|
--- |
|
library_name: setfit |
|
tags: |
|
- setfit |
|
- sentence-transformers |
|
- text-classification |
|
- generated_from_setfit_trainer |
|
datasets: |
|
- Ramyashree/Dataset-train500-test100 |
|
metrics: |
|
- accuracy |
|
widget: |
|
- text: I weant to use my other account, switch them |
|
- text: I can't remember my password, help me reset it |
|
- text: the game was postponed and i wanna get a reimbursement |
|
- text: where to change to another online account |
|
- text: the show was cancelled, get a reimbursement |
|
pipeline_tag: text-classification |
|
inference: true |
|
base_model: thenlper/gte-large |
|
model-index: |
|
- name: SetFit with thenlper/gte-large |
|
results: |
|
- task: |
|
type: text-classification |
|
name: Text Classification |
|
dataset: |
|
name: Ramyashree/Dataset-train500-test100 |
|
type: Ramyashree/Dataset-train500-test100 |
|
split: test |
|
metrics: |
|
- type: accuracy |
|
value: 1.0 |
|
name: Accuracy |
|
--- |
|
|
|
# SetFit with thenlper/gte-large |
|
|
|
This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [Ramyashree/Dataset-train500-test100](https://huggingface.co/datasets/Ramyashree/Dataset-train500-test100) dataset that can be used for Text Classification. This SetFit model uses [thenlper/gte-large](https://huggingface.co/thenlper/gte-large) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. |
|
|
|
The model has been trained using an efficient few-shot learning technique that involves: |
|
|
|
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
|
2. Training a classification head with features from the fine-tuned Sentence Transformer. |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
- **Model Type:** SetFit |
|
- **Sentence Transformer body:** [thenlper/gte-large](https://huggingface.co/thenlper/gte-large) |
|
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
|
- **Maximum Sequence Length:** 512 tokens |
|
- **Number of Classes:** 10 classes |
|
- **Training Dataset:** [Ramyashree/Dataset-train500-test100](https://huggingface.co/datasets/Ramyashree/Dataset-train500-test100) |
|
<!-- - **Language:** Unknown --> |
|
<!-- - **License:** Unknown --> |
|
|
|
### Model Sources |
|
|
|
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) |
|
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) |
|
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) |
|
|
|
### Model Labels |
|
| Label | Examples | |
|
|:--------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| create_account | <ul><li>"I don't have an online account, what do I have to do to register?"</li><li>'can you tell me if i can regisger two accounts with a single email address?'</li><li>'I have no online account, open one, please'</li></ul> | |
|
| edit_account | <ul><li>'how can I modify the information on my profile?'</li><li>'can u ask an agent how to make changes to my profile?'</li><li>'I want to update the information on my profile'</li></ul> | |
|
| delete_account | <ul><li>'can I close my account?'</li><li>"I don't want my account, can you delete it?"</li><li>'how do i close my online account?'</li></ul> | |
|
| switch_account | <ul><li>'I would like to use my other online account , could you switch them, please?'</li><li>'i want to use my other online account, can u change them?'</li><li>'how do i change to another account?'</li></ul> | |
|
| get_invoice | <ul><li>'what can you tell me about getting some bills?'</li><li>'tell me where I can request a bill'</li><li>'ask an agent if i can obtain some bills'</li></ul> | |
|
| get_refund | <ul><li>'the game was postponed, help me obtain a reimbursement'</li><li>'the game was postponed, what should I do to obtain a reimbursement?'</li><li>'the concert was postponed, what should I do to request a reimbursement?'</li></ul> | |
|
| payment_issue | <ul><li>'i have an issue making a payment with card and i want to inform of it, please'</li><li>'I got an error message when I attempted to pay, but my card was charged anyway and I want to notify it'</li><li>'I want to notify a problem making a payment, can you help me?'</li></ul> | |
|
| check_refund_policy | <ul><li>"I'm interested in your reimbursement polivy"</li><li>'i wanna see your refund policy, can u help me?'</li><li>'where do I see your money back policy?'</li></ul> | |
|
| recover_password | <ul><li>'my online account was hacked and I want tyo get it back'</li><li>"I lost my password and I'd like to retrieve it, please"</li><li>'could u ask an agent how i can reset my password?'</li></ul> | |
|
| track_refund | <ul><li>'tell me if my refund was processed'</li><li>'I need help checking the status of my refund'</li><li>'I want to see the status of my refund, can you help me?'</li></ul> | |
|
|
|
## Evaluation |
|
|
|
### Metrics |
|
| Label | Accuracy | |
|
|:--------|:---------| |
|
| **all** | 1.0 | |
|
|
|
## Uses |
|
|
|
### Direct Use for Inference |
|
|
|
First install the SetFit library: |
|
|
|
```bash |
|
pip install setfit |
|
``` |
|
|
|
Then you can load this model and run inference. |
|
|
|
```python |
|
from setfit import SetFitModel |
|
|
|
# Download from the 🤗 Hub |
|
model = SetFitModel.from_pretrained("Ramyashree/gte-large-train-test-2") |
|
# Run inference |
|
preds = model("where to change to another online account") |
|
``` |
|
|
|
<!-- |
|
### Downstream Use |
|
|
|
*List how someone could finetune this model on their own dataset.* |
|
--> |
|
|
|
<!-- |
|
### Out-of-Scope Use |
|
|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
|
--> |
|
|
|
<!-- |
|
## Bias, Risks and Limitations |
|
|
|
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
|
--> |
|
|
|
<!-- |
|
### Recommendations |
|
|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
|
--> |
|
|
|
## 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 |
|
```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} |
|
} |
|
``` |
|
|
|
<!-- |
|
## Glossary |
|
|
|
*Clearly define terms in order to be accessible across audiences.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Authors |
|
|
|
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
|
--> |
|
|
|
<!-- |
|
## Model Card Contact |
|
|
|
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
|
--> |
