metadata
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: >-
He is Male, his heart rate is 148, he walks 10000 steps daily, and is
Normal. He slept at 1 hrs. Yesterday, he slept from 2.0hrs to 3.0 hrs,
with a duration of 90.0 minutes and 0 interruptions. The day before
yesterday, he slept from 22.0 hrs to 6.0 hrs, with a duration of 485.0
minutes and 0 interruptions.
- text: >-
She is Female, her heart rate is 68, she walks 11000 steps daily and is
Normal. She slept at 1 hrs. Yesterday, she slept from 1.0 hrs to 9.0 hrs,
with a duration of 495.0 minutes and 0 interruptions. The day before
yesterday, she slept from 1.0 hrs to 10.0 hrs, with a duration of 540.0
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: >-
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.
- text: >-
He is Male, his heart rate is 75, he walks 11000 steps daily, and is
Normal. He slept at 2 hrs. Yesterday, he slept from 3.0hrs to 7.0 hrs,
with a duration of 400.0 minutes and 2 interruptions. The day before
yesterday, he slept from 1.0 hrs to 8.0 hrs, with a duration of 450.0
minutes and 3 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: 0.8
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 | 0.8 |
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 75, he walks 11000 steps daily, and is Normal. He slept at 2 hrs. Yesterday, he slept from 3.0hrs to 7.0 hrs, with a duration of 400.0 minutes and 2 interruptions. The day before yesterday, he slept from 1.0 hrs to 8.0 hrs, with a duration of 450.0 minutes and 3 interruptions.")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 59 | 59.5 | 60 |
| Label | Training Sample Count |
|---|---|
| 0 | 2 |
| 1 | 6 |
| 2 | 2 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 15
- 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 |
|---|---|---|---|
| 0.0526 | 1 | 0.4337 | - |
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}
}