File size: 12,016 Bytes
624bfe5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 |
---
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) -->
<!-- - **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>"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?")
```
<!--
### 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 | 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}
}
```
<!--
## 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.*
--> |