Push model using huggingface_hub.

.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": 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 +1,203 @@
+---
+library_name: setfit
+metrics:
+- accuracy
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: Lewis Hamilton pide perdón tras ser acusado de sexista por burlarse de su
+    sobrino
+- text: 'Nuevas revelaciones del FIFA Gate: una cuenta ultra secreta y el temor reverencial
+    a Julio Grondona'
+- text: Hallaron una inmensa `huella digital` en el espacio
+- text: Qué hacía Gastón Pauls viendo a la Selección con Lionel Messi y Sergio Agüero
+- text: 'Bitcoin: la volatilidad de las últimas semanas abre el debate sobre el futuro
+    de la moneda'
+inference: true
+---
+
+# SetFit
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. 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:** [Unknown](https://huggingface.co/unknown) -->
+- **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:** 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                                                                                                                                                                                                                                                                  |
+|:------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Evento      | <ul><li>'El dólar vuelve a subir a la espera de una decisión clave del Banco Central'</li><li>'Viernes caluroso y sin lluvias'</li><li>'ARA San Juan | El dolor de los familiares tras la retirada de EEUU: `Nos están dejando sin recursos para buscar`'</li></ul>       |
+| Perspectiva | <ul><li>'El futuro de la educación tras la pandemia: ¿hacia un modelo híbrido permanente?'</li><li>'¿Cómo impacta la automatización en los trabajos de baja calificación?'</li><li>'Feminicidios: Falta una construcción social y cultural contra la violencia'</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("EmanuelOrler/setfit-spanish-event-perspective")
+# Run inference
+preds = model("Hallaron una inmensa `huella digital` en el espacio")
+```
+
+<!--
+### 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   | 12.9231 | 24  |
+
+| Label       | Training Sample Count |
+|:------------|:----------------------|
+| Evento      | 22                    |
+| Perspectiva | 17                    |
+
+### Training Hyperparameters
+- batch_size: (12, 12)
+- num_epochs: (4, 16)
+- max_steps: -1
+- sampling_strategy: undersampling
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0159 | 1    | 0.0885        | -               |
+| 0.1587 | 10   | 0.3927        | 0.2944          |
+| 0.3175 | 20   | 0.3039        | 0.2387          |
+| 0.4762 | 30   | 0.2466        | 0.1807          |
+| 0.6349 | 40   | 0.2049        | 0.1686          |
+| 0.7937 | 50   | 0.1803        | 0.1786          |
+| 0.9524 | 60   | 0.1319        | 0.2002          |
+| 1.1111 | 70   | 0.045         | 0.3103          |
+| 1.2698 | 80   | 0.0099        | 0.3200          |
+| 1.4286 | 90   | 0.0036        | 0.3845          |
+| 1.5873 | 100  | 0.0021        | 0.4078          |
+| 1.7460 | 110  | 0.0011        | 0.4184          |
+| 1.9048 | 120  | 0.0011        | 0.4186          |
+| 2.0635 | 130  | 0.0009        | 0.4282          |
+| 2.2222 | 140  | 0.0008        | 0.4242          |
+| 2.3810 | 150  | 0.0008        | 0.4269          |
+| 2.5397 | 160  | 0.0007        | 0.4303          |
+| 2.6984 | 170  | 0.0006        | 0.4301          |
+| 2.8571 | 180  | 0.0006        | 0.4321          |
+| 3.0159 | 190  | 0.0006        | 0.4311          |
+| 3.1746 | 200  | 0.0005        | 0.4291          |
+| 3.3333 | 210  | 0.0006        | 0.4322          |
+| 3.4921 | 220  | 0.0005        | 0.4315          |
+| 3.6508 | 230  | 0.0005        | 0.4308          |
+| 3.8095 | 240  | 0.0005        | 0.4307          |
+| 3.9683 | 250  | 0.0004        | 0.4312          |
+
+### Framework Versions
+- Python: 3.10.14
+- SetFit: 1.1.0
+- Sentence Transformers: 3.2.1
+- Transformers: 4.44.0
+- PyTorch: 2.4.0
+- Datasets: 2.21.0
+- 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

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+{
+  "_name_or_path": "Marqo/multilingual-e5-small",
+  "architectures": [
+    "BertModel"
+  ],
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+  "model_type": "bert",
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+  "num_hidden_layers": 12,
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+  "position_embedding_type": "absolute",
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+  "torch_dtype": "float32",
+  "transformers_version": "4.44.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 250037
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.2.1",
+    "transformers": "4.44.0",
+    "pytorch": "2.4.0"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

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+{
+  "normalize_embeddings": false,
+  "labels": [
+    "Evento",
+    "Perspectiva"
+  ]
+}

model.safetensors

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modules.json

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+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+  "do_lower_case": false
+}

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special_tokens_map.json

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tokenizer_config.json

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+}