metadata
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: >-
There’s been an early morning skate that’s been going on for years. They
have a Facebook page. I think its “6:30 am morning drop-in skate at
lakeview” might be on a break right now, but would be a good way to find
some other drop in skates or a way into some of the beer leagues. Mountain
biking is a great year around activity, and a good way to meet people.
Stop into any of the shops and ask about group rides.
- text: >-
So despite the minor audience, i think they can be quite damaging.
Especially after the burning of the tents. It's only the beginning (not
that I am expecting some form of January 6 style thing), they have been
brewing for a while and have now found an outlet.
- text: >-
My worry is that pretty much my entire social life is spent at a bar of
some sort, especially in the winter. So this month might suck. But I'm
going to go to my golf course's bar after work today with my 6pk of Craft
non-alcoholic beer so we'll see how that goes! Best of luck to you as
well!
- text: >-
@GayChemist @TylerDinucci It's Wisconsin. I assume that "drinking
fountains" mean beer there.
- text: >-
It's great! Spent some time at the brewery and got it there. Craft beer
and hot sauces are probably my two biggest vices lol.
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
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: 6 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 |
---|---|
3 |
|
1 |
|
4 |
|
2 |
|
5 |
|
0 |
|
Evaluation
Metrics
Label | Accuracy |
---|---|
all | 0.0 |
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("bhaskars113/beer-model")
# Run inference
preds = model("@GayChemist @TylerDinucci It's Wisconsin. I assume that \"drinking fountains\" mean beer there.")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 3 | 60.6875 | 260 |
Label | Training Sample Count |
---|---|
0 | 16 |
1 | 16 |
2 | 16 |
3 | 16 |
4 | 16 |
5 | 16 |
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 |
---|---|---|---|
0.0042 | 1 | 0.2324 | - |
0.2083 | 50 | 0.1497 | - |
0.4167 | 100 | 0.0141 | - |
0.625 | 150 | 0.002 | - |
0.8333 | 200 | 0.0013 | - |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.16.1
- Tokenizers: 0.15.1
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}
}