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                                                                                                                                                                                                                                                                                                                                                                                            |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0     | <ul><li>'what are the top brands contributing to share gain for Jumex in Cuernavaca in 2022'</li><li>'Apart from Jugos + NÃ©ctares, 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?")
```

<!--
### Downstream Use

*List how someone could finetune this model on their own dataset.*
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->