monentiadev
/

es-input-classifier

@@ -1,206 +1,190 @@
----
-tags:
-- setfit
-- sentence-transformers
-- text-classification
-- generated_from_setfit_trainer
-widget:
-- text: Plasta
-- text: 203 terminada
-- text: habitación 294 limpia
-- text: ¡Hola, cómo va todo!
-- text: Quiero ver el estado de la incidencia que reporté en la Calle Mayor de Triana,
-    25.
-metrics:
-- accuracy
-pipeline_tag: text-classification
-library_name: setfit
-inference: true
-base_model: jaimevera1107/all-MiniLM-L6-v2-similarity-es
----
-# SetFit with jaimevera1107/all-MiniLM-L6-v2-similarity-es
-This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [jaimevera1107/all-MiniLM-L6-v2-similarity-es](https://huggingface.co/jaimevera1107/all-MiniLM-L6-v2-similarity-es) 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:** [jaimevera1107/all-MiniLM-L6-v2-similarity-es](https://huggingface.co/jaimevera1107/all-MiniLM-L6-v2-similarity-es)
-- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
-- **Maximum Sequence Length:** 256 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                                                                                                                                                             |
-|:-------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| request      | <ul><li>'lencería necesaria'</li><li>'material necesario para hoy'</li><li>'terminé la habitación 234'</li></ul>                                                     |
-| conversation | <ul><li>'buena noche'</li><li>'Qué pasa, tío, ¿todo bien?'</li><li>'Buenas, ¿cómo va la cosa?!'</li></ul>                                                            |
-| help         | <ul><li>'ayuda por favor'</li><li>'Ayuda que no sé que puedo hacer'</li><li>'Hola, que puedo hacer'</li></ul>                                                        |
-| censorship   | <ul><li>'Eres un completo inútil, no sirves para nada'</li><li>'Siempre diciendo estupideces, mejor cállate'</li><li>'Tu sola existencia es una vergüenza'</li></ul> |
-## 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("monentiadev/es-input-classifier")
-# Run inference
-preds = model("Plasta")
-```
-<!--
-### 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   | 5.0723 | 38  |
-| Label        | Training Sample Count |
-|:-------------|:----------------------|
-| censorship   | 407                   |
-| conversation | 137                   |
-| help         | 274                   |
-| request      | 552                   |
-### Training Hyperparameters
-- batch_size: (128, 128)
-- num_epochs: (3, 3)
-- max_steps: -1
-- sampling_strategy: oversampling
-- num_iterations: 20
-- 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
-- eval_max_steps: -1
-- load_best_model_at_end: False
-### Training Results
-| Epoch  | Step | Training Loss | Validation Loss |
-|:------:|:----:|:-------------:|:---------------:|
-| 0.0023 | 1    | 0.3161        | -               |
-| 0.1166 | 50   | 0.2857        | -               |
-| 0.2331 | 100  | 0.2158        | -               |
-| 0.3497 | 150  | 0.1581        | -               |
-| 0.4662 | 200  | 0.0878        | -               |
-| 0.5828 | 250  | 0.0299        | -               |
-| 0.6993 | 300  | 0.0124        | -               |
-| 0.8159 | 350  | 0.0083        | -               |
-| 0.9324 | 400  | 0.006         | -               |
-| 1.0490 | 450  | 0.0038        | -               |
-| 1.1655 | 500  | 0.0027        | -               |
-| 1.2821 | 550  | 0.0027        | -               |
-| 1.3986 | 600  | 0.0017        | -               |
-| 1.5152 | 650  | 0.0016        | -               |
-| 1.6317 | 700  | 0.0013        | -               |
-| 1.7483 | 750  | 0.0012        | -               |
-| 1.8648 | 800  | 0.0012        | -               |
-| 1.9814 | 850  | 0.001         | -               |
-| 2.0979 | 900  | 0.001         | -               |
-| 2.2145 | 950  | 0.0011        | -               |
-| 2.3310 | 1000 | 0.0009        | -               |
-| 2.4476 | 1050 | 0.0008        | -               |
-| 2.5641 | 1100 | 0.0009        | -               |
-| 2.6807 | 1150 | 0.0008        | -               |
-| 2.7972 | 1200 | 0.0008        | -               |
-| 2.9138 | 1250 | 0.0007        | -               |
-### Framework Versions
-- Python: 3.10.0
-- SetFit: 1.1.2
-- Sentence Transformers: 5.0.0
-- Transformers: 4.53.1
-- PyTorch: 2.7.1+cu126
-- Datasets: 2.19.2
-- Tokenizers: 0.21.2
-## 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.*
 -->

