File size: 9,936 Bytes
6b599de |
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 |
---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: receive upi mandate collect request marg techno project private limit inr
15000.00. log google pay app authorize - axis bank
- text: 'sep-23 statement credit card x6343 total due : inr 5575.55 min due : inr
4811.55 due date : 08-oct-23 . pay www.kotak.com/rd/ccpymt - kotak bank'
- text: '< # > use otp : 8233 login turtlemintpro zck+rfoaqnm'
- text: 'arrive today : please use otp-550041 carefully read instructions secure amazon
package ( id : sptp719784310 )'
- text: a/c xxx51941 credit rs 132.00 12-08-2023 - fd1186130010001148int:132.00 tax:0.00.
a/c balance rs 67022.91 .please call 18002082121 query . ujjivan sfb
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/all-mpnet-base-v2
model-index:
- name: SetFit with sentence-transformers/all-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.9722222222222222
name: Accuracy
---
# SetFit with sentence-transformers/all-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 384 tokens
- **Number of Classes:** 3 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 |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 2 | <ul><li>'validity airtel xstream fiber id 20001896982 expire 04-sep-23 . please recharge rs 589 enjoy uninterrupted service . recharge , click www.airtel.in/5/c_summary ? n=021710937343_dsl . please ignore already pay .'</li><li>'initiate process add a/c . xxxx59 upi app - axis bank'</li><li>'google-pay registration initiate icici bank . do , report bank . card details/otp/cvv secret . disclose anyone .'</li></ul> |
| 0 | <ul><li>'rs 260.00 debit a/c xxxxxx7783 credit krjngm @ oksbi upi ref:325154274303. ? call 18005700 -bob'</li><li>'send rs.400.00 kotak bank ac x4524 7800600122 @ ybl 15-10-23.upi ref 328855774953. , kotak.com/fraud'</li><li>'send rs.400.00 kotak bank ac x4524 7800600122 @ ybl 15-10-23.upi ref 328855774953. , kotak.com/fraud'</li></ul> |
| 1 | <ul><li>'dear bob upi user , account credit inr 50.00 date 2023-08-27 11:41:09 upi ref 360562629741 - bob'</li><li>'receive rs.10000.00 kotak bank ac x4524 mahimagyamlani08 @ okaxis 21-08-23.bal:197,838.14.upi ref:323334598750'</li><li>'update ! inr5.66 credit federal bank account xxxx9374 jupiter app . happy bank !'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9722 |
## 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("vipinbansal179/SetFit_sms_Analyzer5c95292")
# Run inference
preds = model("< # > use otp : 8233 login turtlemintpro zck+rfoaqnm")
```
<!--
### 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 | 20.5357 | 35 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 31 |
| 1 | 28 |
| 2 | 81 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (2, 2)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- 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: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-------:|:-------:|:-------------:|:---------------:|
| 0.0014 | 1 | 0.2939 | - |
| 0.0708 | 50 | 0.1406 | - |
| 0.1416 | 100 | 0.0304 | - |
| 0.2125 | 150 | 0.0078 | - |
| 0.2833 | 200 | 0.0019 | - |
| 0.3541 | 250 | 0.0001 | - |
| 0.4249 | 300 | 0.0003 | - |
| 0.4958 | 350 | 0.0001 | - |
| 0.5666 | 400 | 0.0001 | - |
| 0.6374 | 450 | 0.0001 | - |
| 0.7082 | 500 | 0.0001 | - |
| 0.7790 | 550 | 0.0001 | - |
| 0.8499 | 600 | 0.0002 | - |
| 0.9207 | 650 | 0.0001 | - |
| 0.9915 | 700 | 0.0001 | - |
| **1.0** | **706** | **-** | **0.0168** |
| 1.0623 | 750 | 0.0 | - |
| 1.1331 | 800 | 0.0001 | - |
| 1.2040 | 850 | 0.0001 | - |
| 1.2748 | 900 | 0.0 | - |
| 1.3456 | 950 | 0.0001 | - |
| 1.4164 | 1000 | 0.0 | - |
| 1.4873 | 1050 | 0.0 | - |
| 1.5581 | 1100 | 0.0 | - |
| 1.6289 | 1150 | 0.0 | - |
| 1.6997 | 1200 | 0.0 | - |
| 1.7705 | 1250 | 0.0 | - |
| 1.8414 | 1300 | 0.0002 | - |
| 1.9122 | 1350 | 0.0 | - |
| 1.9830 | 1400 | 0.0 | - |
| 2.0 | 1412 | - | 0.0183 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.16.0
- 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.*
--> |