metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
base_model: BAAI/bge-small-en-v1.5
metrics:
  - accuracy
widget:
  - text: >-
      Can you tell I about eny ongoing promoistion onr discounts onteh organic
      produce?
  - text: >-
      A bought somenting that didn ' th meet my expectations. It there ein way
      go get and partial refund?
  - text: >-
      I ' d like to palac a ladge ordet for my business. Do you offer ang
      specialy bulk shopping rates?
  - text: >-
      Ken you telle mo more about the origin atch farming practices of your
      cofffee beans?
  - text: >-
      I ' d llike to exchange a product I bought in - store. Du hi needs yo
      bring tie oringal receipt?
pipeline_tag: text-classification
inference: true

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: 5 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
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?'

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("setfit_model_id")
# Run inference
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

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}
}