metadata
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget: []
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: Hcompany/NeoMME-260M-Retriever-ST-dense
SetFit with Hcompany/NeoMME-260M-Retriever-ST-dense
This is a SetFit model that can be used for Text Classification. This SetFit model uses Hcompany/NeoMME-260M-Retriever-ST-dense as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
- Fine-tuning a Sentence Transformer with contrastive learning.
- Training a classification head with features from the fine-tuned Sentence Transformer.
Model Details
Model Description
- Model Type: SetFit
- Sentence Transformer body: Hcompany/NeoMME-260M-Retriever-ST-dense
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 16384 tokens
- Number of Classes: 6 classes
Model Sources
- Repository: SetFit on GitHub
- Paper: Efficient Few-Shot Learning Without Prompts
- Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts
Model Labels
| Label | Examples |
|---|---|
| instruction_reference |
|
| news_promotion |
|
| exercise_assessment |
|
| administration_policy |
|
| research_analysis |
|
| other |
|
Uses
Direct Use for Inference
First install the SetFit library:
pip install setfit
Then you can load this model and run inference.
from PIL import Image
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("oneryalcin/finepdfs-purpose-setfit-neomme-v2")
# Run inference on images
preds = model([Image.open("example.png")])
Training Details
Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 3
- 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
Training Results
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.0014 | 1 | 0.2760 | - |
| 0.0686 | 50 | 0.2481 | - |
| 0.1372 | 100 | 0.2100 | - |
| 0.2058 | 150 | 0.1900 | - |
| 0.2743 | 200 | 0.1748 | - |
| 0.3429 | 250 | 0.1622 | - |
| 0.4115 | 300 | 0.1531 | - |
| 0.4801 | 350 | 0.1388 | - |
| 0.5487 | 400 | 0.1345 | - |
| 0.6173 | 450 | 0.1241 | - |
| 0.6859 | 500 | 0.1158 | - |
| 0.7545 | 550 | 0.1059 | - |
| 0.8230 | 600 | 0.0968 | - |
| 0.8916 | 650 | 0.0840 | - |
| 0.9602 | 700 | 0.0874 | - |
Framework Versions
- Python: 3.12.10
- SetFit: 1.3.0.dev0
- Sentence Transformers: 6.0.1
- Transformers: 5.17.0
- PyTorch: 2.14.0+cu130
- Datasets: 5.0.1
- Tokenizers: 0.23.2
Citation
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}
}
Evaluation (oneryalcin/finepdfs-purpose-pages-v2, split test)
accuracy 0.681, macro F1 0.638 on 702 images; 800 training images per class; body Hcompany/NeoMME-260M-Retriever-ST-dense, task document; trained in 1056s on cuda.
precision recall f1-score support
administration_policy 0.59 0.70 0.64 67
exercise_assessment 0.71 0.81 0.76 118
instruction_reference 0.66 0.61 0.63 220
news_promotion 0.63 0.70 0.66 132
other 0.47 0.29 0.36 24
research_analysis 0.83 0.72 0.77 141
accuracy 0.68 702
macro avg 0.65 0.64 0.64 702
weighted avg 0.68 0.68 0.68 702