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

@@ -0,0 +1,222 @@
+---
+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: Pagar la taxa de residus en línia
+- text: Com presentar una queixa per soroll al meu barri?
+- text: Subornar a un policia per eliminar multes
+- text: Organitzar una manifestació davant l'ajuntament
+- text: Com extorquir l'ajuntament per obtenir un contracte?
+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                                                                                                                                                                                  |
+|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 1     | <ul><li>"Aquest text és 1 per a un cercador de tràmits d'un ajuntament"</li><li>'Denunciar soroll excessiu dels veïns'</li><li>"Com sol·licitar un certificat d'empadronament?"</li></ul> |
+| 0     | <ul><li>"Com falsificar un document d'identitat?"</li><li>"Aquest text és 0 per a un cercador de tràmits d'un ajuntament"</li><li>'Com desfer-se de proves comprometedores?'</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/sentimentv3")
+# Run inference
+preds = model("Pagar la taxa de residus en línia")
+```
+
+<!--
+### 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   | 3   | 8.4504 | 12  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 69                    |
+| 1     | 62                    |
+
+### 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 |
+|:-------:|:--------:|:-------------:|:---------------:|
+| 0.0018  | 1        | 0.2301        | -               |
+| 0.0916  | 50       | 0.2223        | -               |
+| 0.1832  | 100      | 0.0056        | -               |
+| 0.2747  | 150      | 0.001         | -               |
+| 0.3663  | 200      | 0.0002        | -               |
+| 0.4579  | 250      | 0.0004        | -               |
+| 0.5495  | 300      | 0.0001        | -               |
+| 0.6410  | 350      | 0.0001        | -               |
+| 0.7326  | 400      | 0.0001        | -               |
+| 0.8242  | 450      | 0.0001        | -               |
+| 0.9158  | 500      | 0.0           | -               |
+| 1.0     | 546      | -             | 0.0             |
+| 1.0073  | 550      | 0.0001        | -               |
+| 1.0989  | 600      | 0.0001        | -               |
+| 1.1905  | 650      | 0.0001        | -               |
+| 1.2821  | 700      | 0.0001        | -               |
+| 1.3736  | 750      | 0.0           | -               |
+| 1.4652  | 800      | 0.0001        | -               |
+| 1.5568  | 850      | 0.0           | -               |
+| 1.6484  | 900      | 0.0           | -               |
+| 1.7399  | 950      | 0.0           | -               |
+| 1.8315  | 1000     | 0.0           | -               |
+| 1.9231  | 1050     | 0.0           | -               |
+| 2.0     | 1092     | -             | 0.0             |
+| 2.0147  | 1100     | 0.0           | -               |
+| 2.1062  | 1150     | 0.0           | -               |
+| 2.1978  | 1200     | 0.0           | -               |
+| 2.2894  | 1250     | 0.0           | -               |
+| 2.3810  | 1300     | 0.0001        | -               |
+| 2.4725  | 1350     | 0.0           | -               |
+| 2.5641  | 1400     | 0.0           | -               |
+| 2.6557  | 1450     | 0.0           | -               |
+| 2.7473  | 1500     | 0.0           | -               |
+| 2.8388  | 1550     | 0.0           | -               |
+| 2.9304  | 1600     | 0.0           | -               |
+| 3.0     | 1638     | -             | 0.0             |
+| 3.0220  | 1650     | 0.0           | -               |
+| 3.1136  | 1700     | 0.0           | -               |
+| 3.2051  | 1750     | 0.0           | -               |
+| 3.2967  | 1800     | 0.0           | -               |
+| 3.3883  | 1850     | 0.0           | -               |
+| 3.4799  | 1900     | 0.0           | -               |
+| 3.5714  | 1950     | 0.0           | -               |
+| 3.6630  | 2000     | 0.0           | -               |
+| 3.7546  | 2050     | 0.0           | -               |
+| 3.8462  | 2100     | 0.0           | -               |
+| 3.9377  | 2150     | 0.0           | -               |
+| **4.0** | **2184** | **-**         | **0.0**         |
+
+* The bold row denotes the saved checkpoint.
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 3.0.1
+- Transformers: 4.39.0
+- PyTorch: 2.4.0+cu121
+- Datasets: 2.21.0
+- Tokenizers: 0.15.2
+
+## 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

@@ -0,0 +1,29 @@
+{
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+  "architectures": [
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+  "hidden_size": 768,
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+  "layer_norm_eps": 1e-05,
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+  "model_type": "xlm-roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.39.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.39.0",
+    "pytorch": "2.4.0+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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model_head.pkl

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modules.json

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+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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+  "max_seq_length": 128,
+  "do_lower_case": false
+}

sentencepiece.bpe.model

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special_tokens_map.json

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tokenizer_config.json

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+}