setfit safetensors xlm-roberta sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:intfloat/multilingual-e5-large base_model:finetune:intfloat/multilingual-e5-large model-index region:us
---
base_model: intfloat/multilingual-e5-large
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
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?
inference: true
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) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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>'Since which quarter, the share decline started happening for Colas MS in Cuernavaca?'</li><li>'What is the Market share for Resto in colas MS at each size groups in TT HM Orizaba in 2022'</li><li>'How is the Jumex market share have changed over last three year at quaterly level in TT HM from 2019-2022'</li></ul> |
| 0     | <ul><li>'what are the top brands contributing to share loss for KOF in TT OP Cuernavaca in 2021'</li><li>'Which are the top contributing categoriesXconsumo to the share loss for KOF in Cuernavaca in 2022?'</li><li>'Which packs have driven the shares for the competition in Colas in FY 21-22?'</li></ul>                     |
| 10    | <ul><li>'Which pack segment shows opportunities to drive my market share in NCBS Colas SS?'</li><li>'How can I strategically expand my presence in specific packaging segments to enhance market penetration in CSD Sabores?'</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>'What are the untapped opportunities  in Graffon?'</li><li>'What areas should I focus on to grow my market presence?'</li></ul>                                                                                                                         |
| 7     | <ul><li>'What are the upsizing benefits being offered in Coca-Cola NR Packs? Is there any recommendation to improve it?'</li><li>'Is Fanta a premium brand? How premium are its offerings as compared to other brands in Sabores?'</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>'List headroom opportunities for AGUA'</li></ul>                                                                                                               |
| 11    | <ul><li>'How to win in the prioritized pack segments in Colas MS ?'</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>'What is the price range for CSD in TT HM?'</li><li>'what is PCO share for different price bracket in TT OP 2021'</li><li>'distribution wise, which non csd skus are doing the best?'</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>'What are my pricing opportuities?'</li></ul>                                                                        |
| 1     | <ul><li>"What are the primary factors behind Danone's market share decline in Orizaba for FY21-22?"</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>'What is the industry mix for all KOF brands in TT HM Cuernavaca in 2022'</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_12_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   | 13.7632 | 32  |

| 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    | 0.3488        | -               |
| 0.1754 | 50   | 0.1594        | -               |
| 0.3509 | 100  | 0.0872        | -               |
| 0.5263 | 150  | 0.0065        | -               |
| 0.7018 | 200  | 0.0033        | -               |
| 0.8772 | 250  | 0.0018        | -               |
| 1.0526 | 300  | 0.001         | -               |
| 1.2281 | 350  | 0.0008        | -               |
| 1.4035 | 400  | 0.0008        | -               |
| 1.5789 | 450  | 0.0006        | -               |
| 1.7544 | 500  | 0.0005        | -               |
| 1.9298 | 550  | 0.0005        | -               |
| 2.1053 | 600  | 0.0005        | -               |
| 2.2807 | 650  | 0.0004        | -               |
| 2.4561 | 700  | 0.0003        | -               |
| 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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