metadata
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
- f1
- precision
- recall
widget:
- text: >-
this is complete crap. i asked exactly five questions and he asked me to
start a new topic, after which my daily limit was reached. why the hell
did you add this restriction that makes the chat process completely
useless??
- text: >-
brand wow, brands product is amazing! its definitely going to
revolutionize product workflows! great job, brand!
- text: >-
why though? whats the harm in using ai as a tool. theres more to ai than
product.
- text: >-
i got invited to participate in an early preview of the new product
ai-powered product in product. as a scientific researcher, i'm finding
this an amazingly powerful tool. this technology is simply revolutionary.
- text: >-
brand is the premier anti-fascist enterprise in the world today buy
product! stop fascism!
pipeline_tag: text-classification
inference: true
base_model: BAAI/bge-large-en-v1.5
model-index:
- name: SetFit with BAAI/bge-large-en-v1.5
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.88
name: Accuracy
- type: f1
value:
- 0.8846153846153847
- 0.6666666666666666
- 0.9222520107238605
name: F1
- type: precision
value:
- 0.8214285714285714
- 0.5
- 1
name: Precision
- type: recall
value:
- 0.9583333333333334
- 1
- 0.8557213930348259
name: Recall
SetFit with BAAI/bge-large-en-v1.5
This is a SetFit model that can be used for Text Classification. This SetFit model uses BAAI/bge-large-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-large-en-v1.5
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 3 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 |
---|---|
peak |
|
neither |
|
pit |
|
Evaluation
Metrics
Label | Accuracy | F1 | Precision | Recall |
---|---|---|---|---|
all | 0.88 | [0.8846153846153847, 0.6666666666666666, 0.9222520107238605] | [0.8214285714285714, 0.5, 1.0] | [0.9583333333333334, 1.0, 0.8557213930348259] |
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("jamiehudson/725_model_v6")
# Run inference
preds = model("why though? whats the harm in using ai as a tool. theres more to ai than product.")
Training Details
Training Set Metrics
Training set | Min | Median | Max |
---|---|---|---|
Word count | 10 | 37.08 | 98 |
Label | Training Sample Count |
---|---|
pit | 50 |
peak | 50 |
neither | 50 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (3, 3)
- 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 |
---|---|---|---|
0.0011 | 1 | 0.2299 | - |
0.0533 | 50 | 0.1604 | - |
0.1066 | 100 | 0.0071 | - |
0.1599 | 150 | 0.0016 | - |
0.2132 | 200 | 0.0012 | - |
0.2665 | 250 | 0.0012 | - |
0.3198 | 300 | 0.0011 | - |
0.3731 | 350 | 0.0009 | - |
0.4264 | 400 | 0.0008 | - |
0.4797 | 450 | 0.0009 | - |
0.5330 | 500 | 0.0007 | - |
0.5864 | 550 | 0.0008 | - |
0.6397 | 600 | 0.0007 | - |
0.6930 | 650 | 0.0007 | - |
0.7463 | 700 | 0.0007 | - |
0.7996 | 750 | 0.0006 | - |
0.8529 | 800 | 0.0006 | - |
0.9062 | 850 | 0.0006 | - |
0.9595 | 900 | 0.0006 | - |
0.0011 | 1 | 0.0006 | - |
0.0533 | 50 | 0.0005 | - |
0.1066 | 100 | 0.0005 | - |
0.1599 | 150 | 0.0005 | - |
0.2132 | 200 | 0.0004 | - |
0.2665 | 250 | 0.0003 | - |
0.3198 | 300 | 0.0004 | - |
0.3731 | 350 | 0.0003 | - |
0.4264 | 400 | 0.0004 | - |
0.4797 | 450 | 0.0004 | - |
0.5330 | 500 | 0.0002 | - |
0.5864 | 550 | 0.0002 | - |
0.6397 | 600 | 0.0002 | - |
0.6930 | 650 | 0.0002 | - |
0.7463 | 700 | 0.0002 | - |
0.7996 | 750 | 0.0003 | - |
0.8529 | 800 | 0.0002 | - |
0.9062 | 850 | 0.0002 | - |
0.9595 | 900 | 0.0001 | - |
1.0128 | 950 | 0.0002 | - |
1.0661 | 1000 | 0.0002 | - |
1.1194 | 1050 | 0.0002 | - |
1.1727 | 1100 | 0.0001 | - |
1.2260 | 1150 | 0.0001 | - |
1.2793 | 1200 | 0.0001 | - |
1.3326 | 1250 | 0.0001 | - |
1.3859 | 1300 | 0.0001 | - |
1.4392 | 1350 | 0.0001 | - |
1.4925 | 1400 | 0.0001 | - |
1.5458 | 1450 | 0.0001 | - |
1.5991 | 1500 | 0.0001 | - |
1.6525 | 1550 | 0.0001 | - |
1.7058 | 1600 | 0.0001 | - |
1.7591 | 1650 | 0.0001 | - |
1.8124 | 1700 | 0.0001 | - |
1.8657 | 1750 | 0.0001 | - |
1.9190 | 1800 | 0.0001 | - |
1.9723 | 1850 | 0.0001 | - |
2.0256 | 1900 | 0.0001 | - |
2.0789 | 1950 | 0.0001 | - |
2.1322 | 2000 | 0.0001 | - |
2.1855 | 2050 | 0.0001 | - |
2.2388 | 2100 | 0.0001 | - |
2.2921 | 2150 | 0.0001 | - |
2.3454 | 2200 | 0.0001 | - |
2.3987 | 2250 | 0.0001 | - |
2.4520 | 2300 | 0.0001 | - |
2.5053 | 2350 | 0.0001 | - |
2.5586 | 2400 | 0.0001 | - |
2.6119 | 2450 | 0.0001 | - |
2.6652 | 2500 | 0.0001 | - |
2.7186 | 2550 | 0.0001 | - |
2.7719 | 2600 | 0.0001 | - |
2.8252 | 2650 | 0.0001 | - |
2.8785 | 2700 | 0.0001 | - |
2.9318 | 2750 | 0.0001 | - |
2.9851 | 2800 | 0.0001 | - |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.5.1
- Transformers: 4.38.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.18.0
- Tokenizers: 0.15.2
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}
}