deepachalapathi/parasci3_1

SetFit with sentence-transformers/paraphrase-TinyBERT-L6-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-TinyBERT-L6-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:

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-TinyBERT-L6-v2
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: 128 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	'the defacto standard metric in machine translation is bleu---from character representations , we propose to generate vector representations of entire tweets from characters in our tweet2vec model' 'arabic is a highly inflectional language with 85 % of words derived from trilateral roots ( alfedaghi and al-anzi 1989 )---chen et al derive bilingual subtree constraints with auto-parsed source-language sentences' 'labeling of sentence boundaries is a necessary prerequisite for many natural language processing tasks , including part-of-speech tagging and sentence alignment---we have proposed a model for video description which uses neural networks for the entire pipeline from pixels to sentences'
1	'in this paper , we present a comprehensive analysis of the relationship between personal traits and brand preferences---in previous research , in this study , we want to systematically investigate the relationship between a comprehensive set of personal traits and brand preferences' 'the 50-dimensional pre-trained word embeddings are provided by glove , which are fixed during our model training---we use glove vectors with 200 dimensions as pre-trained word embeddings , which are tuned during training' 'we use glove vectors with 100 dimensions trained on wikipedia and gigaword as word embeddings , which we do not optimize during training---we use glove vectors with 100 dimensions trained on wikipedia and gigaword as word embeddings'

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("whateverweird17/parasci3_1")
# Run inference
preds = model("the two baseline methods were implemented using scikit-learn in python---the models were implemented using scikit-learn module")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	27	35.8125	54

Label	Training Sample Count
0	8
1	8

Training Hyperparameters

• batch_size: (8, 8)
• num_epochs: (10, 10)
• max_steps: -1
• sampling_strategy: oversampling
• num_iterations: 10
• 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
0.025	1	0.1715	-
1.25	50	0.0028	-
2.5	100	0.0005	-
3.75	150	0.0002	-
5.0	200	0.0003	-
6.25	250	0.0001	-
7.5	300	0.0002	-
8.75	350	0.0001	-
10.0	400	0.0001	-

Framework Versions

• Python: 3.10.12
• SetFit: 1.0.1
• Sentence Transformers: 2.2.2
• Transformers: 4.33.0
• PyTorch: 2.0.0
• Datasets: 2.16.0
• Tokenizers: 0.13.3

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}
}