File size: 7,073 Bytes
ce6121c |
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 |
---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: meedan/paraphrase-filipino-mpnet-base-v2
datasets:
- bsen26/eyeR-classification-multi-label-category2
metrics:
- accuracy
widget:
- text: i ordered shake shake fries but they give me just the plain one!! there's
no ketchup or any cutlery!!! i will only give you one star!! tsk poor service
??
- text: The fries were soggy and did not taste good, there was no cutlery, the butter
was already melted when I got the order.
- text: i ordered crispy fillet ala king why no sauce ? and asked for iced tea and
you give pineapple juice ? are you kidding me ? are you even reading some instructions?
- text: Wrong coffee / no ketchup / cold fries. Ugh
- text: They have forgot to put inside the toy i ordered, my child is dispointed because
she's expecting the pikachu toy please fix this !!
pipeline_tag: text-classification
inference: false
model-index:
- name: SetFit with meedan/paraphrase-filipino-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: bsen26/eyeR-classification-multi-label-category2
type: bsen26/eyeR-classification-multi-label-category2
split: test
metrics:
- type: accuracy
value: 0.5407407407407407
name: Accuracy
---
# SetFit with meedan/paraphrase-filipino-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [bsen26/eyeR-classification-multi-label-category2](https://huggingface.co/datasets/bsen26/eyeR-classification-multi-label-category2) dataset that can be used for Text Classification. This SetFit model uses [meedan/paraphrase-filipino-mpnet-base-v2](https://huggingface.co/meedan/paraphrase-filipino-mpnet-base-v2) as the Sentence Transformer embedding model. A OneVsRestClassifier 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:** [meedan/paraphrase-filipino-mpnet-base-v2](https://huggingface.co/meedan/paraphrase-filipino-mpnet-base-v2)
- **Classification head:** a OneVsRestClassifier instance
- **Maximum Sequence Length:** 128 tokens
<!-- - **Number of Classes:** Unknown -->
- **Training Dataset:** [bsen26/eyeR-classification-multi-label-category2](https://huggingface.co/datasets/bsen26/eyeR-classification-multi-label-category2)
<!-- - **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)
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.5407 |
## 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("bsen26/eyeR-category2-multilabel")
# Run inference
preds = model("Wrong coffee / no ketchup / cold fries. Ugh")
```
<!--
### 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 | 1 | 18.3958 | 41 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0010 | 1 | 0.0919 | - |
| 0.0521 | 50 | 0.1443 | - |
| 0.1042 | 100 | 0.0682 | - |
| 0.1562 | 150 | 0.1043 | - |
| 0.2083 | 200 | 0.0653 | - |
| 0.2604 | 250 | 0.0136 | - |
| 0.3125 | 300 | 0.0025 | - |
| 0.3646 | 350 | 0.0195 | - |
| 0.4167 | 400 | 0.0073 | - |
| 0.4688 | 450 | 0.0115 | - |
| 0.5208 | 500 | 0.0045 | - |
| 0.5729 | 550 | 0.0052 | - |
| 0.625 | 600 | 0.0091 | - |
| 0.6771 | 650 | 0.0037 | - |
| 0.7292 | 700 | 0.0027 | - |
| 0.7812 | 750 | 0.0058 | - |
| 0.8333 | 800 | 0.0118 | - |
| 0.8854 | 850 | 0.0025 | - |
| 0.9375 | 900 | 0.0005 | - |
| 0.9896 | 950 | 0.0085 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.2
- PyTorch: 2.2.1+cu121
- Datasets: 2.19.1
- Tokenizers: 0.19.1
## 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.*
--> |