SetFit with intfloat/multilingual-e5-large
This is a SetFit model that can be used for Text Classification. This SetFit model uses intfloat/multilingual-e5-large 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:
- Fine-tuning a Sentence Transformer with contrastive learning.
- Training a classification head with features from the fine-tuned Sentence Transformer.
Model Details
Model Description
Model Sources
Model Labels
Label |
Examples |
6 |
- 'Are there any major whitespace opportunity in terms of Categories x Pack Segments in Cuernavaca?'
- 'In Colas MS which packsegment is not dominated by KOF in TT HM Orizaba 2022? At what price point we can launch an offering'
- 'I want to launch a new pack type in csd for kof. Tell me what'
|
2 |
- "Do any seasonal patterns exist in Jumex's share change in Orizaba?"
- 'What is the Market share for Resto in colas MS at each size groups in TT HM Orizaba in 2022'
- 'Which categories have seen the some of the highest Share losses for KOF in Cuernavaca in FY22-21?'
|
0 |
- 'Which packs have driven the shares for the competition in Colas in FY 21-22?'
- 'Apart from Jugos + Néctares, Which are the top contributing categoriesXconsumo to the share loss for Jumex in Orizaba in 2021?'
- 'which pack segment is contributing most to share change for Resto in Orizaba NCBs in 2022'
|
10 |
- 'Which pack segment shows opportunities to drive my market share in NCBS Colas SS?'
- 'What are my priority pack segments to gain share in NCB Colas SS?'
- 'What are my priority pack segments to gain share in AGUA Colas SS?'
|
5 |
- 'Where should I play in terms\xa0of flavor in Sabores SS?'
- 'I want to launch flavored water in onion flavor for kof.'
- 'What areas should I focus on to grow my market presence?'
|
7 |
- 'Is Fanta a premium brand? How premium are its offerings as compared to other brands in Sabores?'
- "Is there potential for PPL correction in the packaging and pricing strategy of Tropicana's fruit juice offerings within the Juice category?"
- 'Is there an opportunity to premiumize any offerings for coca-cola?'
|
9 |
- 'Which industries to prioritize to gain share in AGUA in Cuernavaca?'
- 'What measures can be taken to maximize headroom in the AGUA market?'
- 'How much headroom do I have in CSDS'
|
11 |
- 'How can I gain share in NCBS?'
- 'How should KOF gain share in Colas MS in Cuernavaca? '
- 'How can I gain share in CSD Colas MS in Cuernavaca'
|
8 |
- 'Category wise market share'
- 'What is the ND, WD of KOF in colas'
- 'Tell me the top 10 SKUs in colas'
|
3 |
- 'What is the difference in offerings for KOF vs the key competitors in xx price bracket within CSD Colas in TT HM?'
- 'How should KOF gain share in <10 price bracket for NCB in TT HM'
- 'Which price points to play in?'
|
1 |
- 'what factors contributed to share change for agua?'
- 'Why is Resto losing share in Cuernavaca Colas SS RET Original?'
- 'What are the main factors contributing to the share gain of Jumex in Still Drinks MS in Orizaba for FY 2022?'
|
4 |
- 'How has the csd industry evolved in the last two years?'
- 'Tell me the categories to focus on, for driving growth in future'
- 'What is the change in industry mix for coca-cola in TT HM Orizaba in 2021 to 2022'
|
Evaluation
Metrics
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
model = SetFitModel.from_pretrained("vgarg/fw_identification_model_e5_large_v5_14_02_24")
preds = model("Why is KOF losing share in Cuernavaca Colas MS RET Original?")
Training Details
Training Set Metrics
Training set |
Min |
Median |
Max |
Word count |
4 |
13.5351 |
28 |
Label |
Training Sample Count |
0 |
10 |
1 |
10 |
2 |
10 |
3 |
8 |
4 |
10 |
5 |
10 |
6 |
10 |
7 |
10 |
8 |
10 |
9 |
10 |
10 |
10 |
11 |
6 |
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 |
0.0035 |
1 |
0.3481 |
- |
0.1754 |
50 |
0.1442 |
- |
0.3509 |
100 |
0.091 |
- |
0.5263 |
150 |
0.0089 |
- |
0.7018 |
200 |
0.0038 |
- |
0.8772 |
250 |
0.0018 |
- |
1.0526 |
300 |
0.001 |
- |
1.2281 |
350 |
0.0012 |
- |
1.4035 |
400 |
0.0007 |
- |
1.5789 |
450 |
0.0007 |
- |
1.7544 |
500 |
0.0004 |
- |
1.9298 |
550 |
0.0005 |
- |
2.1053 |
600 |
0.0006 |
- |
2.2807 |
650 |
0.0005 |
- |
2.4561 |
700 |
0.0006 |
- |
2.6316 |
750 |
0.0004 |
- |
2.8070 |
800 |
0.0004 |
- |
2.9825 |
850 |
0.0004 |
- |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.3.1
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.17.0
- Tokenizers: 0.15.1
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}
}