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fw_identification_model_e5_large_v5_14_02_24

Text Classification
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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
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<!-- - **Language:** 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 |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 6 | <ul><li>'Are there any major whitespace opportunity in terms of Categories x Pack Segments in Cuernavaca?'</li><li>'In Colas MS which packsegment is not dominated by KOF in TT HM Orizaba 2022? At what price point we can launch an offering'</li><li>'I want to launch a new pack type in csd for kof. Tell me what'</li></ul> |
| 2 | <ul><li>"Do any seasonal patterns exist in Jumex's share change in Orizaba?"</li><li>'What is the Market share for Resto in colas MS at each size groups in TT HM Orizaba in 2022'</li><li>'Which categories have seen the some of the highest Share losses for KOF in Cuernavaca in FY22-21?'</li></ul> |
| 0 | <ul><li>'Which packs have driven the shares for the competition in Colas in FY 21-22?'</li><li>'Apart from Jugos + Néctares, Which are the top contributing categoriesXconsumo to the share loss for Jumex in Orizaba in 2021?'</li><li>'which pack segment is contributing most to share change for Resto in Orizaba NCBs in 2022'</li></ul> |
| 10 | <ul><li>'Which pack segment shows opportunities to drive my market share in NCBS Colas SS?'</li><li>'What are my priority pack segments to gain share in NCB Colas SS?'</li><li>'What are my priority pack segments to gain share in AGUA Colas SS?'</li></ul> |
| 5 | <ul><li>'Where should I play in terms\xa0of flavor in Sabores SS?'</li><li>'I want to launch flavored water in onion flavor for kof.'</li><li>'What areas should I focus on to grow my market presence?'</li></ul> |
| 7 | <ul><li>'Is Fanta a premium brand? How premium are its offerings as compared to other brands in Sabores?'</li><li>"Is there potential for PPL correction in the packaging and pricing strategy of Tropicana's fruit juice offerings within the Juice category?"</li><li>'Is there an opportunity to premiumize any offerings for coca-cola?'</li></ul> |
| 9 | <ul><li>'Which industries to prioritize to gain share in AGUA in Cuernavaca?'</li><li>'What measures can be taken to maximize headroom in the AGUA market?'</li><li>'How much headroom do I have in CSDS'</li></ul> |
| 11 | <ul><li>'How can I gain share in NCBS?'</li><li>'How should KOF gain share in Colas MS in Cuernavaca? '</li><li>'How can I gain share in CSD Colas MS in Cuernavaca'</li></ul> |
| 8 | <ul><li>'Category wise market share'</li><li>'What is the ND, WD of KOF in colas'</li><li>'Tell me the top 10 SKUs in colas'</li></ul> |
| 3 | <ul><li>'What is the difference in offerings for KOF vs the key competitors in xx price bracket within CSD Colas in TT HM?'</li><li>'How should KOF gain share in <10 price bracket for NCB in TT HM'</li><li>'Which price points to play in?'</li></ul> |
| 1 | <ul><li>'what factors contributed to share change for agua?'</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> |
| 4 | <ul><li>'How has the csd industry evolved in the last two years?'</li><li>'Tell me the categories to focus on, for driving growth in future'</li><li>'What is the change in industry mix for coca-cola in TT HM Orizaba in 2021 to 2022'</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_02_24")
# 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 | 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
```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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