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": 384,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "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

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+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: Solicite um relatório financeiro trimestral via ERP conectado.
+- text: If you save $200 monthly, how much money will you have saved after 18 months?
+- text: Get the stock price history of Tesla for the last month.
+- text: Given a historical archive of economic indicators, build a forecasting model
+    that predicts recessions, incorporating leading, lagging, and coincident indicators
+    with explainable outputs.
+- text: Narrate the experience of a character born without the ability to dream.
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: true
+base_model: ibm-granite/granite-embedding-107m-multilingual
+model-index:
+- name: SetFit with ibm-granite/granite-embedding-107m-multilingual
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.9966555183946488
+      name: Accuracy
+---
+
+# SetFit with ibm-granite/granite-embedding-107m-multilingual
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [ibm-granite/granite-embedding-107m-multilingual](https://huggingface.co/ibm-granite/granite-embedding-107m-multilingual) 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:** [ibm-granite/granite-embedding-107m-multilingual](https://huggingface.co/ibm-granite/granite-embedding-107m-multilingual)
+- **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:** 8 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                                                                                                                                                                                                                                                                                                                                                                                                        |
+|:------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| summarization     | <ul><li>'Resuma um texto acadêmico sobre psicologia do comportamento.'</li><li>'Summarize the timeline and outcomes of a historical event based on multiple eyewitness accounts.'</li><li>'Extract and summarize the key lessons learned from multiple post-project reviews.'</li></ul>                                                                                                                         |
+| general_knowledge | <ul><li>'Qual é a importância da agricultura para a economia brasileira?'</li><li>'Quais são os principais países membros da Organização dos Países Exportadores de Petróleo (OPEP)?'</li><li>'What is the mechanism by which vaccines provide immunity?'</li></ul>                                                                                                                                             |
+| roleplay          | <ul><li>'Personifique um chef pâtissier criando uma sobremesa para um júri exigente.'</li><li>'You are a software tester devising scenarios to uncover bugs in a complex system.'</li><li>'Simule uma reunião de conselho editorial decidindo o rumo de uma grande publicação.'</li></ul>                                                                                                                       |
+| creativity        | <ul><li>'Write a thriller in which the protagonist communicates only through artwork.'</li><li>'Imagine um poema narrativo sobre a relação entre o sertão e a poesia de uma geração esquecida.'</li><li>'Write a story from the perspective of a shadow that gains independence.'</li></ul>                                                                                                                     |
+| complex_reasoning | <ul><li>'Analise as implicações do uso de drones autônomos para entregas em áreas urbanas densas.'</li><li>'Proponha um sistema para avaliação automatizada e justa de currículos em processos seletivos corporativos.'</li><li>'Proponha um modelo para prever o crescimento urbano sustentável considerando variáveis ambientais e sociais.'</li></ul>                                                        |
+| coding            | <ul><li>'Implemente uma função para decompor números inteiros em fatores primos eficientemente para valores grandes.'</li><li>'Create an integration that consumes streaming data from an external message broker and processes events in real-time with backpressure management.'</li><li>'Escreva um algoritmo para encontrar os pontos de articulação (cut vertices) em um grafo não direcionado.'</li></ul> |
+| basic_reasoning   | <ul><li>'Se um carro consome 12 litros de gasolina para 100 km, quantos litros usará para 150 km?'</li><li>'If a ladder leans against a wall forming a 60-degree angle and the ladder length is 10 feet, how high does it reach on the wall?'</li><li>'Quantos centímetros tem 1 metro?'</li></ul>                                                                                                              |
+| tool              | <ul><li>'Fetch comprehensive user reviews and ratings for a mobile app across platforms.'</li><li>'Analyze sentiment of a tweet and classify it as positive, neutral, or negative.'</li><li>'Retrieve country-wise COVID-19 vaccination rates from an authoritative source.'</li></ul>                                                                                                                          |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.9967   |
