metadata
base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Thank you for your email. Please go ahead and issue. Please invoice in KES
- text: >-
Hi, We are missing some invoices, can you please provide it. 02 - 12 -
2020 AGENT FEE 8900784339018 $21.00 02 - 19 - 2020 AGENT FEE 0017417554160
$22.00 02 - 19 - 2020 AGENT FEE 0017417554143 $22.00 02 - 19 - 2020 AGENT
FEE 8900783383420 $21.00
- text: >-
We need your assistance with the payment for the recent office supplies
order. Let us know once it's done.
- text: >-
I have reported this in November and not only was the trip supposed to be
cancelled and credited I was double billed and the billing has not been
corrected. The total credit should be $667.20. Please confirm this will be
done.
- text: >-
The invoice for the travel arrangements needs to be settled. Kindly
provide payment confirmation.
inference: true
SetFit with sentence-transformers/all-MiniLM-L6-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-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/all-MiniLM-L6-v2
- Classification head: a LogisticRegression instance
- Maximum Sequence Length: 256 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
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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_ALL")
# Run inference
preds = model("Thank you for your email. Please go ahead and issue. Please invoice in KES")
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: (64, 64)
- num_epochs: (2, 2)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 2
- body_learning_rate: (0.0005201181161511404, 0.0005201181161511404)
- head_learning_rate: 0.00021200244124154418
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- max_length: 512
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
Training Results
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.0476 | 1 | 0.2504 | - |
| 1.0 | 21 | - | 0.0691 |
| 2.0 | 42 | - | 0.0445 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0.dev0
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.3.1+cu121
- Datasets: 2.20.0
- Tokenizers: 0.19.1
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}
}