Push model using huggingface_hub.

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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+---
+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.*
+-->

config.json

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+{
+  "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": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.53.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "5.0.0",
+    "transformers": "4.53.1",
+    "pytorch": "2.7.1+cu126"
+  },
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

config_setfit.json

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+{
+  "labels": [
+    "censorship",
+    "conversation",
+    "help",
+    "request"
+  ],
+  "normalize_embeddings": false
+}

model.safetensors

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+size 90864192

model_head.pkl

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modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+{
+    "max_seq_length": 256,
+    "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "max_length": 256,
+  "model_max_length": 256,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}