File size: 10,397 Bytes
ddd3d2f |
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 |
---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: Someone comes out of the shack and shoves one of the kids to the ground. He
- text: He approaches the object and reads a plaque on its side. Someone
- text: Later at someone's family farm, someone sees the lights on in the hangar.
Someone
- text: Someone stands looking over some of the old photographs as someone goes through
the mess on the desk. Someone
- text: Snow blows around a city of towering crystalline structures. A warrior
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-mpnet-base-v2
model-index:
- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.16557377049180327
name: Accuracy
---
# SetFit with sentence-transformers/paraphrase-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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 9 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>'The man claps his hands together. The man'</li><li>'Emerging in open water, he does a breaststroke toward the murky. He'</li><li>'The girl does 2 perfect flips. The girls'</li></ul> |
| 3 | <ul><li>'The younger insurance rep solemnly faces his partner. The older man'</li><li>'He grabs her hair and pulls her head back. She'</li><li>'A kid in blue shorts is vacuuming the floor. A kid in a red shirt'</li></ul> |
| 2 | <ul><li>'In slow motion, both the Russians and Americans celebrate. Someone'</li><li>'Through a window, we watch someone raise his teacup to his companions. At home, someone'</li><li>'As our view retracts through the star map a holographic line sets out from the gunner chair and targets hologram of the planet earth. She'</li></ul> |
| 4 | <ul><li>"The waiter refills someone's glass. Someone"</li><li>"He finds someone's records in a box. Someone"</li><li>"Bloodstains spread over someone's white shirt. Someone"</li></ul> |
| 7 | <ul><li>'Now, someone stands below an overcast sky. Strands of his greasy black hair'</li><li>'Someone turns at the sound of the distant horns. 6000 horsemen, lead by people,'</li><li>'Someone points his wand upwards. High above, red sparks'</li></ul> |
| 5 | <ul><li>'Now in the eating quarters, someone faces a husky, larged - nosed cook. The cook'</li><li>'A logo for a sports even is shown. There'</li><li>'Someone stirs the cookie dough in a bowl. The dough'</li></ul> |
| 0 | <ul><li>'He shows a water bottle he has along with a brush, and uses the brush to remove snow from the dash window of a car and the water to remove any excess snow left on the windshield. Once finished, he'</li><li>"Someone and someone step into a tent. Someone's mouth"</li><li>'Someone steps outside and opens an umbrella. Someone halts,'</li></ul> |
| 8 | <ul><li>'Someone peers out from the cabin. As she emerges, someone'</li><li>'He gently tries to pull up and then reel the fishing line out of the hole. He'</li><li>'A woman smiles at the camera. The woman'</li></ul> |
| 1 | <ul><li>'We see a title screen. We'</li><li>'A lot of people are sitting on terraces in a big field and people is walking in the entrance of a big stadium. men'</li><li>'We see the finished painting and a line of paints. We then'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.1656 |
## 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("HelgeKn/Swag-multi-class-8")
# Run inference
preds = model("He approaches the object and reads a plaque on its side. Someone")
```
<!--
### 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 | 6 | 14.0833 | 40 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 8 |
| 1 | 8 |
| 2 | 8 |
| 3 | 8 |
| 4 | 8 |
| 5 | 8 |
| 6 | 8 |
| 7 | 8 |
| 8 | 8 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (2, 2)
- 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0056 | 1 | 0.2013 | - |
| 0.2778 | 50 | 0.1955 | - |
| 0.5556 | 100 | 0.0693 | - |
| 0.8333 | 150 | 0.0166 | - |
| 1.1111 | 200 | 0.0369 | - |
| 1.3889 | 250 | 0.0149 | - |
| 1.6667 | 300 | 0.0095 | - |
| 1.9444 | 350 | 0.0238 | - |
### Framework Versions
- Python: 3.9.13
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.36.0
- PyTorch: 2.1.1+cpu
- 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.*
--> |