metadata
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
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pipeline_tag: text-classification
inference: true
base_model: BAAI/bge-small-en-v1.5
model-index:
- name: SetFit with BAAI/bge-small-en-v1.5
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 1
name: Accuracy
SetFit with BAAI/bge-small-en-v1.5
This is a SetFit model that can be used for Text Classification. This SetFit model uses BAAI/bge-small-en-v1.5 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: BAAI/bge-small-en-v1.5
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 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 |
|---|---|
| 2 |
|
| 4 |
|
| 0 |
|
| 5 |
|
| 3 |
|
| 1 |
|
Evaluation
Metrics
| Label | Accuracy |
|---|---|
| all | 1.0 |
Uses
Direct Use for Inference
First install the SetFit library:
pip install setfit
Then you can load this model and run inference.
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("Gopal2002/setfit_finetune_6class")
# Run inference
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Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 5 | 136.4444 | 763 |
| Label | Training Sample Count |
|---|---|
| 0 | 30 |
| 1 | 24 |
| 2 | 28 |
| 3 | 16 |
| 4 | 24 |
| 5 | 22 |
Training Hyperparameters
- batch_size: (32, 32)
- 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: False
Training Results
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.0019 | 1 | 0.3143 | - |
| 0.0931 | 50 | 0.2117 | - |
| 0.1862 | 100 | 0.1418 | - |
| 0.2793 | 150 | 0.0511 | - |
| 0.3724 | 200 | 0.016 | - |
| 0.4655 | 250 | 0.0087 | - |
| 0.5587 | 300 | 0.0052 | - |
| 0.6518 | 350 | 0.0029 | - |
| 0.7449 | 400 | 0.0031 | - |
| 0.8380 | 450 | 0.0021 | - |
| 0.9311 | 500 | 0.0023 | - |
| 1.0242 | 550 | 0.0016 | - |
| 1.1173 | 600 | 0.0018 | - |
| 1.2104 | 650 | 0.0021 | - |
| 1.3035 | 700 | 0.0018 | - |
| 1.3966 | 750 | 0.0017 | - |
| 1.4898 | 800 | 0.0015 | - |
| 1.5829 | 850 | 0.002 | - |
| 1.6760 | 900 | 0.0013 | - |
| 1.7691 | 950 | 0.0015 | - |
| 1.8622 | 1000 | 0.0018 | - |
| 1.9553 | 1050 | 0.0013 | - |
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: 2.16.1
- Tokenizers: 0.15.0
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}
}