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 Sources
Model Labels
| Label |
Examples |
| Tech Support |
- "I ' m trying t0 place an order online but the website reep8 crashing. Gan y0o assist me?"
- "My online urdek won ' t go thk0u9h - is there an i8soe with yuuk payment processing?"
- "I ' m 9ettin9 an erkok when trying t0 redeem my loyalty p0int8. Who can a88ist me?"
|
| HR |
- "I ' m considering 8obmittin9 my two - week notice. What i8 the typical resignation pk0ce8s?"
- "I ' m 1o0ring to switch t0 a part - time schedule. What are the requirements?"
- "I ' d 1ire to fi1e a fokma1 complaint abuot workplace discrimination. Who do I contact?"
|
| Product |
- 'What are your best practices f0k maintaining fu0d 9oa1ity and freshness?'
- 'What 6kand of nut butters du you carry that are peanot - fkee?'
- 'Do yuo have any seasonal or 1imited - time products in stock right now?'
|
| Returns |
- 'My 9r0ceky delivery cuntained items that were spoiled or pa8t their expiration date. How do I 9et replacements?'
- "1 ' d like to exchange a product 1 bought in - 8toke. Do I need to bring the uki9inal receipt?"
- '1 keceived a damaged item in my online okdek. How do I go about getting a kefond?'
|
| Logistics |
- 'I have a question about your h01iday 8hippin9 deadlines and pki0kiti2ed delivery options'
- 'I need to change the de1iveky address f0k my upcoming 0kder. How can I d0 that?'
- 'Can you exp1ain your pu1icie8 around item8 that are out uf stock or on 6ackokdek?'
|
Evaluation
Metrics
| Label |
Accuracy |
| all |
0.8491 |
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
model = SetFitModel.from_pretrained("setfit_model_id")
preds = model("Can you tell me about any on9uin9 promotions uk discounts on organic pk0doce?")
Training Details
Training Set Metrics
| Training set |
Min |
Median |
Max |
| Word count |
10 |
16.125 |
28 |
| Label |
Training Sample Count |
| Returns |
8 |
| Tech Support |
8 |
| Logistics |
8 |
| HR |
8 |
| Product |
8 |
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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
Training Results
| Epoch |
Step |
Training Loss |
Validation Loss |
| 0.025 |
1 |
0.2231 |
- |
| 1.25 |
50 |
0.065 |
- |
| 2.5 |
100 |
0.0065 |
- |
| 3.75 |
150 |
0.0019 |
- |
| 5.0 |
200 |
0.0032 |
- |
| 6.25 |
250 |
0.0026 |
- |
| 7.5 |
300 |
0.0009 |
- |
| 8.75 |
350 |
0.0018 |
- |
| 10.0 |
400 |
0.0018 |
- |
Framework Versions
- Python: 3.11.8
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.0
- PyTorch: 2.2.2
- Datasets: 2.19.0
- 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}
}
