metadata

library_name: setfit
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
metrics:
  - accuracy
widget:
  - text: >-
      we get some truly unique character studies and a cross-section of
      americana that hollywood could n't possibly fictionalize and be believed .
  - text: >-
      the movie is one of the best examples of artful large format filmmaking
      you are likely to see anytime soon .
  - text: my response to the film is best described as lukewarm .
  - text: >-
      the movie 's ripe , enrapturing beauty will tempt those willing to probe
      its inscrutable mysteries .
  - text: >-
      fear dot com is so rambling and disconnected it never builds any suspense
      .
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-mpnet-base-v2
model-index:
  - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.416289592760181
            name: Accuracy

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: 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
3	'an indispensable peek at the art and the agony of making people laugh .' "there 's a lot to recommend read my lips ." 'but it also has many of the things that made the first one charming .'
1	'a baffling mixed platter of gritty realism and magic realism with a hard-to-swallow premise .' 'each scene drags , underscoring the obvious , and sentiment is slathered on top .' 'even bigger and more ambitious than the first installment , spy kids 2 looks as if it were made by a highly gifted 12-year-old instead of a grown man .'
4	'about schmidt is undoubtedly one of the finest films of the year .' 'a compelling pre-wwii drama with vivid characters and a warm , moving message .' 'twenty years later , e.t. is still a cinematic touchstone .'
2	'an unremarkable , modern action\/comedy buddy movie whose only nod to nostalgia is in the title .' 'a movie that seems motivated more by a desire to match mortarboards with dead poets society and good will hunting than by its own story .' "i ca n't ."
0	'... about as exciting to watch as two last-place basketball teams playing one another on the final day of the season .' '... no charm , no laughs , no fun , no reason to watch .' 'this one aims for the toilet and scores a direct hit .'

Evaluation

Metrics

Label	Accuracy
all	0.4163

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("vidhi0206/setfit-paraphrase-mpnet-sst5")
# Run inference
preds = model("my response to the film is best described as lukewarm .")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	4	16.2	35

Label	Training Sample Count
0	8
1	8
2	8
3	8
4	8

Training Hyperparameters

batch_size: (8, 8)
num_epochs: (1, 1)
max_steps: -1
sampling_strategy: oversampling
num_iterations: 20
body_learning_rate: (2e-05, 2e-05)
head_learning_rate: 2e-05
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
0.005	1	0.2435	-
0.25	50	0.1137	-
0.5	100	0.0018	-
0.75	150	0.0049	-
1.0	200	0.0026	-

Framework Versions

Python: 3.8.10
SetFit: 1.0.3
Sentence Transformers: 2.3.1
Transformers: 4.37.2
PyTorch: 2.2.0+cu121
Datasets: 2.17.0
Tokenizers: 0.15.1

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}
}