metadata

base_model: BAAI/bge-small-en-v1.5
library_name: setfit
metrics:
  - accuracy
pipeline_tag: text-classification
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: >-
      amy and matthew have a bit of a phony relationship , but the film works in
      spite of it .
  - text: it 's refreshing to see a romance this smart .
  - text: >-
      bogdanich is unashamedly pro-serbian and makes little attempt to give
      voice to the other side .
  - text: >-
      sayles has an eye for the ways people of different ethnicities talk to and
      about others outside the group .
  - text: >-
      eddie murphy and owen wilson have a cute partnership in i spy , but the
      movie around them is so often nearly nothing that their charm does n't do
      a load of good .
inference: true
model-index:
  - name: SetFit with BAAI/bge-small-en-v1.5
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.8511806699615596
            name: Accuracy

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: 2 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
negative	'there might be some sort of credible gender-provoking philosophy submerged here , but who the hell cares ?' 'represents the depths to which the girls-behaving-badly film has fallen .' '-lrb- a -rrb- crushing disappointment .'
positive	'what saves it ... and makes it one of the better video-game-based flicks , is that the film acknowledges upfront that the plot makes no sense , such that the lack of linearity is the point of emotional and moral departure for protagonist alice .' 'but it could be , by its art and heart , a necessary one .' 'a culture-clash comedy that , in addition to being very funny , captures some of the discomfort and embarrassment of being a bumbling american in europe .'

Label

Examples

negative

'there might be some sort of credible gender-provoking philosophy submerged here , but who the hell cares ?'
'represents the depths to which the girls-behaving-badly film has fallen .'
'-lrb- a -rrb- crushing disappointment .'

positive

'what saves it ... and makes it one of the better video-game-based flicks , is that the film acknowledges upfront that the plot makes no sense , such that the lack of linearity is the point of emotional and moral departure for protagonist alice .'
'but it could be , by its art and heart , a necessary one .'
'a culture-clash comedy that , in addition to being very funny , captures some of the discomfort and embarrassment of being a bumbling american in europe .'

Evaluation

Metrics

Label	Accuracy
all	0.8512

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("it 's refreshing to see a romance this smart .")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	6	22.5	45

Label	Training Sample Count
negative	8
positive	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
0.2	1	0.2102	-
10.0	50	0.0105	-

Framework Versions

Python: 3.10.0
SetFit: 1.0.3
Sentence Transformers: 3.0.1
Transformers: 4.44.0
PyTorch: 2.4.0
Datasets: 2.20.0
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}
}