kwang123/roberta-large-setfit-ReqORNot

SetFit with sentence-transformers/all-roberta-large-v1

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-roberta-large-v1 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:

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/all-roberta-large-v1
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: 256 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
1	'The matrix dimensions are fixed, and are the same when displaying departments or categories.' 'The Clarus program shall provide for customer service.' 'NPAC SMS shall identify the originator of any accessible system resources.'
0	'A search pattern is a string w such that w is a sub-string of a string α and α is a string derived from some non- terminal β in the target grammar.' 'Normally only one or two parties are engaged in operation and maintenance of the wind turbine(s), typically the owner and the operation and maintenance organisation, which in some cases is one and the same.' 'TASE-2 (ICCP) resides on layer 7 in the OSI-model and is an MMS companion standard, that is, the general MMS services have been particularised for telecontrol applications.'

Evaluation

Metrics

Label	Accuracy	Weighted Precision	Weighted Recall	Weighted F1	Macro Precision	Macro Recall	Macro F1
all	0.7621	0.7628	0.7621	0.7622	0.7622	0.7625	0.7620

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("kwang123/roberta-large-setfit-ReqORNot")
# Run inference
preds = model("The visual representation of an SDT or a part of an SDT. ")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	5	21.7708	46

Label	Training Sample Count
0	24
1	24

Training Hyperparameters

• batch_size: (8, 8)
• num_epochs: (10, 10)
• 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
0.0067	1	0.3795	-
0.3333	50	0.298	-
0.6667	100	0.0025	-
1.0	150	0.0002	-
1.3333	200	0.0002	-
1.6667	250	0.0001	-
2.0	300	0.0001	-
2.3333	350	0.0001	-
2.6667	400	0.0001	-
3.0	450	0.0001	-
3.3333	500	0.0	-
3.6667	550	0.0	-
4.0	600	0.0	-
4.3333	650	0.0001	-
4.6667	700	0.0	-
5.0	750	0.0	-
5.3333	800	0.0	-
5.6667	850	0.0	-
6.0	900	0.0	-
6.3333	950	0.0001	-
6.6667	1000	0.0	-
7.0	1050	0.0	-
7.3333	1100	0.0	-
7.6667	1150	0.0	-
8.0	1200	0.0	-
8.3333	1250	0.0	-
8.6667	1300	0.0	-
9.0	1350	0.0	-
9.3333	1400	0.0	-
9.6667	1450	0.0	-
10.0	1500	0.0	-

Framework Versions

• Python: 3.10.12
• SetFit: 1.0.3
• Sentence Transformers: 2.5.1
• Transformers: 4.38.1
• PyTorch: 2.1.0+cu121
• Datasets: 2.18.0
• Tokenizers: 0.15.2

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}
}