metadata
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: >-
As someone on the line between Millenial and GenZ, yeah. Bars are
expensive and loud, and ubers home are expensive. It's a lot more
reasonable to pool a bit of money, throw some food on a grill, and buy our
own booze. We don't have the disposable income to hang out at bars
regularly.
- text: >-
When we switch main focus from college football to college basketball, I
can report back on Collier. But I'll be interested to see what the guys
who really crunch tape on draft prospects say as these seasons progress. I
know theres more than a few here in the sub. A huge 3 with skills would be
fun to stack next to Wemby though.
- text: >-
The gen Z kids I see are more risk averse in general, because exposure to
a lifetime on the internet has taught them that one mistake can ruin their
lives. It always blows my mind when boomers and Xers like me wonder why
kids have such high anxiety these days. It’s because they are regularly
exposed to the judgement and horrors of the world around them. We were
raised in a protective bubble mentally, in comparison
- text: >-
Well I guess I would expect this from a beer garden but I totally agree,
those vibes don’t belong at Coachella
- text: >-
Can Earned the Brewery Pioneer (Level 6) badge! Earned the I Believe in
IPA! (Level 5) badge!
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-mpnet-base-v2
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: 3 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 |
|---|---|
| 1 |
|
| 2 |
|
| 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/guinness-segments-model")
# Run inference
preds = model("Can Earned the Brewery Pioneer (Level 6) badge! Earned the I Believe in IPA! (Level 5) badge!")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 6 | 45.7143 | 135 |
| Label | Training Sample Count |
|---|---|
| 0 | 14 |
| 1 | 14 |
| 2 | 14 |
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 |
|---|---|---|---|
| 0.0095 | 1 | 0.2908 | - |
| 0.4762 | 50 | 0.0394 | - |
| 0.9524 | 100 | 0.0021 | - |
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: 2.16.1
- 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}
}