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

@@ -0,0 +1,227 @@
+---
+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: Pasivo ahorro y retiro job mejor atención y disponibilidad
+- text: Detractor ahorro y retiro ahorro y retiro premium La atenció telefónica no
+    es buena solo habla una maquina y nunca responde una persona para que le ayude
+    a uno y poder expresar lo que se necesita.
+- text: Detractor gestión patrimonial alto perfil Difícil hacer una gestión por la
+    página. No he podido retirar un saldo porque no llevo carta y no me dicen qué
+    hacer si esa empresa ya no existe
+- text: Detractor ahorro y retiro dynamic top POrque tengo una inversion y hace tiempo
+    que no se contacta mi asesor conmigo, le escribí un correo hace unos días y no
+    me contestó, cambie de celular y no he podido actiualizarlo, estoy buscando como
+    sacar mi dinero de alla, por la mala experiencia.
+- text: Detractor ahorro y retiro pensionado Empecé el proceso en****, y terminé consiguiéndolo
+    en el****, me dejé en el camino más de 250€ en llamadas desde España a Colombia,
+    y cada mes me toca pagar para traer el dinero de mi pensión hasta España porque
+    no hay convenios con los bancos, pierdes en el año más o menos el 80% de una mesada.
+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: 0.8823529411764706
+      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:** 4 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
+|:----------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Construcción de mi pensión personas     | <ul><li>'Promotor ahorro y retiro job Excelente servicio'</li><li>'Promotor ahorro y retiro pensionado Asesoría sobre las modalidades de pensión'</li><li>'Pasivo ahorro y retiro hni job Mejorar la asesoría personalizada según el nivel de ingresos de la persona'</li></ul>                                                                                                                                                                                                             |
+| Solución de ahorro e inversión personas | <ul><li>'Detractor ahorro y retiro job No estoy muy relacionada con el tema'</li><li>'Detractor gestión patrimonial alto perfil Mal servicio por desconocimiento, decisiones unilaterales de Proteccion que afectan a los usuarios, falta de trasparencia en negociones de bonos, falta de soportes aritmeticos y financieros en sus datos a clientes, etc, ect.'</li><li>'Pasivo ahorro y retiro job Asesor pendiente del ahorro sea mucho o poco para  tener más rendimientos.'</li></ul> |
+| Cesantías Personas                      | <ul><li>'Detractor gestión patrimonial alto perfil No me volvieron a enviar información de mi estado de cuenta de las cesantías'</li></ul>                                                                                                                                                                                                                                                                                                                                                  |
+| Construcción de mi pensión empresas     | <ul><li>'Detractor ahorro y retiro ahorro y retiro basic No contamos con acompañamiento.'</li><li>'Promotor grandes empleadores grandes empleadores el reconocimiento y trayectoria'</li><li>'Pasivo ahorro y retiro ahorro y retiro basic Mejor asesoramiento'</li></ul>                                                                                                                                                                                                                   |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.8824   |
+
+## 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("camaosos/journey")
+# Run inference
+preds = model("Pasivo ahorro y retiro job mejor atención y disponibilidad")
+```
+
+<!--
+### 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   | 18.7576 | 169 |
+
+| Label                                   | Training Sample Count |
+|:----------------------------------------|:----------------------|
+| Cesantías Personas                      | 1                     |
+| Construcción de mi pensión empresas     | 8                     |
+| Construcción de mi pensión personas     | 31                    |
+| Solución de ahorro e inversión personas | 26                    |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (4, 4)
+- max_steps: -1
+- 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
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: True
+
+### Training Results
+| Epoch   | Step    | Training Loss | Validation Loss |
+|:-------:|:-------:|:-------------:|:---------------:|
+| 0.0060  | 1       | 0.1959        | -               |
+| 0.3012  | 50      | 0.196         | -               |
+| 0.6024  | 100     | 0.0082        | -               |
+| 0.9036  | 150     | 0.0016        | -               |
+| 1.0     | 166     | -             | 0.1009          |
+| 1.2048  | 200     | 0.0012        | -               |
+| 1.5060  | 250     | 0.0012        | -               |
+| 1.8072  | 300     | 0.0004        | -               |
+| **2.0** | **332** | **-**         | **0.095**       |
+| 2.1084  | 350     | 0.0005        | -               |
+| 2.4096  | 400     | 0.0004        | -               |
+| 2.7108  | 450     | 0.0005        | -               |
+| 3.0     | 498     | -             | 0.1009          |
+| 3.0120  | 500     | 0.0005        | -               |
+| 3.3133  | 550     | 0.0003        | -               |
+| 3.6145  | 600     | 0.0003        | -               |
+| 3.9157  | 650     | 0.0011        | -               |
+| 4.0     | 664     | -             | 0.1002          |
+
+* The bold row denotes the saved checkpoint.
+### Framework Versions
+- Python: 3.10.10
+- SetFit: 1.0.3
+- Sentence Transformers: 3.0.1
+- Transformers: 4.42.3
+- PyTorch: 2.2.1+cu121
+- Datasets: 2.20.0
+- 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 +1,26 @@
+{
+  "_name_or_path": "checkpoints/step_332",
+  "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": "4.42.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 250037
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.42.3",
+    "pytorch": "2.2.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

@@ -0,0 +1,9 @@
+{
+  "normalize_embeddings": false,
+  "labels": [
+    "Cesant\u00edas Personas",
+    "Construcci\u00f3n de mi pensi\u00f3n empresas",
+    "Construcci\u00f3n de mi pensi\u00f3n personas",
+    "Soluci\u00f3n de ahorro e inversi\u00f3n personas"
+  ]
+}

model.safetensors

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modules.json

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+  }
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sentence_bert_config.json

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+  "do_lower_case": false
+}

special_tokens_map.json

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