vgarg/query_type_classifier_13_6_2024

SetFit with sentence-transformers/all-MiniLM-L6-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-MiniLM-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/all-MiniLM-L6-v2
• 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	'what is ROI trend for Fizzy drinks?' 'Are there any notable shifts in market share for KOF from 2021 to 2022 in TT OP' 'Calculate Premiumness Index for KOF in Agua in 2022'
0	'In Colas MS which packsegment is not dominated by KOF in TT HM Orizaba 2022? At what price point we can launch an offering' 'which pack segment is contributing most to share change for Resto in Orizaba NCBs in 2022' 'Help me with new categories to expand in for kof'

Evaluation

Metrics

Label	Accuracy
all	0.9

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("vgarg/query_type_classifier_13_6_2024")
# Run inference
preds = model("How has the csd industry evolved in the last two years?")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	5	13.525	32

Label	Training Sample Count
0	40
1	40

Training Hyperparameters

• batch_size: (16, 16)
• num_epochs: (3, 3)
• 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
0.005	1	0.3157	-
0.25	50	0.1828	-
0.5	100	0.038	-
0.75	150	0.01	-
1.0	200	0.0026	-
1.25	250	0.0018	-
1.5	300	0.0016	-
1.75	350	0.0011	-
2.0	400	0.0008	-
2.25	450	0.0008	-
2.5	500	0.001	-
2.75	550	0.0008	-
3.0	600	0.0006	-

Framework Versions

• Python: 3.12.2
• SetFit: 1.0.3
• Sentence Transformers: 2.6.1
• Transformers: 4.39.3
• PyTorch: 2.2.2+cpu
• 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}
}