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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-500-validation |
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metrics: |
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- accuracy |
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widget: |
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- text: i want to know the status of my reimbursement, how do i track it? |
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- text: ask an agent how to modify my profile |
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- text: I want to use my other online account, help me switch them |
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- text: I want information about your money back policy |
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- text: how can I switch to another account? |
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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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--- |
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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-500-validation](https://huggingface.co/datasets/Ramyashree/Dataset-500-validation) 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-500-validation](https://huggingface.co/datasets/Ramyashree/Dataset-500-validation) |
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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>'can i register?'</li><li>'i have no account, what do i have to do?'</li><li>'i watn to know if i can register two profiles with the same email address'</li></ul> | |
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| delete_account | <ul><li>'I changed my mind, what should I do to cancel my profile?'</li><li>'i changed my mind, what do i have to do to delete my account?'</li><li>"I odn't want my user account, how do I delete it?"</li></ul> | |
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| edit_account | <ul><li>'I want to change my profile, how can I do it?'</li><li>'I need help making changes to my profile'</li><li>'can I make changes to my profile?'</li></ul> | |
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| recover_password | <ul><li>'could u ask an agent if i could retrieve my password?'</li><li>'my online account was hacked, how do I recover it?'</li><li>'my account was hacked, can u recover it?'</li></ul> | |
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| track_refund | <ul><li>'can yoy tell me about the status of my reimbursement?'</li><li>'tell me if my reimbursement was processed'</li><li>'I want to view the status of my refund, what can I do?'</li></ul> | |
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| check_refund_policy | <ul><li>'I want to check your reimbursement policy, what can I do?'</li><li>'cam u ask an agent if i can see their money back guarantee?'</li><li>'I want to check your refund policy'</li></ul> | |
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| switch_account | <ul><li>'I weant to use my other account, switch them'</li><li>'ask an agent if i can change to another user account'</li><li>'where to change to another profile'</li></ul> | |
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| payment_issue | <ul><li>'I have a problem when trying to pay for my online order, notify it'</li><li>'could you ask an agent where I can report issues making a payment, please?'</li><li>'ask an agent how i can inform of problems paying'</li></ul> | |
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| get_refund | <ul><li>'the concert was postponed and i want to get a reimbursement'</li><li>'the concert was postponed, help me get a reimbursement'</li><li>'how to get a reimbursement'</li></ul> | |
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| get_invoice | <ul><li>'I want to request some bills, can you tell me how I can do it?'</li><li>'ask an agent how I can request somebills'</li><li>'i want to see a bill'</li></ul> | |
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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-with500records-validate") |
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# Run inference |
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preds = model("how can I switch to another account?") |
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``` |
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### Downstream Use |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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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 | 10.356 | 25 | |
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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 | 0.3184 | - | |
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| 0.04 | 50 | 0.1532 | - | |
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| 0.08 | 100 | 0.0078 | - | |
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| 0.12 | 150 | 0.0124 | - | |
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| 0.16 | 200 | 0.0017 | - | |
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| 0.2 | 250 | 0.0009 | - | |
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| 0.24 | 300 | 0.0008 | - | |
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| 0.28 | 350 | 0.0008 | - | |
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| 0.32 | 400 | 0.0007 | - | |
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| 0.36 | 450 | 0.0008 | - | |
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| 0.4 | 500 | 0.0004 | - | |
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| 0.44 | 550 | 0.0005 | - | |
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| 0.48 | 600 | 0.0004 | - | |
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| 0.52 | 650 | 0.0005 | - | |
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| 0.56 | 700 | 0.0003 | - | |
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| 0.6 | 750 | 0.0004 | - | |
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| 0.64 | 800 | 0.0003 | - | |
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| 0.68 | 850 | 0.0003 | - | |
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| 0.72 | 900 | 0.0003 | - | |
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| 0.76 | 950 | 0.0004 | - | |
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| 0.8 | 1000 | 0.0004 | - | |
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| 0.84 | 1050 | 0.0004 | - | |
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| 0.88 | 1100 | 0.0002 | - | |
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| 0.92 | 1150 | 0.0002 | - | |
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| 0.96 | 1200 | 0.0003 | - | |
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| 1.0 | 1250 | 0.0004 | - | |
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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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