metadata
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: tron miner contract game . yield diamond 50 times earnings '
- text: >-
infinite yield revolutionizing yield farming experience optimizing
platform sustainability , capital efficiency safety . recognize def rapid
shifting ecosystem . dedicated staying cutting edge creating strategies
incentive , utilize compose latest secure tech . '
- text: >-
hmmcoin community driven def project . hmmcoin open source , decentralized
, peer peer digital currency uses innovative coin distribution model (
reward level schedule ) . coin distribution model : reward level schedule
contains 77 levels . starts level 1 , request equal 1 pmc . one million
coins requested level . million withdrawn coins amount received coins next
level decrease 7 % . every level users need perform actions advance next
level . level 1 : 1 pmc actions required : 1.000.000 actions level 2 :
0.93 pmc actions required : 1.075.268 actions etc . users get coin free
pay transaction fee . '
- text: ' pump dump cryptocurrency trading game lets make real money buying selling coins virtual trading market . '''
- text: >-
platform daily dividend investments , long term contract 3 tier referral
commissions . running tron blockchain verified smart contract . dividends
starting 2.5 % unlimited booster contracts 5.5 % shorter periods . '
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: 0.5666990448437753
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: 9 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 |
---|---|
games |
|
exchanges |
|
social |
|
defi |
|
marketplaces |
|
collectibles |
|
gambling |
|
other |
|
high-risk |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.5667 |
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("dappradar/setfit-model")
# Run inference
preds = model("tron miner contract game . yield diamond 50 times earnings '")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 7 | 65.7639 | 210 |
Label | Training Sample Count |
---|---|
collectibles | 8 |
defi | 8 |
exchanges | 8 |
gambling | 8 |
games | 8 |
high-risk | 8 |
marketplaces | 8 |
other | 8 |
social | 8 |
Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 10
- 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: True
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.0056 | 1 | 0.1857 | - |
0.2778 | 50 | 0.1235 | - |
0.5556 | 100 | 0.0434 | - |
0.8333 | 150 | 0.0148 | - |
1.0 | 180 | - | 0.2355 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.15.0
- Tokenizers: 0.15.0
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}
}