HelgeKn/Swag-multi-class-10

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 SetFitHead instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a Sentence Transformer with contrastive learning.
2. 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 SetFitHead instance
• Maximum Sequence Length: 512 tokens
• Number of Classes: 9 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
7	'Someone turns at the sound of the distant horns. 6000 horsemen, lead by people,' 'A man is playing the drums while wearing earphones. We' 'Now, someone stands below an overcast sky. Strands of his greasy black hair'
5	'Someone throws them onto someone and punches the both of them in the face. The crone then' 'Someone stirs the cookie dough in a bowl. The dough' 'A logo for a sports even is shown. There'
8	'A teenage girl is dressed in a long sleeve red leotard and jumps up on a balance beam. Once she is on, she' 'Someone watches with a heaving chest. He' 'A woman smiles at the camera. The woman'
0	"Someone changes into a Spanish policeman's outfit and heads down an outside staircase with the packed up rifle. As someone leaves, someone" 'He shows a water bottle he has along with a brush, and uses the brush to remove snow from the dash window of a car and the water to remove any excess snow left on the windshield. Once finished, he' "Someone and someone step into a tent. Someone's mouth"
2	'People suddenly wrap their arms around each other and kiss hungrily. Someone' 'Loose papers fly and a wind blows blankets off the bed. Someone' 'Together, they wander a few steps without taking their eyes off of him. Now in the car as someone drives, someone'
1	'Villagers stare up at the night sky. Flashes of white light' 'The water gets rough as the past through some rocks. Several people' 'We see a title screen. We'
3	'He is shown playing a game with a virtual sumo wrestler. The shorter man' 'The Indian guy keeps his malevolent gaze on someone and looks away. The barmaid' 'We see a man in red talking. A man'
4	'He turns away and covers his face with one hand. Someone' 'With a nod, the man hands it over to the defeated boy. Someone' "On the shop floor, his little helper helps himself to an expensive handbag from a display cabinet, then some women's designer shoes, all of which are detailed on a list. He"
6	'The girl does 2 perfect flips. The girls' 'The man claps his hands together. The man' 'A grey bunny is standing on a bed on a black towel eating something in his hand. As he eats, the bunny'

Evaluation

Metrics

Label	Accuracy
all	0.0885

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("HelgeKn/Swag-multi-class-10")
# Run inference
preds = model("He approaches the object and reads a plaque on its side. Someone")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	6	13.9667	40

Label	Training Sample Count
0	10
1	10
2	10
3	10
4	10
5	10
6	10
7	10
8	10

Training Hyperparameters

• batch_size: (16, 16)
• num_epochs: (2, 2)
• 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
0.0044	1	0.2849	-
0.2222	50	0.1894	-
0.4444	100	0.0847	-
0.6667	150	0.0578	-
0.8889	200	0.0584	-
1.1111	250	0.011	-
1.3333	300	0.0183	-
1.5556	350	0.0106	-
1.7778	400	0.0125	-
2.0	450	0.0071	-

Framework Versions

• Python: 3.9.13
• SetFit: 1.0.1
• Sentence Transformers: 2.2.2
• Transformers: 4.36.0
• PyTorch: 2.1.1+cpu
• Datasets: 2.15.0
• 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}
}