You can deploy this model and start earning money today!

pEpOo/
catastrophy5

Name: catastrophy5
Availability: InStock

setfit

safetensors

mpnet

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

sentence-transformers/all-mpnet-base-v2

finetune

model-index

Views

No views yet

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
0	"Was '80s New #Wave a #Casualty of #AIDS?: Tweet And Since they\x89Ûªd grown up watching David\x89Û_ http://t.co/qBecjli7cx" "@CharlesDagnall He's getting 50 here I think. Salt. Wounds. Rub. In." 'Navy sidelines 3 newest subs http://t.co/gpVZV0249Y'
1	'The Latest: More Homes Razed by Northern California Wildfire - ABC News http://t.co/bKsYymvIsg #GN' '@Durban_Knight Rescuers are searching for hundreds of migrants in the Mediterranean after a boat carr... http://t.co/cWCVBuBs01 @Nosy_Be' 'NEMA Ekiti distributed relief materials to affected victims of Rain/Windstorm disaster at Ode-Ekiti in Gbonyin LGA.'

Evaluation

Metrics

Label	Accuracy
all	0.8172

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("pEpOo/catastrophy5")
# Run inference
preds = model("Stuart Broad Takes Eight Before Joe Root Runs Riot Against Aussies")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	14.9796	54

Label	Training Sample Count
0	1732
1	1313

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
0.0001	1	0.3383	-
0.0066	50	0.352	-
0.0131	100	0.3529	-
0.0197	150	0.2286	-
0.0263	200	0.2654	-
0.0328	250	0.2892	-
0.0394	300	0.1808	-
0.0460	350	0.2056	-
0.0525	400	0.0863	-
0.0591	450	0.2034	-
0.0657	500	0.1339	-
0.0722	550	0.1022	-
0.0788	600	0.1083	-
0.0854	650	0.1035	-
0.0919	700	0.1201	-
0.0985	750	0.0626	-
0.1051	800	0.1257	-
0.1117	850	0.1543	-
0.1182	900	0.0367	-
0.1248	950	0.1749	-
0.1314	1000	0.0553	-
0.1379	1050	0.0836	-
0.1445	1100	0.0161	-
0.1511	1150	0.1149	-
0.1576	1200	0.1144	-
0.1642	1250	0.0028	-
0.1708	1300	0.0037	-
0.1773	1350	0.1769	-
0.1839	1400	0.0172	-
0.1905	1450	0.0397	-
0.1970	1500	0.0645	-
0.2036	1550	0.0659	-
0.2102	1600	0.0014	-
0.2167	1650	0.0016	-
0.2233	1700	0.0729	-
0.2299	1750	0.0072	-
0.2364	1800	0.0175	-
0.2430	1850	0.0278	-
0.2496	1900	0.0537	-
0.2561	1950	0.0038	-
0.2627	2000	0.087	-
0.2693	2050	0.0459	-
0.2758	2100	0.0169	-
0.2824	2150	0.0112	-
0.2890	2200	0.001	-
0.2955	2250	0.0204	-
0.3021	2300	0.0796	-
0.3087	2350	0.0592	-
0.3153	2400	0.0003	-
0.3218	2450	0.0033	-
0.3284	2500	0.0309	-
0.3350	2550	0.0065	-
0.3415	2600	0.002	-
0.3481	2650	0.0076	-
0.3547	2700	0.0008	-
0.3612	2750	0.0023	-
0.3678	2800	0.0028	-
0.3744	2850	0.0171	-
0.3809	2900	0.0011	-
0.3875	2950	0.0015	-
0.3941	3000	0.0468	-
0.4006	3050	0.0075	-
0.4072	3100	0.0009	-
0.4138	3150	0.0334	-
0.4203	3200	0.0002	-
0.4269	3250	0.0001	-
0.4335	3300	0.0002	-
0.4400	3350	0.0001	-
0.4466	3400	0.021	-
0.4532	3450	0.0043	-
0.4597	3500	0.0084	-
0.4663	3550	0.0009	-
0.4729	3600	0.0033	-
0.4794	3650	0.0035	-
0.4860	3700	0.0004	-
0.4926	3750	0.0297	-
0.4991	3800	0.0004	-
0.5057	3850	0.0011	-
0.5123	3900	0.0238	-
0.5188	3950	0.0248	-
0.5254	4000	0.0293	-
0.5320	4050	0.0365	-
0.5386	4100	0.0261	-
0.5451	4150	0.0469	-
0.5517	4200	0.0098	-
0.5583	4250	0.0002	-
0.5648	4300	0.0236	-
0.5714	4350	0.0001	-
0.5780	4400	0.0001	-
0.5845	4450	0.0001	-
0.5911	4500	0.0138	-
0.5977	4550	0.0116	-
0.6042	4600	0.0003	-
0.6108	4650	0.0003	-
0.6174	4700	0.0001	-
0.6239	4750	0.0	-
0.6305	4800	0.0246	-
0.6371	4850	0.0001	-
0.6436	4900	0.0543	-
0.6502	4950	0.0001	-
0.6568	5000	0.0093	-
0.6633	5050	0.0001	-
0.6699	5100	0.0	-
0.6765	5150	0.0002	-
0.6830	5200	0.0001	-
0.6896	5250	0.0372	-
0.6962	5300	0.0	-
0.7027	5350	0.0001	-
0.7093	5400	0.0001	-
0.7159	5450	0.0003	-
0.7224	5500	0.0004	-
0.7290	5550	0.0001	-
0.7356	5600	0.0	-
0.7422	5650	0.0	-
0.7487	5700	0.0001	-
0.7553	5750	0.0001	-
0.7619	5800	0.0	-
0.7684	5850	0.0	-
0.7750	5900	0.0	-
0.7816	5950	0.0	-
0.7881	6000	0.0	-
0.7947	6050	0.0	-
0.8013	6100	0.0	-
0.8078	6150	0.0001	-
0.8144	6200	0.0001	-
0.8210	6250	0.0	-
0.8275	6300	0.0	-
0.8341	6350	0.0	-
0.8407	6400	0.0002	-
0.8472	6450	0.0	-
0.8538	6500	0.0001	-
0.8604	6550	0.0	-
0.8669	6600	0.0001	-
0.8735	6650	0.0001	-
0.8801	6700	0.0	-
0.8866	6750	0.0	-
0.8932	6800	0.0373	-
0.8998	6850	0.0	-
0.9063	6900	0.0	-
0.9129	6950	0.0272	-
0.9195	7000	0.0	-
0.9260	7050	0.0	-
0.9326	7100	0.0001	-
0.9392	7150	0.0	-
0.9458	7200	0.0002	-
0.9523	7250	0.0001	-
0.9589	7300	0.0	-
0.9655	7350	0.0	-
0.9720	7400	0.0	-
0.9786	7450	0.0001	-
0.9852	7500	0.0	-
0.9917	7550	0.0	-
0.9983	7600	0.0	-

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: 2.15.0
Tokenizers: 0.15.0

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}
}