You can deploy this model and start earning money today!

ANGKJ1995/
GIST-small-Embedding-v0-checkthat-fitset-256

Name: GIST-small-Embedding-v0-checkthat-fitset-256
Availability: InStock

setfit

safetensors

bert

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

avsolatorio/GIST-small-Embedding-v0

finetune

model-index

Views

No views yet

SetFit with avsolatorio/GIST-small-Embedding-v0

This is a SetFit model that can be used for Text Classification. This SetFit model uses avsolatorio/GIST-small-Embedding-v0 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: avsolatorio/GIST-small-Embedding-v0
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

objective

'"I have never seen it this bad," said Dan Domenech, executive director of the School Superintendents Association.'
'There will be an enormous increase of public revenue, as there was after the war from the carry-over of the wartime taxes.'
'No cases have been spotted so far of a strain that can evade tecovirimat, though the ruling class is warning of a “low barrier to resistance” which poses a risk that a resistant variant could emerge and spread.'

subjective

'But what of American individualism?'
'It’s a kind of brainwashing.'
'In theory, the problematic behavior parts of the New Mexico ruling could still prevent an illegal alien from being given authorization to practice law, but don’t count on it.'

Evaluation

Metrics

Label	Accuracy
all	0.9265

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("setfit_model_id")
# Run inference
preds = model("They are California, Florida, Illinois, Nebraska, New York, and Wyoming.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	22.7637	97

Label	Training Sample Count
objective	256
subjective	256

Training Hyperparameters

batch_size: (32, 32)
num_epochs: (1, 1)
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: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0002	1	0.2779	-
0.0122	50	0.2605	-
0.0243	100	0.2721	-
0.0365	150	0.2404	-
0.0486	200	0.2468	-
0.0608	250	0.1941	-
0.0730	300	0.0574	-
0.0851	350	0.0124	-
0.0973	400	0.0019	-
0.1094	450	0.0017	-
0.1216	500	0.0028	-
0.1338	550	0.0011	-
0.1459	600	0.0011	-
0.1581	650	0.0011	-
0.1702	700	0.0316	-
0.1824	750	0.0007	-
0.1946	800	0.001	-
0.2067	850	0.0009	-
0.2189	900	0.0008	-
0.2310	950	0.0007	-
0.2432	1000	0.0006	-
0.2554	1050	0.0006	-
0.2675	1100	0.0005	-
0.2797	1150	0.0005	-
0.2918	1200	0.0006	-
0.3040	1250	0.0006	-
0.3161	1300	0.0005	-
0.3283	1350	0.0005	-
0.3405	1400	0.001	-
0.3526	1450	0.0004	-
0.3648	1500	0.0005	-
0.3769	1550	0.0005	-
0.3891	1600	0.0004	-
0.4013	1650	0.0005	-
0.4134	1700	0.0004	-
0.4256	1750	0.0004	-
0.4377	1800	0.0004	-
0.4499	1850	0.0004	-
0.4621	1900	0.0003	-
0.4742	1950	0.0004	-
0.4864	2000	0.0004	-
0.4985	2050	0.0003	-
0.5107	2100	0.0003	-
0.5229	2150	0.0004	-
0.5350	2200	0.0004	-
0.5472	2250	0.0003	-
0.5593	2300	0.0003	-
0.5715	2350	0.0004	-
0.5837	2400	0.0004	-
0.5958	2450	0.0004	-
0.6080	2500	0.0003	-
0.6201	2550	0.0003	-
0.6323	2600	0.0003	-
0.6445	2650	0.0003	-
0.6566	2700	0.0003	-
0.6688	2750	0.0003	-
0.6809	2800	0.0003	-
0.6931	2850	0.0002	-
0.7053	2900	0.0003	-
0.7174	2950	0.0003	-
0.7296	3000	0.0003	-
0.7417	3050	0.0002	-
0.7539	3100	0.0003	-
0.7661	3150	0.0003	-
0.7782	3200	0.0003	-
0.7904	3250	0.0003	-
0.8025	3300	0.0003	-
0.8147	3350	0.0003	-
0.8268	3400	0.0003	-
0.8390	3450	0.0003	-
0.8512	3500	0.0003	-
0.8633	3550	0.0003	-
0.8755	3600	0.0003	-
0.8876	3650	0.0002	-
0.8998	3700	0.0003	-
0.9120	3750	0.0003	-
0.9241	3800	0.0002	-
0.9363	3850	0.0003	-
0.9484	3900	0.0003	-
0.9606	3950	0.0003	-
0.9728	4000	0.0003	-
0.9849	4050	0.0002	-
0.9971	4100	0.0003	-

Framework Versions

Python: 3.11.9
SetFit: 1.0.3
Sentence Transformers: 3.0.0
Transformers: 4.40.2
PyTorch: 2.1.2
Datasets: 2.19.1
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}
}

ANGKJ1995/GIST-small-Embedding-v0-checkthat-fitset-256