| | --- |
| | library_name: setfit |
| | tags: |
| | - setfit |
| | - sentence-transformers |
| | - text-classification |
| | - generated_from_setfit_trainer |
| | datasets: |
| | - Ramyashree/Dataset-train500-test100withwronginput |
| | 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-test100withwronginput |
| | type: Ramyashree/Dataset-train500-test100withwronginput |
| | split: test |
| | metrics: |
| | - type: accuracy |
| | value: 0.94 |
| | name: Accuracy |
| | --- |
| | |
| | # SetFit with thenlper/gte-large |
| |
|
| | This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [Ramyashree/Dataset-train500-test100withwronginput](https://huggingface.co/datasets/Ramyashree/Dataset-train500-test100withwronginput) 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-test100withwronginput](https://huggingface.co/datasets/Ramyashree/Dataset-train500-test100withwronginput) |
| | <!-- - **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** | 0.94 | |
| | |
| | ## 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-3") |
| | # 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 |
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| | ## Model Card Authors |
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| | *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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| | ## Model Card Contact |
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