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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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metrics: |
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- accuracy |
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widget: |
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- text: How does cannibalization within the RTEC category compare to other product |
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categories within the MT channel, influencing the overall volumelift? |
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- text: Can you identify the specific factors or challenges that contributed to the |
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decline in ROI within TT in 2022 compared to 2021? |
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- text: Which Sku cannibalizes higher margin Skus the most for CHEDRAUI channel_name? |
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- text: Can you compare the overall market share and competitive landscape of the |
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category more sensitive to internal cannibalization with other categories? |
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- text: Can you identify the key factors or challenges that have contributed to the |
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ROI decline within TT |
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pipeline_tag: text-classification |
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inference: true |
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base_model: intfloat/multilingual-e5-large |
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model-index: |
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- name: SetFit with intfloat/multilingual-e5-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: Unknown |
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type: unknown |
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split: test |
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metrics: |
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- type: accuracy |
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value: 0.9130434782608695 |
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name: Accuracy |
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--- |
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# SetFit with intfloat/multilingual-e5-large |
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-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:** [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-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:** 3 classes |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/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>'Are there particular factors or trends contributing to the high level of cannibalization for certain brands in the SS category?'</li><li>'How does the degree of cannibalization vary among different SKUs in the RTEC ?'</li><li>'Which Sku cannibalizes higher margin Skus the most?'</li></ul> | |
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| 1 | <ul><li>'Are there plans to enhance promotional activities specific to the MT to mitigate the ROI decline in 2023?'</li><li>'What are the main reasons for ROI decline in 2022 in MT compared to 2021?'</li><li>'Are there changes in consumer preferences or trends that have impacted the Lift of Zucaritas, and how does this compare to other brands like Pringles or Frutela?'</li></ul> | |
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| 0 | <ul><li>'What type of promotions worked best for MT Walmart in 2022?'</li><li>'Which channel has the max ROI and Vol Lift when we run the Promotion for RTEC category?'</li><li>'Which sub_catg_nm have the highest ROI in 2022?'</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** | 0.9130 | |
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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("vgarg/promo_prescriptive_gpt_28_02_2024_v1") |
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# Run inference |
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preds = model("Which Sku cannibalizes higher margin Skus the most for CHEDRAUI channel_name?") |
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``` |
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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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*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 | 7 | 15.8333 | 30 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| 0 | 10 | |
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| 1 | 10 | |
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| 2 | 10 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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- num_epochs: (3, 3) |
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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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| 0.0133 | 1 | 0.3582 | - | |
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| 0.6667 | 50 | 0.0024 | - | |
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| 1.3333 | 100 | 0.0005 | - | |
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| 2.0 | 150 | 0.0004 | - | |
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| 2.6667 | 200 | 0.0002 | - | |
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### Framework Versions |
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- Python: 3.10.12 |
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- SetFit: 1.0.3 |
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- Sentence Transformers: 2.4.0 |
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- Transformers: 4.37.2 |
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- PyTorch: 2.1.0+cu121 |
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- Datasets: 2.17.1 |
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- Tokenizers: 0.15.2 |
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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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