metadata
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: >-
Guy Cecil, the former head of the Democratic Senatorial Campaign Committee
and now the boss of a leading Democratic super PAC, voiced his frustration
with the inadequacy of Franken’s apology on Twitter.
- text: >-
Attorney Stephen Le Brocq, who operates a law firm in the North Texas area
sums up the treatment of Guyger perfectly when he says that “The affidavit
isn’t written objectively, not at the slightest.
- text: Phone This field is for validation purposes and should be left unchanged.
- text: The Twitter suspension caught me by surprise.
- text: >-
Popular pages like The AntiMedia (2.1 million fans), The Free Thought
Project (3.1 million fans), Press for Truth (350K fans), Police the Police
(1.9 million fans), Cop Block (1.7 million fans), and Punk Rock
Libertarians (125K fans) are just a few of the ones which were
unpublished.
pipeline_tag: text-classification
inference: true
model-index:
- name: SetFit
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.9987117552334943
name: Accuracy
SetFit
This is a SetFit model that can be used for Text Classification. A OneVsRestClassifier 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
- Classification head: a OneVsRestClassifier instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 3 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 |
---|---|
2 |
|
1 |
|
0 |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.9987 |
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("anismahmahi/doubt_repetition_with_noPropaganda_multiclass_SetFit")
# Run inference
preds = model("The Twitter suspension caught me by surprise.")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 1 | 20.4272 | 109 |
Label | Training Sample Count |
---|---|
0 | 131 |
1 | 129 |
2 | 2479 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (2, 2)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 5
- 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: True
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0006 | 1 | 0.3869 | - |
0.0292 | 50 | 0.3352 | - |
0.0584 | 100 | 0.2235 | - |
0.0876 | 150 | 0.1518 | - |
0.1168 | 200 | 0.1967 | - |
0.1460 | 250 | 0.1615 | - |
0.1752 | 300 | 0.1123 | - |
0.2044 | 350 | 0.1493 | - |
0.2336 | 400 | 0.0039 | - |
0.2629 | 450 | 0.0269 | - |
0.2921 | 500 | 0.0024 | - |
0.3213 | 550 | 0.0072 | - |
0.3505 | 600 | 0.0649 | - |
0.3797 | 650 | 0.0005 | - |
0.4089 | 700 | 0.0008 | - |
0.4381 | 750 | 0.0041 | - |
0.4673 | 800 | 0.0009 | - |
0.4965 | 850 | 0.0004 | - |
0.5257 | 900 | 0.0013 | - |
0.5549 | 950 | 0.0013 | - |
0.5841 | 1000 | 0.0066 | - |
0.6133 | 1050 | 0.0355 | - |
0.6425 | 1100 | 0.0004 | - |
0.6717 | 1150 | 0.0013 | - |
0.7009 | 1200 | 0.0003 | - |
0.7301 | 1250 | 0.0002 | - |
0.7593 | 1300 | 0.0008 | - |
0.7886 | 1350 | 0.0002 | - |
0.8178 | 1400 | 0.0002 | - |
0.8470 | 1450 | 0.0004 | - |
0.8762 | 1500 | 0.1193 | - |
0.9054 | 1550 | 0.0002 | - |
0.9346 | 1600 | 0.0002 | - |
0.9638 | 1650 | 0.0002 | - |
0.9930 | 1700 | 0.0002 | - |
1.0 | 1712 | - | 0.0073 |
1.0222 | 1750 | 0.0002 | - |
1.0514 | 1800 | 0.0006 | - |
1.0806 | 1850 | 0.0005 | - |
1.1098 | 1900 | 0.0001 | - |
1.1390 | 1950 | 0.0012 | - |
1.1682 | 2000 | 0.0003 | - |
1.1974 | 2050 | 0.0344 | - |
1.2266 | 2100 | 0.0038 | - |
1.2558 | 2150 | 0.0001 | - |
1.2850 | 2200 | 0.0003 | - |
1.3143 | 2250 | 0.0114 | - |
1.3435 | 2300 | 0.0001 | - |
1.3727 | 2350 | 0.0001 | - |
1.4019 | 2400 | 0.0001 | - |
1.4311 | 2450 | 0.0001 | - |
1.4603 | 2500 | 0.0005 | - |
1.4895 | 2550 | 0.0086 | - |
1.5187 | 2600 | 0.0001 | - |
1.5479 | 2650 | 0.0002 | - |
1.5771 | 2700 | 0.0001 | - |
1.6063 | 2750 | 0.0002 | - |
1.6355 | 2800 | 0.0001 | - |
1.6647 | 2850 | 0.0001 | - |
1.6939 | 2900 | 0.0001 | - |
1.7231 | 2950 | 0.0001 | - |
1.7523 | 3000 | 0.0001 | - |
1.7815 | 3050 | 0.0001 | - |
1.8107 | 3100 | 0.0 | - |
1.8400 | 3150 | 0.0001 | - |
1.8692 | 3200 | 0.0001 | - |
1.8984 | 3250 | 0.0001 | - |
1.9276 | 3300 | 0.0 | - |
1.9568 | 3350 | 0.0001 | - |
1.9860 | 3400 | 0.0002 | - |
2.0 | 3424 | - | 0.0053 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.16.1
- Tokenizers: 0.15.0
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}
}