Add SetFit model

.gitattributes

@@ -33,3 +33,5 @@ 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
+unigram.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 384,
+  "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

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+---
+base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+library_name: setfit
+metrics:
+- accuracy
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: La app de BBVA está caída, pero se pide paciencia para los depósitos de mañana.
+- text: Tengo un problema con un cajero automático que no me dio el dinero pero sí
+    lo cargó.
+- text: El chip de mi tarjeta de Banorte no funciona, hice una transferencia a mi
+    tarjeta de BBVA y el cajero se quedó con ella, ¿cómo va su sábado?
+- text: Evo Banco reporta un asombroso incremento del 700% en sus depósitos en un
+    año y ahora ofrece la posibilidad de contratar servicios a través de WhatsApp.
+- text: Los nuevos jubilados que acrediten su pensión en BBVA recibirán un regalo
+    de bienvenida de $130.000.
+inference: true
+model-index:
+- name: SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.8028571428571428
+      name: Accuracy
+---
+
+# SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) 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:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 128 tokens
+- **Number of Classes:** 2 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
+|:---------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| discard  | <ul><li>'Marcos informa que se puede realizar el pago de productos de BBVA a través de la Línea BBVA, cajeros automáticos, practicajas, ventanilla de sucursal o diversos comercios.'</li><li>'Se ha celebrado una reunión de alto nivel en 2024 para concretar proyectos de inversión, incluyendo la cooperación con BBVA para la construcción de un portadrones y en el ámbito turístico.'</li><li>'Diversificar es clave para alcanzar nuestros objetivos en inversiones y en la vida, descubre cómo tus decisiones financieras pueden impactar tu vida personal en este artículo.'</li></ul> |
+| relevant | <ul><li>'La persona recibió un correo idéntico al que le explicaron que es una técnica de estafa que simula enviarlo desde su propia cuenta.'</li><li>'La cancelación de la cuenta se ha demorado un mes y al solicitar 200 euros para un viaje, me han cobrado 9 euros de comisión.'</li><li>'El Santander logró récords en beneficios y comisiones a los desfavorecidos bajo el ministerio del consagrado en Consumo, mientras se obsesionan con la apariencia y carecen de dignidad y principios.'</li></ul>                                                                                  |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.8029   |
+
+## 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("saraestevez/setfit-minilm-bank-tweets-processed-400")
+# Run inference
+preds = model("La app de BBVA está caída, pero se pide paciencia para los depósitos de mañana.")
+```
+
+<!--
+### 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   | 1   | 21.6612 | 44  |
+
+| Label    | Training Sample Count |
+|:---------|:----------------------|
+| discard  | 400                   |
+| relevant | 400                   |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (1, 1)
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0005 | 1    | 0.3197        | -               |
+| 0.025  | 50   | 0.2199        | -               |
+| 0.05   | 100  | 0.2876        | -               |
+| 0.075  | 150  | 0.2568        | -               |
+| 0.1    | 200  | 0.196         | -               |
+| 0.125  | 250  | 0.15          | -               |
+| 0.15   | 300  | 0.1475        | -               |
+| 0.175  | 350  | 0.081         | -               |
+| 0.2    | 400  | 0.0441        | -               |
+| 0.225  | 450  | 0.0228        | -               |
+| 0.25   | 500  | 0.0017        | -               |
+| 0.275  | 550  | 0.0083        | -               |
+| 0.3    | 600  | 0.002         | -               |
+| 0.325  | 650  | 0.0013        | -               |
+| 0.35   | 700  | 0.0011        | -               |
+| 0.375  | 750  | 0.0014        | -               |
+| 0.4    | 800  | 0.0004        | -               |
+| 0.425  | 850  | 0.0001        | -               |
+| 0.45   | 900  | 0.0118        | -               |
+| 0.475  | 950  | 0.0002        | -               |
+| 0.5    | 1000 | 0.0012        | -               |
+| 0.525  | 1050 | 0.0003        | -               |
+| 0.55   | 1100 | 0.0001        | -               |
+| 0.575  | 1150 | 0.0003        | -               |
+| 0.6    | 1200 | 0.0001        | -               |
+| 0.625  | 1250 | 0.0001        | -               |
+| 0.65   | 1300 | 0.0001        | -               |
+| 0.675  | 1350 | 0.0002        | -               |
+| 0.7    | 1400 | 0.0197        | -               |
+| 0.725  | 1450 | 0.0002        | -               |
+| 0.75   | 1500 | 0.0002        | -               |
+| 0.775  | 1550 | 0.0001        | -               |
+| 0.8    | 1600 | 0.0004        | -               |
+| 0.825  | 1650 | 0.0001        | -               |
+| 0.85   | 1700 | 0.0001        | -               |
+| 0.875  | 1750 | 0.0001        | -               |
+| 0.9    | 1800 | 0.0001        | -               |
+| 0.925  | 1850 | 0.0001        | -               |
+| 0.95   | 1900 | 0.0158        | -               |
+| 0.975  | 1950 | 0.0001        | -               |
+| 1.0    | 2000 | 0.0001        | -               |
+
+### Framework Versions
+- Python: 3.11.0rc1
+- SetFit: 1.0.3
+- Sentence Transformers: 2.7.0
+- Transformers: 4.39.0
+- PyTorch: 2.3.1+cu121
+- Datasets: 2.19.1
+- Tokenizers: 0.15.2
+
+## 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": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.39.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 250037
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  },
+  "prompts": {},
+  "default_prompt_name": null
+}

config_setfit.json

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+{
+  "labels": [
+    "discard",
+    "relevant"
+  ],
+  "normalize_embeddings": false
+}

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modules.json

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+    "type": "sentence_transformers.models.Pooling"
+  }
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sentence_bert_config.json

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+{
+  "max_seq_length": 128,
+  "do_lower_case": false
+}

special_tokens_map.json

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+}

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