vgarg commited on
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Add SetFit model

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README.md ADDED
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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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+ base_model: intfloat/multilingual-e5-large
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+ metrics:
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+ - accuracy
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+ widget:
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+ - text: What promotions in RTEC have shown declining effectiveness and can be discontinued?
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+ - text: What are my priority brands in RTEC to get positive Lift Change in 2022?
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+ - text: What would be the expected incremental volume lift if the discount on Brand
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+ Zucaritas is raised by 5%?
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+ - text: Which promotion types are better for low discounts for Zucaritas ?
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+ - text: Which Promotions contributred the most ROI Change between 2022 and 2023?
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+ pipeline_tag: text-classification
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+ inference: true
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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: 1.0
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+ name: Accuracy
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+ ---
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+
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+ # SetFit with intfloat/multilingual-e5-large
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+
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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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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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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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+
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+ ## Model Details
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+
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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:** 7 classes
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+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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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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+
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+ ### Model Labels
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+ | Label | Examples |
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+ |:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | 6 | <ul><li>'What kind of promotions generally lead to higher cannibalization?'</li><li>'Which Skus has higher Canninibalization in Natural Juices for 2023?'</li><li>'Which two Product can have simultaneous Promotions?'</li></ul> |
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+ | 2 | <ul><li>'Which Promotions contributred the most lift Change between 2022 and 2023?'</li><li>'Which category x brand has seen major decline in Volume Lift for 2023?'</li><li>'What actions were taken to increase the volume lift for MEGAMART in 2023?'</li></ul> |
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+ | 3 | <ul><li>'What types of promotions within the FIZZY DRINKS category are best suited for offering high discounts?'</li><li>'Which promotion types are better for high discounts in Hydra category for 2022?'</li><li>'Which promotion types in are better for low discounts in FIZZY DRINKS category?'</li></ul> |
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+ | 5 | <ul><li>'How will increasing the discount by 50 percent on Brand BREEZEFIZZ affect the incremental volume lift?'</li><li>'How will the introduction of a 20% discount promotion for Rice Krispies in August affect incremental volume and ROI?'</li><li>'If I raise the discount by 20% on Brand BREEZEFIZZ, what will be the incremental roi?'</li></ul> |
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+ | 0 | <ul><li>'For which category MULTISAVING type of promotions worked best for WorldMart in 2022?'</li><li>'What type of promotions worked best for WorldMart in 2022?'</li><li>'Which subcategory have the highest ROI in 2022?'</li></ul> |
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+ | 4 | <ul><li>'Suggest a better investment strategy to gain better ROI in 2023 for FIZZY DRINKS'</li><li>'Which promotions have scope for higher investment to drive more ROIs in UrbanHub ?'</li><li>'What promotions in FIZZY DRINKS have shown declining effectiveneHydra and can be discontinued?'</li></ul> |
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+ | 1 | <ul><li>'How do the performance metrics of brands in the FIZZY DRINKS category compare to those in HYDRA and NATURAL JUICES concerning ROI change between 2021 to 2022?'</li><li>'Can you identify the specific factors or challenges that contributed to the decline in ROI within ULTRASTORE in 2022 compared to 2021?'</li><li>'What are the main reasons for ROI decline in 2022 compared to 2021?'</li></ul> |
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+
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+ ## Evaluation
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+
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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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+
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+ ## Uses
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+
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+ ### Direct Use for Inference
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+
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+ First install the SetFit library:
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+
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+ ```bash
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+ pip install setfit
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+ ```
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+
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+ Then you can load this model and run inference.
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+
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+ ```python
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+ from setfit import SetFitModel
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+
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+ # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("vgarg/promo_prescriptive_28_04_2024")
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+ # Run inference
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+ preds = model("Which promotion types are better for low discounts for Zucaritas ?")
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+ ```
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+
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+ <!--
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+ ### Downstream Use
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+
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+ *List how someone could finetune this model on their own dataset.*
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+ -->
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+
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+ <!--
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+ ### Out-of-Scope Use
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+
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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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+ -->
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
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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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+ -->
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+
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+ <!--
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+ ### Recommendations
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+
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+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
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+ ## Training Details
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+
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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 | 14.6667 | 27 |
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+
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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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+ | 3 | 10 |
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+ | 4 | 10 |
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+ | 5 | 10 |
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+ | 6 | 9 |
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+
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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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+
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+ ### Training Results
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+ | Epoch | Step | Training Loss | Validation Loss |
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+ |:------:|:----:|:-------------:|:---------------:|
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+ | 0.0058 | 1 | 0.3528 | - |
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+ | 0.2890 | 50 | 0.0485 | - |
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+ | 0.5780 | 100 | 0.0052 | - |
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+ | 0.8671 | 150 | 0.0014 | - |
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+ | 1.1561 | 200 | 0.0006 | - |
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+ | 1.4451 | 250 | 0.0004 | - |
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+ | 1.7341 | 300 | 0.0005 | - |
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+ | 2.0231 | 350 | 0.0004 | - |
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+ | 2.3121 | 400 | 0.0004 | - |
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+ | 2.6012 | 450 | 0.0005 | - |
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+ | 2.8902 | 500 | 0.0004 | - |
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+
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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: 2.7.0
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+ - Transformers: 4.40.0
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+ - PyTorch: 2.2.1+cu121
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+ - Datasets: 2.19.0
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+ - Tokenizers: 0.19.1
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+
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+ ## Citation
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+
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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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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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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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+ -->
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+
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+ <!--
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+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
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