+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: Plasta
+- text: 203 terminada
+- text: habitación 294 limpia
+- text: ¡Hola, cómo va todo!
+- text: Quiero ver el estado de la incidencia que reporté en la Calle Mayor de Triana.
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: true
+base_model: jaimevera1107/all-MiniLM-L6-v2-similarity-es
+---
+# Input Classifier
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [jaimevera1107/all-MiniLM-L6-v2-similarity-es](https://huggingface.co/jaimevera1107/all-MiniLM-L6-v2-similarity-es) 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:** [jaimevera1107/all-MiniLM-L6-v2-similarity-es](https://huggingface.co/jaimevera1107/all-MiniLM-L6-v2-similarity-es)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 256 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                                                                                                                                                             |
+|:-------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| request      | <ul><li>'lencería necesaria'</li><li>'material necesario para hoy'</li><li>'terminé la habitación 234'</li></ul>                                                     |
+| conversation | <ul><li>'buena noche'</li><li>'Qué pasa, tío, ¿todo bien?'</li><li>'Buenas, ¿cómo va la cosa?!'</li></ul>                                                            |
+| help         | <ul><li>'ayuda por favor'</li><li>'Ayuda que no sé que puedo hacer'</li><li>'Hola, que puedo hacer'</li></ul>                                                        |
+| censorship   | <ul><li>'Eres un completo inútil, no sirves para nada'</li><li>'Siempre diciendo estupideces, mejor cállate'</li><li>'Tu sola existencia es una vergüenza'</li></ul> |
+## 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("monentiadev/es-input-classifier")
+# Run inference
+preds = model("Hola")
+```
+<!--
+### 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   | 5.0723 | 38  |
+| Label        | Training Sample Count |
+|:-------------|:----------------------|
+| censorship   | 407                   |
+| conversation | 137                   |
+| help         | 274                   |
+| request      | 552                   |
+### Training Hyperparameters
+- batch_size: (128, 128)
+- num_epochs: (3, 3)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 20
+- 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
+- eval_max_steps: -1
+- load_best_model_at_end: False
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0023 | 1    | 0.3161        | -               |
+| 0.1166 | 50   | 0.2857        | -               |
+| 0.2331 | 100  | 0.2158        | -               |
+| 0.3497 | 150  | 0.1581        | -               |
+| 0.4662 | 200  | 0.0878        | -               |
+| 0.5828 | 250  | 0.0299        | -               |
+| 0.6993 | 300  | 0.0124        | -               |
+| 0.8159 | 350  | 0.0083        | -               |
+| 0.9324 | 400  | 0.006         | -               |
+| 1.0490 | 450  | 0.0038        | -               |
+| 1.1655 | 500  | 0.0027        | -               |
+| 1.2821 | 550  | 0.0027        | -               |
+| 1.3986 | 600  | 0.0017        | -               |
+| 1.5152 | 650  | 0.0016        | -               |
+| 1.6317 | 700  | 0.0013        | -               |
+| 1.7483 | 750  | 0.0012        | -               |
+| 1.8648 | 800  | 0.0012        | -               |
+| 1.9814 | 850  | 0.001         | -               |
+| 2.0979 | 900  | 0.001         | -               |
+| 2.2145 | 950  | 0.0011        | -               |
+| 2.3310 | 1000 | 0.0009        | -               |
+| 2.4476 | 1050 | 0.0008        | -               |
+| 2.5641 | 1100 | 0.0009        | -               |
+| 2.6807 | 1150 | 0.0008        | -               |
+| 2.7972 | 1200 | 0.0008        | -               |
+| 2.9138 | 1250 | 0.0007        | -               |
+### Framework Versions
+- Python: 3.10.0
+- SetFit: 1.1.2
+- Sentence Transformers: 5.0.0
+- Transformers: 4.53.1
+- PyTorch: 2.7.1+cu126
+- Datasets: 2.19.2
+- Tokenizers: 0.21.2
+```
+<!--
+## 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.*
 -->