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 ' am trying to place an orden online bt Then website keeps crashing. Can you assit my?"
- "Mi online order won ' t go throw - is there an isuue with years pament prossesing?"
- "I ' m goning an error when tryied tou redeem my loyality points. Who cen assist we?"
|
| HR |
- "I ' m considere submitting my ow - weeck notice. Waht It's tehe typical resignation process?"
- "I ' m looking e swich to a part - time sehdule. Whate re rhe requirements?"
- "In ' d loke to fill a formal complain about worksplace discrimination. Who did I contact?"
|
| Product |
- 'Whots are your best practices ofr mantain foord quality and freshness?'
- 'Whots newbrand ow nut butters dou you carry tahat are peanut - free?'
- 'Do you hafe any seasonal nor limited - tíme produts in stock rignt now?'
|
| Returns |
- 'My grocery delivary contained items tath where spoiled or pas their expiration date. How dos me get replacements?'
- "I ' d llike to exchange a product I bought in - store. Du hi needs yo bring tie oringal receipt?"
- 'I eceibed de demaged item in my online oder. Hou do I’m go about getting a refund?'
|
| Logistics |
- 'I have a question about youtr Holiday shiping deathlines and prioritized delivery options'
- 'I nedd to change the delivery addrss foy mh upcoming older. How can I go that?'
- 'Can jou explain York polices around iterms that approxmatlly out of stock or on backorder?'
|
Evaluation
Metrics
| Label |
Accuracy |
| all |
0.9057 |
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 I about eny ongoing promoistion onr discounts onteh organic produce?")
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 |
0.2185 |
- |
| 1.25 |
50 |
0.0888 |
- |
| 2.5 |
100 |
0.0157 |
- |
| 3.75 |
150 |
0.0053 |
- |
| 5.0 |
200 |
0.0033 |
- |
| 6.25 |
250 |
0.004 |
- |
| 7.5 |
300 |
0.0024 |
- |
| 8.75 |
350 |
0.0027 |
- |
| 10.0 |
400 |
0.0025 |
- |
Framework Versions
- Python: 3.11.8
- SetFit: 1.0.3
- Sentence Transformers: 2.6.1
- Transformers: 4.39.3
- PyTorch: 2.4.0.dev20240413
- Datasets: 2.18.0
- Tokenizers: 0.15.2
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}
}
