File size: 9,138 Bytes
204a74f |
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 |
---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: avsolatorio/GIST-small-Embedding-v0
metrics:
- accuracy
widget:
- text: In Florida, some military veterans are now eligible for temporary teaching
certificates even if they haven't completed a bachelor's degree.
- text: As the total national income falls, the proportion of it absorbed by government
will rise.
- text: And while local far-right activists appear to have quietly accepted defeat
over Belgrade Pride, a tame and small-scale annual event, the ferocity of their
opposition to EuroPride reveals that social attitudes are not much different from
2001.
- text: 'In return for this extraordinary gift, corporate shareholders owed an implicit
obligation back to society: namely, that corporations ought to consider not only
shareholder interests but broader societal interests when making decisions.'
- text: Nonetheless I believe it falls short for legal and historical reasons that
I lay out in “Woke, Inc”, my book published last year.
pipeline_tag: text-classification
inference: true
model-index:
- name: SetFit with avsolatorio/GIST-small-Embedding-v0
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.844578313253012
name: Accuracy
---
# SetFit with avsolatorio/GIST-small-Embedding-v0
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [avsolatorio/GIST-small-Embedding-v0](https://huggingface.co/avsolatorio/GIST-small-Embedding-v0) 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:** [avsolatorio/GIST-small-Embedding-v0](https://huggingface.co/avsolatorio/GIST-small-Embedding-v0)
- **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:** 2 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 |
|:-----------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| subjective | <ul><li>'Stakeholder capitalism poisons democracy and partisan politics poisons capitalism.'</li><li>'There is yet everywhere a deficit in the public revenue because the shrinkage in everything taxable was so sudden and violent.'</li><li>'Our system of unbridled profit-focused capitalism used to serve as perhaps the most important of those sanctuaries, but no longer.'</li></ul> |
| objective | <ul><li>'But a top buying agent tells me that access to 13 can be gained if you know the right people.'</li><li>'A portion of positive tests around the country is being forwarded to the agency for genetic sequencing, according to a report by CBS News.'</li><li>'asked American Federation of Teachers President Randi Weingarten.'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.8446 |
## 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("setfit_model_id")
# Run inference
preds = model("As the total national income falls, the proportion of it absorbed by government will rise.")
```
<!--
### 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 | 22.9219 | 77 |
| Label | Training Sample Count |
|:-----------|:----------------------|
| objective | 128 |
| subjective | 128 |
### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (1, 1)
- 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: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0010 | 1 | 0.2715 | - |
| 0.0484 | 50 | 0.2469 | - |
| 0.0969 | 100 | 0.2247 | - |
| 0.1453 | 150 | 0.0501 | - |
| 0.1938 | 200 | 0.0039 | - |
| 0.2422 | 250 | 0.0014 | - |
| 0.2907 | 300 | 0.0011 | - |
| 0.3391 | 350 | 0.0014 | - |
| 0.3876 | 400 | 0.001 | - |
| 0.4360 | 450 | 0.0009 | - |
| 0.4845 | 500 | 0.0008 | - |
| 0.5329 | 550 | 0.0008 | - |
| 0.5814 | 600 | 0.0008 | - |
| 0.6298 | 650 | 0.0007 | - |
| 0.6783 | 700 | 0.0007 | - |
| 0.7267 | 750 | 0.0006 | - |
| 0.7752 | 800 | 0.0007 | - |
| 0.8236 | 850 | 0.0006 | - |
| 0.8721 | 900 | 0.0005 | - |
| 0.9205 | 950 | 0.0007 | - |
| 0.9690 | 1000 | 0.0007 | - |
### Framework Versions
- Python: 3.11.9
- SetFit: 1.0.3
- Sentence Transformers: 3.0.0
- Transformers: 4.40.2
- PyTorch: 2.1.2
- 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.*
--> |