 | 33300 | 0.0001 | - |
0.9535 | 33350 | 0.0002 | - |
0.9550 | 33400 | 0.0001 | - |
0.9564 | 33450 | 0.0001 | - |
0.9578 | 33500 | 0.0002 | - |
0.9593 | 33550 | 0.0001 | - |
0.9607 | 33600 | 0.0001 | - |
0.9621 | 33650 | 0.0002 | - |
0.9635 | 33700 | 0.0002 | - |
0.9650 | 33750 | 0.0001 | - |
0.9664 | 33800 | 0.0001 | - |
0.9678 | 33850 | 0.0001 | - |
0.9693 | 33900 | 0.0001 | - |
0.9707 | 33950 | 0.0 | - |
0.9721 | 34000 | 0.0002 | - |
0.9736 | 34050 | 0.0001 | - |
0.9750 | 34100 | 0.0001 | - |
0.9764 | 34150 | 0.0001 | - |
0.9778 | 34200 | 0.0001 | - |
0.9793 | 34250 | 0.0002 | - |
0.9807 | 34300 | 0.0002 | - |
0.9821 | 34350 | 0.0001 | - |
0.9836 | 34400 | 0.0001 | - |
0.9850 | 34450 | 0.0001 | - |
0.9864 | 34500 | 0.0001 | - |
0.9878 | 34550 | 0.0001 | - |
0.9893 | 34600 | 0.0001 | - |
0.9907 | 34650 | 0.0001 | - |
0.9921 | 34700 | 0.0001 | - |
0.9936 | 34750 | 0.0001 | - |
0.9950 | 34800 | 0.0001 | - |
0.9964 | 34850 | 0.0001 | - |
0.9979 | 34900 | 0.0002 | - |
0.9993 | 34950 | 0.0002 | - |
1.0 | 34975 | - | 0.0221 |
- 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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