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davidadamczyk/
setfit-model-6

Name: setfit-model-6
Availability: InStock

setfit

safetensors

mpnet

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

sentence-transformers/all-mpnet-base-v2

finetune

model-index

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SetFit with sentence-transformers/all-mpnet-base-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-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/all-mpnet-base-v2
Classification head: a LogisticRegression instance
Maximum Sequence Length: 384 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

yes

'40K Americans a year die in auto accidents. Countless more die from cancer, guns, suicide. Many of these deaths are preventable but you can still buy cigarettes and guns. Yesterday Ken Block died while snowmobiling -- he is most famous for his automotive exploits, either driving in rally stages or in gymkhana stunt driving videos. Jeremy Renner was hit by a plow truck and airlifted to a hospital yesterday.You can look at anything Red Bull sponsors and see people BASE jumping, wearing squirrel suits flying through mountain passes, or free climbing at deathly heights. They even sponsored a parachute drop from space.Athletes have died while playing other sports that are seemingly "safe". Kids still play with aluminum bats. Hockey players routinely check each other. Men's lacrosse has pads and allows physical contact, not quite as much as football, but not far off, either. MMA leagues are very popular, too -- and feature men and women both. Not to mention boxing, which fills arenas with title matches.\n'
'My one and only flight on a 74 was from NYC to Amsterdam, Amsterdam to Dubai. We stopped in Amsterdam for 90 minutes while they did a security sweep, got on the same plane to Dubai. That plane barring unforeseen tragedy will fly on at least for 40 years.\n'
'That is a pretty darned low body count compared to independent estimates.Near the end of December it was reported that..."Around 9,000 people in China are probably dying each day from COVID-19, UK-based health data firm Airfinity "Based on a rate of 9k per day over the same period, the Chinese Government estimate should be closer to 315,000 deaths due to COVID\n'

'A cautionary story: My mother and my brother are both alcoholics. Several years apart they each suffered a traumatic brain injury while drinking. Within a few years, due to their brain injuries, they each developed vascular dementia. They now reside in the same care facility. It is endless sadness thinking of the lives they should be leading, and everything that has been lost to alcohol.\n'
"I've concluded that if states such as Mississippi, Alabama, Kentucky, Arkansas, Louisiana, South Carolina, Nebraska, et al, want to leave the Union, they should. And, yes, that includes Texas as well. If one looks at critical performance levels such as life expectancy, infant mortality, income, educational levels, access to health care and more (even voter participation levels), invariably this group ranks at the bottom. And, if one looks at federal spending levels, these states receive more than they contribute. Essentially, their ROI is terrible. Yet, what they have in their states is what they envision for the country as a whole. No, thanks. Let them go. I think that is what they'd prefer anyway. How they provide for their common defense and promote their own general welfare is up to them.\n"
'Unless the US gov't has borrowed euros or rubles or renminbi that we don't know about, it has zero "debt." What we're looking at is $31.5 trillion of ACCUMULATED DEFICITS expressed in US dollars. Why would the US Treasury/Federal Reserve ever have to borrow money that it can create at will out of thin air? And "debt" implies that we're going to pay it back, which is an absurd thought. Balances have to balance. If the public sector of our economy shows a deficit of $31.5 trillion, this means by force of logic that the private sector (plus the rest of the world) must show a SURPLUS of $31.5 trillion. That's our aggregate financial wealth. Trying to pay back the "debt" would drive us all into extreme poverty.BTW, it's one thing to say that the Fed is raising interest rates to fight inflation, and it's quite another thing (and incorrect) to claim the market is demanding higher rates. That is simply nonsense. The Fed sets interest rates and if you don't like it you can just go pound sand. The Fed doesn't sell bonds to "borrow" back the money it has just spent into the economy. It does so to remove excess currency from the banking system in order to meet its target interest rate. If it didn't, all banks would be flush with currency and never have to borrow from another bank to settle its accounts. This would drive interest rates down to zero.The notion that the gov't borrows its own currency and accumulates 'debt" is a zombie idea that just refuses to die.\n'

Evaluation

Metrics

Label	Accuracy
all	1.0

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("davidadamczyk/setfit-model-6")
# Run inference
preds = model("Jen that was a prop plane.in Buffalo....but still awfulAlso there was a Delta jet accident in 2006 on kentucky ...the plane took of on the wrong runway...49 killed
")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	9	127.2	277

Label	Training Sample Count
no	18
yes	22

Training Hyperparameters

batch_size: (16, 16)
num_epochs: (1, 1)
max_steps: -1
sampling_strategy: oversampling
num_iterations: 120
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
l2_weight: 0.01
seed: 42
eval_max_steps: -1
load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0017	1	0.4205	-
0.0833	50	0.1936	-
0.1667	100	0.0058	-
0.25	150	0.0003	-
0.3333	200	0.0002	-
0.4167	250	0.0001	-
0.5	300	0.0001	-
0.5833	350	0.0001	-
0.6667	400	0.0001	-
0.75	450	0.0001	-
0.8333	500	0.0001	-
0.9167	550	0.0001	-
1.0	600	0.0001	-

Framework Versions

Python: 3.10.13
SetFit: 1.1.0
Sentence Transformers: 3.0.1
Transformers: 4.45.2
PyTorch: 2.4.0+cu124
Datasets: 2.21.0
Tokenizers: 0.20.0

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}
}