metadata
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: sentence-transformers/all-MiniLM-L6-v2
metrics:
- accuracy
widget:
- text: The itinerary meets our requirements, please book as proposed.
- text: >-
Please may you kindly send us the invoices for our stay at the Protea
hotel in Cape Town from 20/07/2023 - 22/07/2023. The four confirmation
numbers from the vouchers are as follows: 74733068 74731210 74729566
74727187
- text: >-
Can you please tell me if this Flight ticket for Shaun Connolly was charge
to the LBP travel card.
- text: >-
I am very confused on the itineraries I've received for Michelle Curtin.
Can you please send me an updated itinerary with her actual travel
schedule?
- text: >-
I got a call late Friday afternoon to move our meeting of today. The
rental company was supposed to drop off the car for me at 13:00. Can you
please call and find out if they can deliver it before 10 this morning.
Preferably 9. Sorry for the inconvenience.
pipeline_tag: text-classification
inference: true
model-index:
- name: SetFit with sentence-transformers/all-MiniLM-L6-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.875
name: Accuracy
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: 9 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 |
|
Evaluation
Metrics
| Label | Accuracy |
|---|---|
| all | 0.875 |
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/BCMPIIRABSentSim")
# Run inference
preds = model("The itinerary meets our requirements, please book as proposed.")
Training Details
Training Set Metrics
| Training set | Min | Median | Max |
|---|---|---|---|
| Word count | 1 | 30.4097 | 124 |
| Label | Training Sample Count |
|---|---|
| 0 | 16 |
| 1 | 16 |
| 2 | 16 |
| 3 | 16 |
| 4 | 16 |
| 5 | 16 |
| 6 | 16 |
| 7 | 16 |
| 8 | 16 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- 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 |
|---|---|---|---|
| 0.0009 | 1 | 0.1977 | - |
| 0.0434 | 50 | 0.1642 | - |
| 0.0868 | 100 | 0.1034 | - |
| 0.1302 | 150 | 0.05 | - |
| 0.1736 | 200 | 0.0177 | - |
| 0.2170 | 250 | 0.0128 | - |
| 0.2604 | 300 | 0.0148 | - |
| 0.3038 | 350 | 0.0109 | - |
| 0.3472 | 400 | 0.0059 | - |
| 0.3906 | 450 | 0.004 | - |
| 0.4340 | 500 | 0.0036 | - |
| 0.4774 | 550 | 0.0064 | - |
| 0.5208 | 600 | 0.0042 | - |
| 0.5642 | 650 | 0.002 | - |
| 0.6076 | 700 | 0.0017 | - |
| 0.6510 | 750 | 0.002 | - |
| 0.6944 | 800 | 0.0026 | - |
| 0.7378 | 850 | 0.0019 | - |
| 0.7812 | 900 | 0.0017 | - |
| 0.8247 | 950 | 0.0017 | - |
| 0.8681 | 1000 | 0.0015 | - |
| 0.9115 | 1050 | 0.0009 | - |
| 0.9549 | 1100 | 0.002 | - |
| 0.9983 | 1150 | 0.0008 | - |
| 1.0 | 1152 | - | 0.0732 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.9.16
- SetFit: 1.1.0.dev0
- Sentence Transformers: 2.2.2
- Transformers: 4.21.3
- PyTorch: 1.12.1+cu116
- Datasets: 2.4.0
- Tokenizers: 0.12.1
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}
}