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---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: Why is KOF losing share in Cuernavaca Colas MS RET Original?
- text: Are there any whitespaces in terms of flavor for KOF within CSD Sabores?
- text: What is the trend of KOF"s market share in Colas SS in Cuernavaca from 2019
to YTD 2023?
- text: Which categories have seen the some of the highest Share losses for KOF in
Cuernavaca in 2022?
- text: Which Category X Pack can we see the major share gain and which parameters
are driving the share gain in Cuernavaca?
pipeline_tag: text-classification
inference: true
base_model: intfloat/multilingual-e5-large
model-index:
- name: SetFit with intfloat/multilingual-e5-large
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.25
name: Accuracy
---
# SetFit with intfloat/multilingual-e5-large
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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](https://www.sbert.net) 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:** [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 12 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | <ul><li>'what are the top brands contributing to share gain for Jumex in Cuernavaca in 2022'</li><li>'Apart from Jugos + Néctares, Which are the top contributing categoriesXconsumo to the share loss for Jumex in Orizaba in 2021?'</li><li>'what are the top brands contributing to share gain/loss for KOF in Cuernavaca in2022'</li></ul> |
| 2 | <ul><li>"What is the trend of Danone's market share in Colas SS in Cuernavaca from 2019 to YTD 2023?"</li><li>'Are there any notable shifts in market share for KOF from 2021 to 2022 in TT OP'</li><li>'In which categories KOF has gained most share in TT OP Cuernavaca 2021-2022'</li></ul> |
| 3 | <ul><li>'What is the avg pack size for an offering within the 12.1-15 price bracket for Agua in TT HM, for top KOF brand vs Top competitor brand?'</li><li>'How should KOF gain share in <10 price bracket for NCB in TT HM'</li><li>'What is the price range for CSD in TT HM?'</li></ul> |
| 5 | <ul><li>'What are the untapped opportunities in Graffon?'</li><li>'Help me with new categories to expand in for kof'</li><li>'I am a category manager for agua at kof. Tell me what areas to prioritize for category development'</li></ul> |
| 8 | <ul><li>'Which month and at what price was my share highest'</li><li>'What is the sku range and velocity of KOF in colas'</li><li>'distribution wise, which non csd skus are doing the best?'</li></ul> |
| 11 | <ul><li>'Which levers to prioritize to gain share in Orizaba Colas MS_PET_RET?'</li><li>'Which levers to prioritize to gain share in CSDS?'</li><li>'How can I gain share in NCBS?'</li></ul> |
| 9 | <ul><li>'How much headroom do I have in AGUA'</li><li>'What measures can be taken to maximize headroom in the AGUA market?'</li><li>'Which industries to prioritize to gain share in CSDS in TT HM?'</li></ul> |
| 10 | <ul><li>'Which pack segment shows opportunities to drive my market share in CSDs Colas MS?'</li><li>'What are my priority pack segments to gain share in AGUA Colas SS?'</li><li>'What are my priority pack segments to gain share in NCB Colas SS?'</li></ul> |
| 1 | <ul><li>'Which levers have led the share loss of KOF in Colas in Q4'</li><li>'Why is Resto losing share in Cuernavaca Colas SS RET Original?'</li><li>'What are the main factors contributing to the share gain of Jumex in Still Drinks MS in Orizaba for FY 2022?'</li></ul> |
| 7 | <ul><li>'Is there any PPL correction scope for Valle Frut within TT OP?'</li><li>'Is there a need for PPL correction in the energy drink offerings of Red Bull within the Energy Drinks category?'</li><li>'Is CC a premium brand? How premium are its offerings as compared to other brands in Colas?'</li></ul> |
| 4 | <ul><li>'What is the industry mix of CSDS'</li><li>'How has the csd industry evolved in the last two years?'</li><li>'What is the change in industry mix for coca-cola in TT HM Orizaba in 2021 to 2022'</li></ul> |
| 6 | <ul><li>"I'm interested in launching a new orange flavored offering in new york city in the (TT OP) category. What pack sizes would be most suitable for this market?"</li><li>'I want to launch a new pack type in csd for kof. Tell me what'</li><li>'Within Colas MS, which pack segments are dominated by Red cola in Cuernavaca? Do we have any offerings to compete with the same?'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.25 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("vgarg/fw_identification_model_e5_large_v5_14_12_23")
# Run inference
preds = model("Why is KOF losing share in Cuernavaca Colas MS RET Original?")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 5 | 13.8362 | 33 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 10 |
| 1 | 10 |
| 2 | 10 |
| 3 | 10 |
| 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0034 | 1 | 0.3504 | - |
| 0.1724 | 50 | 0.1647 | - |
| 0.3448 | 100 | 0.0301 | - |
| 0.5172 | 150 | 0.0113 | - |
| 0.6897 | 200 | 0.0026 | - |
| 0.8621 | 250 | 0.0012 | - |
| 1.0345 | 300 | 0.0006 | - |
| 1.2069 | 350 | 0.001 | - |
| 1.3793 | 400 | 0.0007 | - |
| 1.5517 | 450 | 0.0004 | - |
| 1.7241 | 500 | 0.0006 | - |
| 1.8966 | 550 | 0.0005 | - |
| 2.0690 | 600 | 0.0005 | - |
| 2.2414 | 650 | 0.0004 | - |
| 2.4138 | 700 | 0.0003 | - |
| 2.5862 | 750 | 0.0005 | - |
| 2.7586 | 800 | 0.0004 | - |
| 2.9310 | 850 | 0.0003 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu118
- Datasets: 2.15.0
- Tokenizers: 0.15.0
## Citation
### BibTeX
```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}
}
```
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