metadata
base_model: sentence-transformers/paraphrase-mpnet-base-v2
library_name: setfit
metrics:
- accuracy
- precision
- recall
- f1
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: >-
temperature salinity profile collected ctd cast nw atlantic limit40 w noaa
ship delaware ii noaa ship albatross iv 14 january 1997 30 october 1997
data collected 12 cruise multiple program ctd cast primarily made
conjunction bongo plankton tow plankton data included
- text: >-
plume height misr 82420 california fire 2020 multiangle imaging
spectroradiometer misr team nasa jet propulsion laboratory california
institute technology pasadena california provided map wildfire smoke plume
height several wildfire california derived data acquired misr instrument
board nasa terra satellite august 24 2020 misr carry nine fixed camera
view scene different angle period seven minute accounting true motion
cloud due wind angular parallax cloud different view used derive height
smoke plume data contain plume height information czu lightning complex
lnu lightning complex scu lightning complex fire observed misr
approximately 1210 pm local time august 24 2020 plume height give
indication fire intensity indicates whether smoke impacting air quality
groundlevel observation plume height also important input air quality
model predict smoke go might affect downwind misr plume height map
produced using misr interactive explorer minx software
- text: municipal land transfer tax revenue summary
- text: aggregated broccoli production yield
- text: street furniture bicycle parking
inference: false
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: 0.595
name: Accuracy
- type: precision
value: 0.7037037037037037
name: Precision
- type: recall
value: 0.8407079646017699
name: Recall
- type: f1
value: 0.7661290322580645
name: F1
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 OneVsRestClassifier 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 OneVsRestClassifier instance
- Maximum Sequence Length: 512 tokens
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Evaluation
Metrics
Label | Accuracy | Precision | Recall | F1 |
---|---|---|---|---|
all | 0.595 | 0.7037 | 0.8407 | 0.7661 |
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("lgd/setfit-multilabel")
# Run inference
preds = model("street furniture bicycle parking")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 1 | 59.4 | 411 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- 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: False
Training Results
Epoch | Step | Training Loss | Validation Loss |
---|---|---|---|
0.002 | 1 | 0.2153 | - |
0.1 | 50 | 0.201 | - |
0.2 | 100 | 0.1433 | - |
0.3 | 150 | 0.0812 | - |
0.4 | 200 | 0.0866 | - |
0.5 | 250 | 0.0306 | - |
0.6 | 300 | 0.1093 | - |
0.7 | 350 | 0.0647 | - |
0.8 | 400 | 0.0255 | - |
0.9 | 450 | 0.0421 | - |
1.0 | 500 | 0.0366 | - |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.39.0
- PyTorch: 2.3.1+cu121
- Datasets: 2.20.0
- 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}
}