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 70, he walks 8700 steps daily, and is Normal
Weight. He slept at 23 hrs. Yesterday, he slept from 23hrs to 7 hrs, with
a duration of 580 minutes and 4 interruptions. The day before yesterday,
he slept from 22 hrs to 6 hrs, with a duration of 490 minutes and 2
interruptions.
- text: >-
She is Female, her heart rate is 87, she walks 5300 steps daily and is
Overweight. She slept at 22 hrs. Yesterday, she slept from 21 hrs to 6
hrs, with a duration of 510 minutes and 5 interruptions. The day before
yesterday, she slept from 21 hrs to 6 hrs, with a duration of 410 minutes
and 3 interruptions.
- text: >-
She is Female, her heart rate is 83, she walks 4700 steps daily and is
Overweight. She slept at 22 hrs. Yesterday, she slept from 21 hrs to 6
hrs, with a duration of 490 minutes and 5 interruptions. The day before
yesterday, she slept from 21 hrs to 5 hrs, with a duration of 390 minutes
and 2 interruptions.
- text: >-
She is Female, her heart rate is 87, she walks 5700 steps daily and is
Overweight. She slept at 22 hrs. Yesterday, she slept from 21 hrs to 6
hrs, with a duration of 540 minutes and 5 interruptions. The day before
yesterday, she slept from 21 hrs to 6 hrs, with a duration of 430 minutes
and 3 interruptions.
- text: >-
She is Female, her heart rate is 85, she walks 5400 steps daily and is
Overweight. She slept at 22 hrs. Yesterday, she slept from 21 hrs to 6
hrs, with a duration of 530 minutes and 5 interruptions. The day before
yesterday, she slept from 21 hrs to 5 hrs, with a duration of 400 minutes
and 2 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: 0.8235294117647058
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: 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 |
|---|---|
| 0 |
|
| 1 |
|
Evaluation
Metrics
| Label | Accuracy |
|---|---|
| all | 0.8235 |
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 70, he walks 8700 steps daily, and is Normal Weight. He slept at 23 hrs. Yesterday, he slept from 23hrs to 7 hrs, with a duration of 580 minutes and 4 interruptions. The day before yesterday, he slept from 22 hrs to 6 hrs, with a duration of 490 minutes and 2 interruptions.")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 60 | 60.0 | 60 |
| Label | Training Sample Count |
|---|---|
| 0 | 3 |
| 1 | 5 |
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 |
|---|---|---|---|
| 0.05 | 1 | 0.4058 | - |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.5.1
- 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}
}