metadata
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: >-
Now that the baffling, elongated, hyperreal coronation has occurred—no,
not that one—and Liz Truss has become Prime Minister, a degree of
intervention and action on energy bills has emerged, ahead of the looming
socioeconomic catastrophe facing the country this winter.
- text: But it needs to go much further.
- text: What could possibly go wrong?
- text: >-
If you are White you might feel bad about hurting others or you might feel
afraid to lose this privilege….Overcoming White privilege is a job that
must start with the White community….
- text: >-
[JF: Obviously, immigration wasn’t stopped: the current population of the
United States is 329.5 million—it passed 300 million in 2006.
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: 0.7736625514403292
name: Accuracy
SetFit
This is a SetFit model that can be used for Text Classification. A LinearSVC 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 LinearSVC 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 |
---|---|
SUBJ |
|
OBJ |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.7737 |
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("SOUMYADEEPSAR/SetFit_SubjectivityDetection")
# Run inference
preds = model("What could possibly go wrong?")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 3 | 22.085 | 77 |
Label | Training Sample Count |
---|---|
OBJ | 100 |
SUBJ | 100 |
Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (3, 3)
- 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 |
---|---|---|---|
0.0016 | 1 | 0.2686 | - |
0.0791 | 50 | 0.2494 | - |
0.1582 | 100 | 0.2639 | - |
0.2373 | 150 | 0.2258 | - |
0.3165 | 200 | 0.0176 | - |
0.3956 | 250 | 0.0027 | - |
0.4747 | 300 | 0.0017 | - |
0.5538 | 350 | 0.0013 | - |
0.6329 | 400 | 0.0016 | - |
0.7120 | 450 | 0.001 | - |
0.7911 | 500 | 0.0009 | - |
0.8703 | 550 | 0.001 | - |
0.9494 | 600 | 0.001 | - |
1.0285 | 650 | 0.0009 | - |
1.1076 | 700 | 0.0008 | - |
1.1867 | 750 | 0.0008 | - |
1.2658 | 800 | 0.0006 | - |
1.3449 | 850 | 0.0007 | - |
1.4241 | 900 | 0.0006 | - |
1.5032 | 950 | 0.0007 | - |
1.5823 | 1000 | 0.0006 | - |
1.6614 | 1050 | 0.0005 | - |
1.7405 | 1100 | 0.0006 | - |
1.8196 | 1150 | 0.0007 | - |
1.8987 | 1200 | 0.0005 | - |
1.9778 | 1250 | 0.0006 | - |
2.0570 | 1300 | 0.0005 | - |
2.1361 | 1350 | 0.0005 | - |
2.2152 | 1400 | 0.0004 | - |
2.2943 | 1450 | 0.0005 | - |
2.3734 | 1500 | 0.0004 | - |
2.4525 | 1550 | 0.0004 | - |
2.5316 | 1600 | 0.0004 | - |
2.6108 | 1650 | 0.0004 | - |
2.6899 | 1700 | 0.0005 | - |
2.7690 | 1750 | 0.0005 | - |
2.8481 | 1800 | 0.0004 | - |
2.9272 | 1850 | 0.0005 | - |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.4.0
- Transformers: 4.37.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.17.1
- 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}
}