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richie-ghost/
setfit-FacebookAI-roberta-Large-MentalHealth-Topic-Check

Name: setfit-FacebookAI-roberta-Large-MentalHealth-Topic-Check
Availability: InStock

setfit

safetensors

roberta

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

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SetFit with FacebookAI/roberta-Large

This is a SetFit model that can be used for Text Classification. This SetFit model uses FacebookAI/roberta-Large 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: FacebookAI/roberta-Large
Classification head: a LogisticRegression instance
Maximum Sequence Length: 512 tokens
Number of Classes: 2 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
True	'Exploring historical landmarks in Europe' 'How to create an effective resume' 'Exercises to improve core strength'
False	'Feeling sad or empty for long periods without any specific reason' 'Dealing with the emotional impact of chronic illness' 'Understanding and coping with panic attacks'

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

python

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("richie-ghost/setfit-FacebookAI-roberta-Large-MentalHealth-Topic-Check")
# Run inference
preds = model("Understanding stock market trends")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	4	6.4583	11

Label	Training Sample Count
True	22
False	26

Training Hyperparameters

batch_size: (16, 16)
num_epochs: (8, 8)
max_steps: -1
sampling_strategy: oversampling
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
0.0132	1	0.4868	-
0.6579	50	0.0286	-
1.0	76	-	0.0079
1.3158	100	0.0028	-
1.9737	150	0.0005	-
2.0	152	-	0.0015
2.6316	200	0.0003	-
3.0	228	-	0.001
3.2895	250	0.0006	-
3.9474	300	0.0002	-
4.0	304	-	0.0009
4.6053	350	0.0001	-
5.0	380	-	0.0004
5.2632	400	0.0002	-
5.9211	450	0.0001	-
6.0	456	-	0.0005
6.5789	500	0.0001	-
7.0	532	-	0.0006
7.2368	550	0.0001	-
7.8947	600	0.0002	-
8.0	608	-	0.0008

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.10.12
SetFit: 1.0.3
Sentence Transformers: 2.7.0
Transformers: 4.40.0
PyTorch: 2.2.1+cu121
Datasets: 2.19.0
Tokenizers: 0.19.1

Citation

BibTeX

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}
}