SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 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: sentence-transformers/paraphrase-mpnet-base-v2
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 8 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 |
---|---|
RequestMoveToFloor |
|
Confirm |
|
RequestEmployeeLocation |
|
Feedback |
|
Repeat |
|
CurrentFloor |
|
Stop |
|
OutOfCoverage |
|
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("victomoe/setfit-intent-classifier")
# Run inference
preds = model("Yes, please.")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 1 | 5.2267 | 10 |
Label | Training Sample Count |
---|---|
Confirm | 22 |
CurrentFloor | 21 |
Feedback | 22 |
OutOfCoverage | 22 |
Repeat | 20 |
RequestEmployeeLocation | 22 |
RequestMoveToFloor | 23 |
Stop | 20 |
Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (10, 10)
- 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0012 | 1 | 0.0001 | - |
0.0618 | 50 | 0.0001 | - |
0.1236 | 100 | 0.0001 | - |
0.1854 | 150 | 0.0001 | - |
0.2472 | 200 | 0.0001 | - |
0.3090 | 250 | 0.0001 | - |
0.3708 | 300 | 0.0001 | - |
0.4326 | 350 | 0.0001 | - |
0.4944 | 400 | 0.0001 | - |
0.5562 | 450 | 0.0001 | - |
0.6180 | 500 | 0.0001 | - |
0.6799 | 550 | 0.0001 | - |
0.7417 | 600 | 0.0012 | - |
0.8035 | 650 | 0.0001 | - |
0.8653 | 700 | 0.0001 | - |
0.9271 | 750 | 0.0012 | - |
0.9889 | 800 | 0.0001 | - |
1.0507 | 850 | 0.0001 | - |
1.1125 | 900 | 0.0001 | - |
1.1743 | 950 | 0.0001 | - |
1.2361 | 1000 | 0.0001 | - |
1.2979 | 1050 | 0.0001 | - |
1.3597 | 1100 | 0.0001 | - |
1.4215 | 1150 | 0.0001 | - |
1.4833 | 1200 | 0.0001 | - |
1.5451 | 1250 | 0.0001 | - |
1.6069 | 1300 | 0.0001 | - |
1.6687 | 1350 | 0.0001 | - |
1.7305 | 1400 | 0.0001 | - |
1.7923 | 1450 | 0.0001 | - |
1.8541 | 1500 | 0.0023 | - |
1.9159 | 1550 | 0.0018 | - |
1.9778 | 1600 | 0.0007 | - |
2.0396 | 1650 | 0.0001 | - |
2.1014 | 1700 | 0.0001 | - |
2.1632 | 1750 | 0.0001 | - |
2.2250 | 1800 | 0.0001 | - |
2.2868 | 1850 | 0.0001 | - |
2.3486 | 1900 | 0.0001 | - |
2.4104 | 1950 | 0.0001 | - |
2.4722 | 2000 | 0.0001 | - |
2.5340 | 2050 | 0.0001 | - |
2.5958 | 2100 | 0.0001 | - |
2.6576 | 2150 | 0.0001 | - |
2.7194 | 2200 | 0.0001 | - |
2.7812 | 2250 | 0.0001 | - |
2.8430 | 2300 | 0.0001 | - |
2.9048 | 2350 | 0.0001 | - |
2.9666 | 2400 | 0.0001 | - |
3.0284 | 2450 | 0.0001 | - |
3.0902 | 2500 | 0.0001 | - |
3.1520 | 2550 | 0.0001 | - |
3.2138 | 2600 | 0.0001 | - |
3.2756 | 2650 | 0.0001 | - |
3.3375 | 2700 | 0.0001 | - |
3.3993 | 2750 | 0.0001 | - |
3.4611 | 2800 | 0.0001 | - |
3.5229 | 2850 | 0.0001 | - |
3.5847 | 2900 | 0.0001 | - |
3.6465 | 2950 | 0.0001 | - |
3.7083 | 3000 | 0.0001 | - |
3.7701 | 3050 | 0.0001 | - |
3.8319 | 3100 | 0.0 | - |
3.8937 | 3150 | 0.0 | - |
3.9555 | 3200 | 0.0001 | - |
4.0173 | 3250 | 0.0001 | - |
4.0791 | 3300 | 0.0 | - |
4.1409 | 3350 | 0.0001 | - |
4.2027 | 3400 | 0.0001 | - |
