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
+}

README.md

@@ -0,0 +1,236 @@
+---
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+metrics:
+- accuracy
+widget:
+- text: Why is KOF losing share in Cuernavaca Colas MS RET Original?
+- text: Are there any whitespaces in terms of flavor for KOF within CSD Sabores?
+- text: What is the trend of KOF"s market share in Colas SS in Cuernavaca from 2019
+    to YTD 2023?
+- text: Which categories have seen the some of the highest Share losses for KOF in
+    Cuernavaca in 2022?
+- text: Which Category X Pack can we see the major share gain and which parameters
+    are driving the share gain in Cuernavaca?
+pipeline_tag: text-classification
+inference: true
+base_model: intfloat/multilingual-e5-large
+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: 0.25
+      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:** 12 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                                                                                                                                                                                                                                                                                                                                               |
+|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 6     | <ul><li>'Are there any major whitespace opportunity in terms of Categories x Pack Segments in Cuernavaca?'</li><li>'In Colas MS which packsegment is not dominated by KOF in TT HM Orizaba 2022? At what price point we can launch an offering'</li><li>'I want to launch a new pack type in csd for kof. Tell me what'</li></ul>                      |
+| 2     | <ul><li>"Do any seasonal patterns exist in Jumex's share change in Orizaba?"</li><li>'What is the Market share for Resto in colas MS at each size groups in TT HM Orizaba in 2022'</li><li>'Which categories have seen the some of the highest Share losses for KOF in Cuernavaca in FY22-21?'</li></ul>                                               |
+| 0     | <ul><li>'Which packs have driven the shares for the competition in Colas in FY 21-22?'</li><li>'Apart from Jugos + Néctares, Which are the top contributing categoriesXconsumo to the share loss for Jumex in Orizaba in 2021?'</li><li>'which pack segment is contributing most to share change for Resto in Orizaba NCBs in 2022'</li></ul>         |
+| 10    | <ul><li>'Which pack segment shows opportunities to drive my market share in NCBS Colas SS?'</li><li>'What are my priority pack segments to gain share in NCB Colas SS?'</li><li>'What are my priority pack segments to gain share in AGUA Colas SS?'</li></ul>                                                                                         |
+| 5     | <ul><li>'Where should I play in terms\xa0of flavor in Sabores SS?'</li><li>'I want to launch flavored water in onion flavor for kof.'</li><li>'What areas should I focus on to grow my market presence?'</li></ul>                                                                                                                                     |
+| 7     | <ul><li>'Is Fanta a premium brand? How premium are its offerings as compared to other brands in Sabores?'</li><li>"Is there potential for PPL correction in the packaging and pricing strategy of Tropicana's fruit juice offerings within the Juice category?"</li><li>'Is there an opportunity to premiumize any offerings for coca-cola?'</li></ul> |
+| 9     | <ul><li>'Which industries to prioritize to gain share in AGUA in Cuernavaca?'</li><li>'What measures can be taken to maximize headroom in the AGUA market?'</li><li>'How much headroom do I have in CSDS'</li></ul>                                                                                                                                    |
+| 11    | <ul><li>'How can I gain share in NCBS?'</li><li>'How should KOF gain share in Colas MS in Cuernavaca? '</li><li>'How can I gain share in CSD Colas MS in Cuernavaca'</li></ul>                                                                                                                                                                         |
+| 8     | <ul><li>'Category wise market share'</li><li>'What is the ND, WD of KOF in colas'</li><li>'Tell me the top 10 SKUs in colas'</li></ul>                                                                                                                                                                                                                 |
+| 3     | <ul><li>'What is the difference in offerings for KOF vs the key competitors in xx price bracket within CSD Colas in TT HM?'</li><li>'How should KOF gain share in <10 price bracket for NCB in TT HM'</li><li>'Which price points to play in?'</li></ul>                                                                                               |
+| 1     | <ul><li>'what factors contributed to share change for agua?'</li><li>'Why is Resto losing share in Cuernavaca Colas SS RET Original?'</li><li>'What are the main factors contributing to the share gain of Jumex in Still Drinks MS in Orizaba for FY 2022?'</li></ul>                                                                                 |
+| 4     | <ul><li>'How has the csd industry evolved in the last two years?'</li><li>'Tell me the categories to focus on, for driving growth in future'</li><li>'What is the change in industry mix for coca-cola in TT HM Orizaba in 2021 to 2022'</li></ul>                                                                                                     |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.25     |
+
+## 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/fw_identification_model_e5_large_v5_14_02_24")
+# Run inference
+preds = model("Why is KOF losing share in Cuernavaca Colas MS RET Original?")
+```
+
+<!--
+### 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   | 4   | 13.5351 | 28  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 10                    |
+| 1     | 10                    |
+| 2     | 10                    |
+| 3     | 8                     |
+| 4     | 10                    |
+| 5     | 10                    |
+| 6     | 10                    |
+| 7     | 10                    |
+| 8     | 10                    |
+| 9     | 10                    |
+| 10    | 10                    |
+| 11    | 6                     |
+
+### 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0035 | 1    | 0.3481        | -               |
+| 0.1754 | 50   | 0.1442        | -               |
+| 0.3509 | 100  | 0.091         | -               |
+| 0.5263 | 150  | 0.0089        | -               |
+| 0.7018 | 200  | 0.0038        | -               |
+| 0.8772 | 250  | 0.0018        | -               |
+| 1.0526 | 300  | 0.001         | -               |
+| 1.2281 | 350  | 0.0012        | -               |
+| 1.4035 | 400  | 0.0007        | -               |
+| 1.5789 | 450  | 0.0007        | -               |
+| 1.7544 | 500  | 0.0004        | -               |
+| 1.9298 | 550  | 0.0005        | -               |
+| 2.1053 | 600  | 0.0006        | -               |
+| 2.2807 | 650  | 0.0005        | -               |
+| 2.4561 | 700  | 0.0006        | -               |
+| 2.6316 | 750  | 0.0004        | -               |
+| 2.8070 | 800  | 0.0004        | -               |
+| 2.9825 | 850  | 0.0004        | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 2.3.1
+- Transformers: 4.35.2
+- PyTorch: 2.1.0+cu121
+- Datasets: 2.17.0
+- Tokenizers: 0.15.1
+
+## 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

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+{
+  "_name_or_path": "intfloat/multilingual-e5-large",
+  "architectures": [
+    "XLMRobertaModel"
+  ],
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+  "hidden_act": "gelu",
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+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

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+    "sentence_transformers": "2.3.1",
+    "transformers": "4.35.2",
+    "pytorch": "2.1.0+cu121"
+  }
+}

config_setfit.json

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modules.json

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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.json

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tokenizer_config.json

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+}