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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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- dvilasuero/banking77-topics-setfit |
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metrics: |
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- accuracy |
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widget: |
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- text: I requested a refund, and never received it. What can I do? |
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- text: I have a 1 euro fee on my statement. |
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- text: I would like an account for my children, how do I go about doing this? |
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- text: What do I need to do to transfer money into my account? |
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- text: Which country's currency do you support? |
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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: dvilasuero/banking77-topics-setfit |
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type: dvilasuero/banking77-topics-setfit |
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split: test |
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metrics: |
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- type: accuracy |
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value: 0.9230769230769231 |
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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 [dvilasuero/banking77-topics-setfit](https://huggingface.co/datasets/dvilasuero/banking77-topics-setfit) 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:** 8 classes |
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- **Training Dataset:** [dvilasuero/banking77-topics-setfit](https://huggingface.co/datasets/dvilasuero/banking77-topics-setfit) |
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<!-- - **Language:** Unknown --> |
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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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| 2 | <ul><li>'The money I transferred does not show in the balance.'</li><li>'I was wondering how I could have two charges for the same item happen more than once in a 7 day period. Is there anyway I could get this corrected asap.'</li><li>'What is the source of my available funds?'</li></ul> | |
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| 0 | <ul><li>'Do you support the EU?'</li><li>"Can you freeze my account? I just saw there are transactions on my account that I don't recognize. How can I fix this?"</li><li>'Please close my account. I am unsatisfied with your service.'</li></ul> | |
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| 5 | <ul><li>'Are you able to unblock my pin?'</li><li>'I can not find my card pin.'</li><li>'If I need a PIN for my card, where is it located?'</li></ul> | |
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| 1 | <ul><li>"I can't get money out of the ATM"</li><li>'Where can I use this card at an ATM?'</li><li>'Can I use my card at any ATMs?'</li></ul> | |
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| 3 | <ul><li>'Can I get cash with this card anywhere?'</li><li>'Can you please show me where I can find the location to link my card?'</li><li>'Am I able to get a card in EU?'</li></ul> | |
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| 6 | <ul><li>'My friends want to top up my account'</li><li>'Can I be topped up once I hit a certain balance?'</li><li>'Can you tell me why my top up was reverted?'</li></ul> | |
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| 7 | <ul><li>'How do I send my account money through transfer?'</li><li>'How do I transfer money to my account?'</li><li>'How can I transfer money from an outside bank?'</li></ul> | |
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| 4 | <ul><li>'Do you work with all fiat currencies?'</li><li>'Can I exchange to EUR?'</li><li>'Is my country supported'</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** | 0.9231 | |
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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("HarshalBhg/gte-large-setfit-train-test2") |
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# Run inference |
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preds = model("I have a 1 euro fee on my statement.") |
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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 | 4 | 10.5833 | 40 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 10 | |
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| 1 | 19 | |
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| 2 | 28 | |
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| 3 | 36 | |
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| 4 | 13 | |
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| 5 | 14 | |
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| 6 | 15 | |
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| 7 | 21 | |
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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, 1e-05) |
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- head_learning_rate: 0.01 |
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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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| 0.0026 | 1 | 0.3183 | - | |
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| 0.1282 | 50 | 0.0614 | - | |
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| 0.2564 | 100 | 0.0044 | - | |
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| 0.3846 | 150 | 0.001 | - | |
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| 0.5128 | 200 | 0.0008 | - | |
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| 0.6410 | 250 | 0.001 | - | |
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| 0.7692 | 300 | 0.0006 | - | |
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| 0.8974 | 350 | 0.0012 | - | |
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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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