metadata
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: sentence-transformers/paraphrase-mpnet-base-v2
datasets:
- dendimaki/v1
metrics:
- accuracy
widget:
- text: so you know you said that layer three maybe sounded interesting
- text: >-
just this like sense of energy thats aliveness and aliveness tingly
aliveness
- text: >-
id say is pretty or really the dominant state unless i really focus on
location one and even then
- text: pervading presence
- text: nonduality for you
pipeline_tag: text-classification
inference: true
model-index:
- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: dendimaki/v1
type: dendimaki/v1
split: test
metrics:
- type: accuracy
value: 0.46352941176470586
name: Accuracy
SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a SetFit model trained on the dendimaki/v1 dataset 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: 26 classes
- Training Dataset: dendimaki/v1
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 |
|---|---|
| 20 |
|
| 26 |
|
| 27 |
|
| 18 |
|
| 28 |
|
| 16 |
|
| 17 |
|
| 25 |
|
| 19 |
|
| 15 |
|
| 8 |
|
| 14 |
|
| 22 |
|
| 3 |
|
| 4 |
|
| 6 |
|
| 21 |
|
| 10 |
|
| 24 |
|
| 0 |
|
| 11 |
|
| 1 |
|
| 9 |
|
| 5 |
|
| 12 |
|
| 23 |
|
Evaluation
Metrics
| Label | Accuracy |
|---|---|
| all | 0.4635 |
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("dendimaki/fewshot-model")
# Run inference
preds = model("pervading presence")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 1 | 21.9052 | 247 |
| Label | Training Sample Count |
|---|---|
| 0 | 2 |
| 1 | 1 |
| 3 | 5 |
| 4 | 2 |
| 5 | 4 |
| 6 | 11 |
| 8 | 1 |
| 9 | 2 |
| 10 | 1 |
| 11 | 2 |
| 12 | 3 |
| 14 | 4 |
| 15 | 8 |
| 16 | 8 |
| 17 | 11 |
| 18 | 28 |
| 19 | 25 |
| 20 | 14 |
| 21 | 4 |
| 22 | 7 |
| 23 | 2 |
| 24 | 1 |
| 25 | 13 |
| 26 | 30 |
| 27 | 36 |
| 28 | 7 |
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 |
|---|---|---|---|
| 0.0017 | 1 | 0.252 | - |
| 0.0862 | 50 | 0.1891 | - |
| 0.1724 | 100 | 0.1793 | - |
| 0.2586 | 150 | 0.1848 | - |
| 0.3448 | 200 | 0.1033 | - |
| 0.4310 | 250 | 0.0473 | - |
| 0.5172 | 300 | 0.1213 | - |
| 0.6034 | 350 | 0.0343 | - |
| 0.6897 | 400 | 0.0276 | - |
| 0.7759 | 450 | 0.0262 | - |
| 0.8621 | 500 | 0.0425 | - |
| 0.9483 | 550 | 0.0482 | - |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.1
- PyTorch: 2.2.1+cu121
- Datasets: 2.19.0
- Tokenizers: 0.19.1
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}
}