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--- |
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library_name: setfit |
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tags: |
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- setfit |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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datasets: |
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- Ramyashree/Dataset-train500-test100 |
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metrics: |
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- accuracy |
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widget: |
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- text: I weant to use my other account, switch them |
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- text: I can't remember my password, help me reset it |
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- text: the game was postponed and i wanna get a reimbursement |
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- text: where to change to another online account |
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- text: the show was cancelled, get a reimbursement |
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pipeline_tag: text-classification |
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inference: true |
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base_model: thenlper/gte-large |
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model-index: |
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- name: SetFit with thenlper/gte-large |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: Ramyashree/Dataset-train500-test100 |
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type: Ramyashree/Dataset-train500-test100 |
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split: test |
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metrics: |
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- type: accuracy |
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value: 1.0 |
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name: Accuracy |
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--- |
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# SetFit with thenlper/gte-large |
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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. |
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The model has been trained using an efficient few-shot learning technique that involves: |
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
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2. Training a classification head with features from the fine-tuned Sentence Transformer. |
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## Model Details |
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### Model Description |
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- **Model Type:** SetFit |
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- **Sentence Transformer body:** [thenlper/gte-large](https://huggingface.co/thenlper/gte-large) |
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- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
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- **Maximum Sequence Length:** 512 tokens |
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- **Number of Classes:** 10 classes |
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- **Training Dataset:** [Ramyashree/Dataset-train500-test100](https://huggingface.co/datasets/Ramyashree/Dataset-train500-test100) |
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### Model Sources |
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- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) |
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- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) |
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) |
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### Model Labels |
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| Label | Examples | |
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|:--------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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| 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> | |
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## Evaluation |
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### Metrics |
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| Label | Accuracy | |
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|:--------|:---------| |
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| **all** | 1.0 | |
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## Uses |
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### Direct Use for Inference |
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First install the SetFit library: |
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```bash |
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pip install setfit |
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``` |
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Then you can load this model and run inference. |
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```python |
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from setfit import SetFitModel |
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# Download from the 🤗 Hub |
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model = SetFitModel.from_pretrained("Ramyashree/gte-large-train-test") |
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# Run inference |
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preds = model("where to change to another online account") |
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``` |
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## Training Details |
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### Training Set Metrics |
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| Training set | Min | Median | Max | |
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|:-------------|:----|:-------|:----| |
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| Word count | 3 | 10.258 | 24 | |
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| Label | Training Sample Count | |
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|:--------------------|:----------------------| |
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| check_refund_policy | 50 | |
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| create_account | 50 | |
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| delete_account | 50 | |
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| edit_account | 50 | |
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| get_invoice | 50 | |
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| get_refund | 50 | |
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| payment_issue | 50 | |
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| recover_password | 50 | |
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| switch_account | 50 | |
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| track_refund | 50 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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- num_epochs: (1, 1) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- num_iterations: 20 |
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- body_learning_rate: (2e-05, 2e-05) |
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- head_learning_rate: 2e-05 |
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- loss: CosineSimilarityLoss |
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- distance_metric: cosine_distance |
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- margin: 0.25 |
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- end_to_end: False |
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- use_amp: False |
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- warmup_proportion: 0.1 |
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- seed: 42 |
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- eval_max_steps: -1 |
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- load_best_model_at_end: False |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:------:|:----:|:-------------:|:---------------:| |
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| 0.0008 | 1 | 0.3248 | - | |
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| 0.04 | 50 | 0.1606 | - | |
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| 0.08 | 100 | 0.0058 | - | |
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| 0.12 | 150 | 0.0047 | - | |
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| 0.16 | 200 | 0.0009 | - | |
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| 0.2 | 250 | 0.0007 | - | |
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| 0.24 | 300 | 0.001 | - | |
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| 0.28 | 350 | 0.0008 | - | |
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| 0.32 | 400 | 0.0005 | - | |
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| 0.36 | 450 | 0.0004 | - | |
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| 0.4 | 500 | 0.0005 | - | |
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| 0.44 | 550 | 0.0005 | - | |
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| 0.48 | 600 | 0.0006 | - | |
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| 0.52 | 650 | 0.0005 | - | |
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| 0.56 | 700 | 0.0004 | - | |
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| 0.6 | 750 | 0.0004 | - | |
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| 0.64 | 800 | 0.0002 | - | |
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| 0.68 | 850 | 0.0003 | - | |
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| 0.72 | 900 | 0.0002 | - | |
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| 0.76 | 950 | 0.0002 | - | |
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| 0.8 | 1000 | 0.0003 | - | |
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| 0.84 | 1050 | 0.0002 | - | |
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| 0.88 | 1100 | 0.0002 | - | |
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| 0.92 | 1150 | 0.0003 | - | |
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| 0.96 | 1200 | 0.0003 | - | |
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| 1.0 | 1250 | 0.0003 | - | |
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### Framework Versions |
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- Python: 3.10.12 |
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- SetFit: 1.0.1 |
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- Sentence Transformers: 2.2.2 |
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- Transformers: 4.35.2 |
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- PyTorch: 2.1.0+cu121 |
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- Datasets: 2.15.0 |
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- Tokenizers: 0.15.0 |
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## Citation |
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### BibTeX |
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```bibtex |
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@article{https://doi.org/10.48550/arxiv.2209.11055, |
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doi = {10.48550/ARXIV.2209.11055}, |
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url = {https://arxiv.org/abs/2209.11055}, |
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author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Efficient Few-Shot Learning Without Prompts}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
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