SetFit with thenlper/gte-large
This is a SetFit model trained on the Ramyashree/Dataset-train500-test100 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 |
create_account |
- "I don't have an online account, what do I have to do to register?"
- 'can you tell me if i can regisger two accounts with a single email address?'
- 'I have no online account, open one, please'
|
edit_account |
- 'how can I modify the information on my profile?'
- 'can u ask an agent how to make changes to my profile?'
- 'I want to update the information on my profile'
|
delete_account |
- 'can I close my account?'
- "I don't want my account, can you delete it?"
- 'how do i close my online account?'
|
switch_account |
- 'I would like to use my other online account , could you switch them, please?'
- 'i want to use my other online account, can u change them?'
- 'how do i change to another account?'
|
get_invoice |
- 'what can you tell me about getting some bills?'
- 'tell me where I can request a bill'
- 'ask an agent if i can obtain some bills'
|
get_refund |
- 'the game was postponed, help me obtain a reimbursement'
- 'the game was postponed, what should I do to obtain a reimbursement?'
- 'the concert was postponed, what should I do to request a reimbursement?'
|
payment_issue |
- 'i have an issue making a payment with card and i want to inform of it, please'
- 'I got an error message when I attempted to pay, but my card was charged anyway and I want to notify it'
- 'I want to notify a problem making a payment, can you help me?'
|
check_refund_policy |
- "I'm interested in your reimbursement polivy"
- 'i wanna see your refund policy, can u help me?'
- 'where do I see your money back policy?'
|
recover_password |
- 'my online account was hacked and I want tyo get it back'
- "I lost my password and I'd like to retrieve it, please"
- 'could u ask an agent how i can reset my password?'
|
track_refund |
- 'tell me if my refund was processed'
- 'I need help checking the status of my refund'
- 'I want to see the status of my refund, can you help me?'
|
Evaluation
Metrics
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("Ramyashree/gte-large-train-test-2")
preds = model("where to change to another online account")
Training Details
Training Set Metrics
Training set |
Min |
Median |
Max |
Word count |
3 |
10.258 |
24 |
Label |
Training Sample Count |
check_refund_policy |
50 |
create_account |
50 |
delete_account |
50 |
edit_account |
50 |
get_invoice |
50 |
get_refund |
50 |
payment_issue |
50 |
recover_password |
50 |
switch_account |
50 |
track_refund |
50 |
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 |
0.0008 |
1 |
0.3248 |
- |
0.04 |
50 |
0.1606 |
- |
0.08 |
100 |
0.0058 |
- |
0.12 |
150 |
0.0047 |
- |
0.16 |
200 |
0.0009 |
- |
0.2 |
250 |
0.0007 |
- |
0.24 |
300 |
0.001 |
- |
0.28 |
350 |
0.0008 |
- |
0.32 |
400 |
0.0005 |
- |
0.36 |
450 |
0.0004 |
- |
0.4 |
500 |
0.0005 |
- |
0.44 |
550 |
0.0005 |
- |
0.48 |
600 |
0.0006 |
- |
0.52 |
650 |
0.0005 |
- |
0.56 |
700 |
0.0004 |
- |
0.6 |
750 |
0.0004 |
- |
0.64 |
800 |
0.0002 |
- |
0.68 |
850 |
0.0003 |
- |
0.72 |
900 |
0.0002 |
- |
0.76 |
950 |
0.0002 |
- |
0.8 |
1000 |
0.0003 |
- |
0.84 |
1050 |
0.0002 |
- |
0.88 |
1100 |
0.0002 |
- |
0.92 |
1150 |
0.0003 |
- |
0.96 |
1200 |
0.0003 |
- |
1.0 |
1250 |
0.0003 |
- |
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}
}