4.2645 | 3450 | 0.0001 | - |
4.3263 | 3500 | 0.0 | - |
4.3881 | 3550 | 0.0001 | - |
4.4499 | 3600 | 0.0001 | - |
4.5117 | 3650 | 0.0 | - |
4.5735 | 3700 | 0.0 | - |
4.6354 | 3750 | 0.0 | - |
4.6972 | 3800 | 0.0001 | - |
4.7590 | 3850 | 0.0 | - |
4.8208 | 3900 | 0.0 | - |
4.8826 | 3950 | 0.0 | - |
4.9444 | 4000 | 0.0 | - |
5.0062 | 4050 | 0.0 | - |
5.0680 | 4100 | 0.0 | - |
5.1298 | 4150 | 0.0001 | - |
5.1916 | 4200 | 0.0148 | - |
5.2534 | 4250 | 0.0258 | - |
5.3152 | 4300 | 0.0147 | - |
5.3770 | 4350 | 0.0015 | - |
5.4388 | 4400 | 0.0001 | - |
5.5006 | 4450 | 0.0001 | - |
5.5624 | 4500 | 0.0001 | - |
5.6242 | 4550 | 0.0001 | - |
5.6860 | 4600 | 0.0001 | - |
5.7478 | 4650 | 0.0001 | - |
5.8096 | 4700 | 0.0001 | - |
5.8714 | 4750 | 0.0001 | - |
5.9333 | 4800 | 0.0001 | - |
5.9951 | 4850 | 0.0001 | - |
6.0569 | 4900 | 0.0001 | - |
6.1187 | 4950 | 0.0001 | - |
6.1805 | 5000 | 0.0001 | - |
6.2423 | 5050 | 0.0001 | - |
6.3041 | 5100 | 0.0001 | - |
6.3659 | 5150 | 0.0001 | - |
6.4277 | 5200 | 0.0001 | - |
6.4895 | 5250 | 0.0001 | - |
6.5513 | 5300 | 0.0001 | - |
6.6131 | 5350 | 0.0001 | - |
6.6749 | 5400 | 0.0001 | - |
6.7367 | 5450 | 0.0001 | - |
6.7985 | 5500 | 0.0001 | - |
6.8603 | 5550 | 0.0001 | - |
6.9221 | 5600 | 0.0001 | - |
6.9839 | 5650 | 0.0001 | - |
7.0457 | 5700 | 0.0001 | - |
7.1075 | 5750 | 0.0001 | - |
7.1693 | 5800 | 0.0001 | - |
7.2311 | 5850 | 0.0001 | - |
7.2930 | 5900 | 0.0001 | - |
7.3548 | 5950 | 0.0001 | - |
7.4166 | 6000 | 0.0001 | - |
7.4784 | 6050 | 0.0001 | - |
7.5402 | 6100 | 0.0001 | - |
7.6020 | 6150 | 0.0001 | - |
7.6638 | 6200 | 0.0001 | - |
7.7256 | 6250 | 0.0001 | - |
7.7874 | 6300 | 0.0001 | - |
7.8492 | 6350 | 0.0001 | - |
7.9110 | 6400 | 0.0001 | - |
7.9728 | 6450 | 0.0001 | - |
8.0346 | 6500 | 0.0001 | - |
8.0964 | 6550 | 0.0001 | - |
8.1582 | 6600 | 0.0001 | - |
8.2200 | 6650 | 0.0001 | - |
8.2818 | 6700 | 0.0001 | - |
8.3436 | 6750 | 0.0001 | - |
8.4054 | 6800 | 0.0001 | - |
8.4672 | 6850 | 0.0 | - |
8.5290 | 6900 | 0.0001 | - |
8.5909 | 6950 | 0.0 | - |
8.6527 | 7000 | 0.0 | - |
8.7145 | 7050 | 0.0 | - |
8.7763 | 7100 | 0.0001 | - |
8.8381 | 7150 | 0.0001 | - |
8.8999 | 7200 | 0.0001 | - |
8.9617 | 7250 | 0.0 | - |
9.0235 | 7300 | 0.0 | - |
9.0853 | 7350 | 0.0 | - |
9.1471 | 7400 | 0.0001 | - |
9.2089 | 7450 | 0.0 | - |
9.2707 | 7500 | 0.0 | - |
9.3325 | 7550 | 0.0 | - |
9.3943 | 7600 | 0.0001 | - |
9.4561 | 7650 | 0.0001 | - |
9.5179 | 7700 | 0.0 | - |
9.5797 | 7750 | 0.0 | - |
9.6415 | 7800 | 0.0 | - |
9.7033 | 7850 | 0.0 | - |
9.7651 | 7900 | 0.0001 | - |
9.8269 | 7950 | 0.0 | - |
9.8888 | 8000 | 0.0001 | - |
9.9506 | 8050 | 0.0 | - |
Framework Versions
- Python: 3.10.8
- SetFit: 1.1.0
- Sentence Transformers: 3.1.1
- Transformers: 4.38.2
- PyTorch: 2.1.2
- Datasets: 2.17.1
- Tokenizers: 0.15.0
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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