Add SetFit model

.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": 768,
+  "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: projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base
+library_name: setfit
+metrics:
+- accuracy
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: Hola!
+- text: Hola, tinc algunes preguntes sobre tràmits que voldria fer.
+- text: Quin és el propòsit de la garantia dels serveis adjudicats?
+- text: Hola, quin és el paper dels dipòsits o fiances en la garantia dels serveis?
+- text: Bona tarda! Què tal?
+inference: true
+---
+
+# SetFit with projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base](https://huggingface.co/projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base) 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:** [projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base](https://huggingface.co/projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base)
+- **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                                                                                                                                                                                                                                   |
+|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 0     | <ul><li>'Quin és el benefici de la devolució de fiances i avals?'</li><li>'Bon dia, quin és el procediment per obtenir la llicència?'</li><li>'Hola Bon dia, vull saber quin és el benefici de la devolució de fiances i avals.'</li></ul> |
+| 1     | <ul><li>'Ei, què tal? Com va tot?'</li><li>"Bon dia, m'agradaria saber més sobre els tràmits disponibles."</li><li>'Ei, com et va?'</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("adriansanz/gret3")
+# 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   | 10.0083 | 17  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 60                    |
+| 1     | 60                    |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (2, 2)
+- 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
+- l2_weight: 0.01
+- seed: 42
+- evaluation_strategy: epoch
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0022 | 1    | 0.2716        | -               |
+| 0.1092 | 50   | 0.1656        | -               |
+| 0.2183 | 100  | 0.0068        | -               |
+| 0.3275 | 150  | 0.0003        | -               |
+| 0.4367 | 200  | 0.0002        | -               |
+| 0.5459 | 250  | 0.0001        | -               |
+| 0.6550 | 300  | 0.0001        | -               |
+| 0.7642 | 350  | 0.0001        | -               |
+| 0.8734 | 400  | 0.0001        | -               |
+| 0.9825 | 450  | 0.0001        | -               |
+| 1.0    | 458  | -             | 0.0002          |
+| 0.0022 | 1    | 0.0001        | -               |
+| 0.1092 | 50   | 0.0001        | -               |
+| 0.2183 | 100  | 0.0001        | -               |
+| 0.3275 | 150  | 0.0016        | -               |
+| 0.4367 | 200  | 0.0002        | -               |
+| 0.5459 | 250  | 0.0           | -               |
+| 0.6550 | 300  | 0.0           | -               |
+| 0.7642 | 350  | 0.0           | -               |
+| 0.8734 | 400  | 0.0           | -               |
+| 0.9825 | 450  | 0.0           | -               |
+| 1.0    | 458  | -             | 0.0001          |
+| 1.0917 | 500  | 0.0           | -               |
+| 1.2009 | 550  | 0.0           | -               |
+| 1.3100 | 600  | 0.0           | -               |
+| 1.4192 | 650  | 0.0           | -               |
+| 1.5284 | 700  | 0.0           | -               |
+| 1.6376 | 750  | 0.0           | -               |
+| 1.7467 | 800  | 0.0           | -               |
+| 1.8559 | 850  | 0.0           | -               |
+| 1.9651 | 900  | 0.0           | -               |
+| 2.0    | 916  | -             | 0.0000          |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.1.0
+- Sentence Transformers: 3.2.1
+- Transformers: 4.42.2
+- PyTorch: 2.5.0+cu121
+- Datasets: 3.1.0
+- 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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+{
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+  "model_type": "xlm-roberta",
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+  "num_hidden_layers": 12,
+  "output_past": true,
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+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.42.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.2.1",
+    "transformers": "4.42.2",
+    "pytorch": "2.5.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

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+  "normalize_embeddings": false,
+  "labels": null
+}

model.safetensors

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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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+  "do_lower_case": false
+}

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tokenizer_config.json

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