SetFit with thenlper/gte-large
This is a SetFit model trained on the dvilasuero/banking77-topics-setfit dataset that can be used for Text Classification. This SetFit model uses thenlper/gte-large 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 Sources
Model Labels
| Label |
Examples |
| 2 |
- 'The money I transferred does not show in the balance.'
- 'I was wondering how I could have two charges for the same item happen more than once in a 7 day period. Is there anyway I could get this corrected asap.'
- 'What is the source of my available funds?'
|
| 0 |
- 'Do you support the EU?'
- "Can you freeze my account? I just saw there are transactions on my account that I don't recognize. How can I fix this?"
- 'Please close my account. I am unsatisfied with your service.'
|
| 5 |
- 'Are you able to unblock my pin?'
- 'I can not find my card pin.'
- 'If I need a PIN for my card, where is it located?'
|
| 1 |
- "I can't get money out of the ATM"
- 'Where can I use this card at an ATM?'
- 'Can I use my card at any ATMs?'
|
| 3 |
- 'Can I get cash with this card anywhere?'
- 'Can you please show me where I can find the location to link my card?'
- 'Am I able to get a card in EU?'
|
| 6 |
- 'My friends want to top up my account'
- 'Can I be topped up once I hit a certain balance?'
- 'Can you tell me why my top up was reverted?'
|
| 7 |
- 'How do I send my account money through transfer?'
- 'How do I transfer money to my account?'
- 'How can I transfer money from an outside bank?'
|
| 4 |
- 'Do you work with all fiat currencies?'
- 'Can I exchange to EUR?'
- 'Is my country supported'
|
Evaluation
Metrics
| Label |
Accuracy |
| all |
0.9231 |
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
model = SetFitModel.from_pretrained("HarshalBhg/gte-large-setfit-train-test2")
preds = model("I have a 1 euro fee on my statement.")
Training Details
Training Set Metrics
| Training set |
Min |
Median |
Max |
| Word count |
4 |
10.5833 |
40 |
| Label |
Training Sample Count |
| 0 |
10 |
| 1 |
19 |
| 2 |
28 |
| 3 |
36 |
| 4 |
13 |
| 5 |
14 |
| 6 |
15 |
| 7 |
21 |
Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- 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 |
| 0.0026 |
1 |
0.3183 |
- |
| 0.1282 |
50 |
0.0614 |
- |
| 0.2564 |
100 |
0.0044 |
- |
| 0.3846 |
150 |
0.001 |
- |
| 0.5128 |
200 |
0.0008 |
- |
| 0.6410 |
250 |
0.001 |
- |
| 0.7692 |
300 |
0.0006 |
- |
| 0.8974 |
350 |
0.0012 |
- |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.15.0
- Tokenizers: 0.15.0
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}
}