+
+## 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("cnmoro/prompt-router")
+# Run inference
+preds = model("Get the stock price history of Tesla for the last month.")
+```
+
+<!--
+### 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   | 5   | 13.6792 | 38  |
+
+| Label             | Training Sample Count |
+|:------------------|:----------------------|
+| summarization     | 160                   |
+| tool              | 144                   |
+| general_knowledge | 154                   |
+| roleplay          | 145                   |
+| complex_reasoning | 130                   |
+| creativity        | 164                   |
+| coding            | 152                   |
+| basic_reasoning   | 148                   |
+
+### Training Hyperparameters
+- batch_size: (8, 8)
+- num_epochs: (1, 16)
+- max_steps: 2400
+- sampling_strategy: oversampling
+- body_learning_rate: (2e-05, 1e-05)
+- head_learning_rate: 0.01
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: False
+- use_amp: False
+- warmup_proportion: 0.1
+- l2_weight: 0.01
+- seed: 42
+- evaluation_strategy: steps
+- eval_max_steps: -1
+- load_best_model_at_end: True
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0004 | 1    | 0.1954        | -               |
+| 0.0208 | 50   | 0.2125        | -               |
+| 0.0417 | 100  | 0.2131        | -               |
+| 0.0625 | 150  | 0.2072        | -               |
+| 0.0833 | 200  | 0.2029        | 0.1902          |
+| 0.1042 | 250  | 0.1925        | -               |
+| 0.125  | 300  | 0.1764        | -               |
+| 0.1458 | 350  | 0.1512        | -               |
+| 0.1667 | 400  | 0.1229        | 0.1072          |
+| 0.1875 | 450  | 0.1015        | -               |
+| 0.2083 | 500  | 0.0862        | -               |
+| 0.2292 | 550  | 0.065         | -               |
+| 0.25   | 600  | 0.0505        | 0.0504          |
+| 0.2708 | 650  | 0.0532        | -               |
+| 0.2917 | 700  | 0.0427        | -               |
+| 0.3125 | 750  | 0.0378        | -               |
+| 0.3333 | 800  | 0.0357        | 0.0322          |
+| 0.3542 | 850  | 0.0286        | -               |
+| 0.375  | 900  | 0.0381        | -               |
+| 0.3958 | 950  | 0.0333        | -               |
+| 0.4167 | 1000 | 0.0307        | 0.0235          |
+| 0.4375 | 1050 | 0.0245        | -               |
+| 0.4583 | 1100 | 0.0245        | -               |
+| 0.4792 | 1150 | 0.0217        | -               |
+| 0.5    | 1200 | 0.0193        | 0.0168          |
+| 0.5208 | 1250 | 0.0167        | -               |
+| 0.5417 | 1300 | 0.0158        | -               |
+| 0.5625 | 1350 | 0.02          | -               |
+| 0.5833 | 1400 | 0.0167        | 0.0120          |
+| 0.6042 | 1450 | 0.0176        | -               |
+| 0.625  | 1500 | 0.0159        | -               |
+| 0.6458 | 1550 | 0.0141        | -               |
+| 0.6667 | 1600 | 0.0131        | 0.0094          |
+| 0.6875 | 1650 | 0.0097        | -               |
+| 0.7083 | 1700 | 0.0109        | -               |
+| 0.7292 | 1750 | 0.0126        | -               |
+| 0.75   | 1800 | 0.0115        | 0.0079          |
+| 0.7708 | 1850 | 0.0122        | -               |
+| 0.7917 | 1900 | 0.0104        | -               |
+| 0.8125 | 1950 | 0.0111        | -               |
+| 0.8333 | 2000 | 0.011         | 0.0071          |
+| 0.8542 | 2050 | 0.0095        | -               |
+| 0.875  | 2100 | 0.009         | -               |
+| 0.8958 | 2150 | 0.0107        | -               |
+| 0.9167 | 2200 | 0.0099        | 0.0067          |
+| 0.9375 | 2250 | 0.0084        | -               |
+| 0.9583 | 2300 | 0.0086        | -               |
+| 0.9792 | 2350 | 0.0089        | -               |
+| 1.0    | 2400 | 0.0098        | 0.0066          |
+
+### Framework Versions
+- Python: 3.11.11
+- SetFit: 1.2.0.dev0
+- Sentence Transformers: 4.0.2
+- Transformers: 4.51.3
+- PyTorch: 2.6.0+cu124
+- Datasets: 3.5.0
+- Tokenizers: 0.21.1
+
+## 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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+{
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+    "XLMRobertaModel"
+  ],
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+  "hidden_act": "gelu",
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+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "4.0.2",
+    "transformers": "4.51.3",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

config_setfit.json

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+{
+  "normalize_embeddings": false,
+  "labels": [
+    "summarization",
+    "tool",
+    "general_knowledge",
+    "roleplay",
+    "complex_reasoning",
+    "creativity",
+    "coding",
+    "basic_reasoning"
+  ]
+}

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modules.json

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+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+  "do_lower_case": false
+}

sentencepiece.bpe.model

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special_tokens_map.json

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tokenizer_config.json

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+}