metadata
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: >-
She is Female, her heart rate is 63, she walks 5000 steps daily and is
Underweight. She slept at 22 hrs. Yesterday, she slept from 21.0 hrs to
5.0 hrs, with a duration of 380.0 minutes and 1 interruptions. The day
before yesterday, she slept from 22.0 hrs to 7.0 hrs, with a duration of
440.0 minutes and 0 interruptions.
- text: >-
He is Male, his heart rate is 70, he walks 8500 steps daily, and is
Normal. He slept at 23 hrs. Yesterday, he slept from 23.0hrs to 8.0 hrs,
with a duration of 350.0 minutes and 3 interruptions. The day before
yesterday, he slept from 22.0 hrs to 6.0 hrs, with a duration of 390.0
minutes and 1 interruptions.
- text: >-
She is Female, her heart rate is 85, she walks 3000 steps daily and is
Overweight. She slept at 5 hrs. Yesterday, she slept from 6.0 hrs to 8.0
hrs, with a duration of 280.0 minutes and 2 interruptions. The day before
yesterday, she slept from 5.0 hrs to 9.0 hrs, with a duration of 320.0
minutes and 7 interruptions.
- text: >-
He is Male, his heart rate is 92, he walks 7500 steps daily, and is
Normal. He slept at 4 hrs. Yesterday, he slept from 5.0hrs to 9.0 hrs,
with a duration of 320.0 minutes and 3 interruptions. The day before
yesterday, he slept from 4.0 hrs to 10.0 hrs, with a duration of 350.0
minutes and 2 interruptions.
- text: >-
He is Male, his heart rate is 93, he walks 9800 steps daily, and is
Normal. He slept at 0 hrs. Yesterday, he slept from 23.0hrs to 7.0 hrs,
with a duration of 460.0 minutes and 0 interruptions. The day before
yesterday, he slept from 23.0 hrs to 7.0 hrs, with a duration of 425.0
minutes and 1 interruptions.
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: 0.6666666666666666
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: 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 |
|---|---|
| 1 |
|
| 2 |
|
| 0 |
|
Evaluation
Metrics
| Label | Accuracy |
|---|---|
| all | 0.6667 |
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("reecursion/few-shot-stress-detection")
# Run inference
preds = model("He is Male, his heart rate is 92, he walks 7500 steps daily, and is Normal. He slept at 4 hrs. Yesterday, he slept from 5.0hrs to 9.0 hrs, with a duration of 320.0 minutes and 3 interruptions. The day before yesterday, he slept from 4.0 hrs to 10.0 hrs, with a duration of 350.0 minutes and 2 interruptions.")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 59 | 59.5 | 60 |
| Label | Training Sample Count |
|---|---|
| 0 | 2 |
| 1 | 10 |
| 2 | 4 |
Training Hyperparameters
- batch_size: (16, 16)
- 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 |
|---|---|---|---|
| 0.025 | 1 | 0.421 | - |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.6.1
- Transformers: 4.38.2
- PyTorch: 2.2.1+cu121
- 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}
}