metadata
base_model: sentence-transformers/paraphrase-mpnet-base-v2
datasets:
- fancyzhx/ag_news
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: >-
Google gets a bounce, ends its first day up 18 percent Shares of Google
leaped \$15.34, or 18 percent, to \$100.34 on the Nasdaq exchange
yesterday in an opening day of trading that harkened back to the wild
run-ups of the dot-com era.
- text: >-
Jobs Data Hold Promise of Stronger Growth (AP) AP - Employers stepped up
hiring in August, expanding payrolls by 144,000 and lowering the
unemployment rate marginally to 5.4 percent. While the figures didn't
amount to a national job fair, analysts said, they did hold out the
promise of stronger growth following the summer lull.
- text: >-
Canadians in Southeast Asia at risk from terrorist groups, report cautions
(Canadian Press) Canadian Press - OTTAWA (CP) - Islamic extremists with
links to Osama bin Laden's al-Qaida network pose a threat to Canadians
living in Southeast Asia, warns a newly obtained intelligence report.
- text: >-
Cricket telecast: Opportunity for lost bidders EVEN as Zee Telefilms
chalks out its strategy for the cricket rights, legal experts are of the
opinion that the Bombay High Court #39;s suggestion for a fresh round of
bidding could open options for the other broadcasters who had lost out in
the first round.
- text: >-
UN Security Council to Witness Sudan Peace Pledge NAIROBI (Reuters) - The
Sudanese government and its southern rebel opponents have agreed to sign
a pledge in the Kenyan capital on Friday to formally end a brutal
21-year-old civil war, with U.N. Security Council ambassadors as
witnesses.
inference: true
model-index:
- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: fancyzhx/ag_news
type: fancyzhx/ag_news
split: test
metrics:
- type: accuracy
value: 0.7647368421052632
name: Accuracy
SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a SetFit model trained on the fancyzhx/ag_news dataset that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 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: sentence-transformers/paraphrase-mpnet-base-v2
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 512 tokens
- Number of Classes: 4 classes
- Training Dataset: fancyzhx/ag_news
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 |
|---|---|
| Sci/Tech |
|
| Business |
|
| World |
|
| Sports |
|
Evaluation
Metrics
| Label | Accuracy |
|---|---|
| all | 0.7647 |
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("setfit_model_id")
# Run inference
preds = model("Google gets a bounce, ends its first day up 18 percent Shares of Google leaped \$15.34, or 18 percent, to \$100.34 on the Nasdaq exchange yesterday in an opening day of trading that harkened back to the wild run-ups of the dot-com era.")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 14 | 39.5694 | 62 |
| Label | Training Sample Count |
|---|---|
| World | 28 |
| Sports | 16 |
| Business | 18 |
| Sci/Tech | 10 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (5, 5)
- 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: True
Training Results
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.0043 | 1 | 0.4093 | - |
| 0.2146 | 50 | 0.2095 | - |
| 0.4292 | 100 | 0.0329 | - |
| 0.6438 | 150 | 0.0008 | - |
| 0.8584 | 200 | 0.0002 | - |
| 1.0 | 233 | - | 0.1542 |
| 1.0730 | 250 | 0.0002 | - |
| 1.2876 | 300 | 0.0001 | - |
| 1.5021 | 350 | 0.0002 | - |
| 1.7167 | 400 | 0.0001 | - |
| 1.9313 | 450 | 0.0001 | - |
| 2.0 | 466 | - | 0.1631 |
| 2.1459 | 500 | 0.0001 | - |
| 2.3605 | 550 | 0.0001 | - |
| 2.5751 | 600 | 0.0001 | - |
| 2.7897 | 650 | 0.0001 | - |
| 3.0 | 699 | - | 0.1648 |
| 3.0043 | 700 | 0.0001 | - |
| 3.2189 | 750 | 0.0001 | - |
| 3.4335 | 800 | 0.0001 | - |
| 3.6481 | 850 | 0.0001 | - |
| 3.8627 | 900 | 0.0001 | - |
| 4.0 | 932 | - | 0.1663 |
| 4.0773 | 950 | 0.0001 | - |
| 4.2918 | 1000 | 0.0 | - |
| 4.5064 | 1050 | 0.0 | - |
| 4.7210 | 1100 | 0.0001 | - |
| 4.9356 | 1150 | 0.0001 | - |
| 5.0 | 1165 | - | 0.1648 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.9.19
- SetFit: 1.1.0.dev0
- Sentence Transformers: 3.0.1
- Transformers: 4.39.0
- PyTorch: 2.4.0
- Datasets: 2.20.0
- 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}
}