Add SetFit model

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 1024,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -0,0 +1,214 @@
+---
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+base_model: intfloat/multilingual-e5-large
+metrics:
+- accuracy
+widget:
+- text: What promotions in RTEC have shown declining effectiveness and can be discontinued?
+- text: What are my priority brands in RTEC to get positive Lift Change in 2022?
+- text: What would be the expected incremental volume lift if the discount on Brand
+    Zucaritas is raised by 5%?
+- text: Which promotion types are better for low discounts for Zucaritas ?
+- text: Which Promotions contributred the most ROI Change between 2022 and 2023?
+pipeline_tag: text-classification
+inference: true
+model-index:
+- name: SetFit with intfloat/multilingual-e5-large
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.9714285714285714
+      name: Accuracy
+---
+
+# SetFit with intfloat/multilingual-e5-large
+
+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.
+
+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:** [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-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:** 6 classes
+<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
+<!-- - **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                                                                                                                                                                                                                                                                                                                                                                                             |
+|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 2     | <ul><li>'Which brand has the highest change in lift  for NATURAL JUICES category in 2022?'</li><li>'What are the main reasons for Lift decline for ULTRASTORE in 2023 compared to 2022?'</li><li>'Why has the overall Lift declined in 2023 in BREEZEFIZZ vs 2022?'</li></ul>                                                                                                                        |
+| 5     | <ul><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><li>'How will increasing the discount by 50 percent on Brand BREEZEFIZZ affect the incremental volume lift?'</li></ul>                                            |
+| 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>'Were there any sku-specific promotions that may have influenced their ROI and contributed to the overall decline?'</li><li>'Which category has contributed the most to ROI change between 2021 to 2022?'</li></ul> |
+| 0     | <ul><li>'How is the promotion efficacy in 2022 compared to 2021 for CHEDRAUI channel?'</li><li>'Which subcategory have the highest ROI in 2022?'</li><li>'Which channel has the max ROI and Vol Lift when we run the Promotion for FIZZY DRINKS category?'</li></ul>                                                                                                                                 |
+| 3     | <ul><li>'Which promotion types are better for high discounts in Hydra category for 2022?'</li><li>'Which promotion types are preferable for high discounts in FIZZY DRINKS for CORN POPS?'</li><li>'Which promotion strategies in FIZZY DRINKS allow for offering substantial discounts while maintaining profitability?'</li></ul>                                                                  |
+| 4     | <ul><li>'Which promotions have scope for higher investment to drive more ROIs in Hydra ?'</li><li>'How can Hydra category investors diversify their investment portfolio to improve ROI?'</li><li>'For FIZZY DRINKS what would be a better investment strategy to gain ROI'</li></ul>                                                                                                                |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.9714   |
+
+## 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("vgarg/promo_prescriptive_gpt_30_04_2024_v1")
+# Run inference
+preds = model("Which promotion types are better for low discounts for Zucaritas ?")
+```
+
+<!--
+### 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   | 8   | 15.1667 | 27  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 10                    |
+| 1     | 10                    |
+| 2     | 10                    |
+| 3     | 10                    |
+| 4     | 10                    |
+| 5     | 10                    |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (3, 3)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 20
+- body_learning_rate: (2e-05, 2e-05)
+- head_learning_rate: 2e-05
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0067 | 1    | 0.3577        | -               |
+| 0.3333 | 50   | 0.04          | -               |
+| 0.6667 | 100  | 0.002         | -               |
+| 1.0    | 150  | 0.0013        | -               |
+| 1.3333 | 200  | 0.0009        | -               |
+| 1.6667 | 250  | 0.0006        | -               |
+| 2.0    | 300  | 0.0006        | -               |
+| 2.3333 | 350  | 0.0004        | -               |
+| 2.6667 | 400  | 0.0006        | -               |
+| 3.0    | 450  | 0.0004        | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 2.7.0
+- Transformers: 4.40.1
+- PyTorch: 2.2.1+cu121
+- Datasets: 2.19.0
+- Tokenizers: 0.19.1
+
+## 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
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,28 @@
+{
+  "_name_or_path": "intfloat/multilingual-e5-large",
+  "architectures": [
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+  ],
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+  "hidden_act": "gelu",
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+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
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+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.40.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.7.0",
+    "transformers": "4.40.1",
+    "pytorch": "2.2.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null
+}

config_setfit.json

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+  "labels": null
+}

model.safetensors

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modules.json

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+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
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sentence_bert_config.json

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+  "do_lower_case": false
+}

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special_tokens_map.json

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tokenizer_config.json

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+}