metadata
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: >-
We are awaiting payment for the project completed in June. Please confirm
when this will be processed.
- text: Hello, Good morning, would you mind cancelling this rental car?
- text: >-
Kindly book accommodation for Lindelani Mkhize as follows: Establishment:
City Lodge Lynwood Date checked in : 04 October 2023 Time checked in:
19h00pm Date checked out: 06 October 2023 Time checked out: 07h00am
- text: >-
You've been selected for a free energy audit. Click here to schedule your
appointment.
- text: >-
Please can you provide with the invoices for my stays this month as
follows: 1. Premier Splendid Inn Bayshore (07 Aug - 08 Aug) 2. Port
Nolloth Beach Shack (14 Aug - 17 Aug)
metrics:
- silhouette_score
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: sentence-transformers/paraphrase-MiniLM-L6-v2
model-index:
- name: SetFit with sentence-transformers/paraphrase-MiniLM-L6-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: silhouette_score
value: 0.4196937375508804
name: Silhouette_Score
SetFit with sentence-transformers/paraphrase-MiniLM-L6-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-MiniLM-L6-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-MiniLM-L6-v2
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 128 tokens
- Number of Classes: 14 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 |
|---|---|
| 0 |
|
| 1 |
|
| 2 |
|
| 3 |
|
| 4 |
|
| 5 |
|
| 6 |
|
| 7 |
|
| 8 |
|
| 9 |
|
| 10 |
|
| 11 |
|
| 12 |
|
| 13 |
|
Evaluation
Metrics
| Label | Silhouette_Score |
|---|---|
| all | 0.4197 |
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("mann2107/BCMPIIRAB_MiniLM_HTTest")
# Run inference
preds = model("Hello, Good morning, would you mind cancelling this rental car?")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 1 | 25.6577 | 136 |
| Label | Training Sample Count |
|---|---|
| 0 | 24 |
| 1 | 24 |
| 2 | 24 |
| 3 | 24 |
| 4 | 24 |
| 5 | 24 |
| 6 | 24 |
| 7 | 24 |
| 8 | 24 |
| 9 | 24 |
| 10 | 24 |
| 11 | 24 |
| 12 | 24 |
| 13 | 24 |
Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 1
- body_learning_rate: (3e-05, 3e-05)
- head_learning_rate: 3e-05
- loss: MultipleNegativesRankingLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: True
- 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 |
|---|---|---|---|
| 0.0476 | 1 | 5.5459 | - |
Framework Versions
- Python: 3.12.0
- SetFit: 1.2.0.dev0
- Sentence Transformers: 3.2.1
- Transformers: 4.45.2
- PyTorch: 2.5.0+cpu
- Datasets: 3.0.2
- Tokenizers: 0.20.1
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}
}