vidhi0206/setfit-paraphrase-mpnet-base-v2-emotion_comp

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 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-mpnet-base-v2
• Classification head: a LogisticRegression instance
• Maximum Sequence Length: 512 tokens
• Number of Classes: 6 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
4	'i feel kind of strange' 'i am feeling pretty restless right now while typing this' 'i feel pressured when people say im going t beat you or whatever'
3	'i feel cranky and annoyed when i dont' 'i feel i did some thing impolite katanya' 'i feel like i should be offended but yawwwn'
5	'i was feeling an act of god at work in my life and it was an amazing feeling' 'i tween sat for my moms boss year old and year old boys this weekend id say babysit but that feels weird considering there were n' 'i started feeling funny and then friday i woke up sick as a dog'
0	'i am from new jersey and this first drink was consumed at a post prom party so i feel it s appropriately lame' 'i feel inside cause life is like a game sometimes then you came around me the walls just disappeared nothing to surround me and keep me from my fears im unprotected see how ive opened up oh youve made me trust cause ive never felt like this before im naked around you does it show' 'i cant believe with that statement being said that im already feeling sexually deprived'
2	'i suddenly feel that this is more than a sweet love song that every girls could sing in front of their boyfriends' 'i really wish i had the courage to drag a blade across my skin i wish i could do it i wish i could see the blood and feel that sweet release as it starts to pour out of my flesh and down my body' 'im sure they feel the more caring loving people in the kids lives the better'
1	'i am not feeling particularly creative' 'id probably go with none on and hope that my date admires a confident girl who feels fine without makeup' 'i woke on saturday feeling a little brighter and was very keen to get outdoors after spending all day friday wallowing in self pity'

Evaluation

Metrics

Label	Accuracy
all	0.448

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("vidhi0206/setfit-paraphrase-mpnet-base-v2-emotion_comp")
# Run inference
preds = model("i feeling so aggravated about all of this")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	4	17.6458	55

Label	Training Sample Count
0	8
1	8
2	8
3	8
4	8
5	8

Training Hyperparameters

• batch_size: (8, 8)
• 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
0.0042	1	0.2835	-
0.2083	50	0.1427	-
0.4167	100	0.0968	-
0.625	150	0.0086	-
0.8333	200	0.0028	-

Framework Versions

• Python: 3.10.12
• SetFit: 1.0.3
• Sentence Transformers: 2.6.1
• Transformers: 4.38.2
• PyTorch: 2.2.1+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}
}