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

@@ -0,0 +1,10 @@
+{
+  "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 +1,318 @@
+---
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+base_model: projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base
+metrics:
+- accuracy
+widget:
+- text: Estic preocupat per la falta de legislació i regulació adequada per protegir
+    les dades personals en línia. Les empreses han d'assumir més responsabilitat i
+    complir amb els estàndards de seguretat més estrictes per protegir la privacitat
+    dels usuaris.
+- text: M'he sentit frustrat i insegur a causa de la manca de control sobre les meves
+    dades personals en línia. Les empreses i els proveïdors de serveis haurien de
+    ser més transparents sobre com gestionen les nostres dades i oferir opcions de
+    control més gran als usuaris.
+- text: Proposo l'ús de lluminàries de tecnologia LED amb control de la intensitat
+    i la direccionalitat de la llum per minimitzar la contaminació lumínica i preservar
+    la visió del cel nocturn.
+- text: Estic frustrat amb les polítiques de comerç exterior que no promoguin la transferència
+    de tecnologia i coneixement cap a les empreses locals. La manca d'assistència
+    tècnica i suport pot limitar la capacitat de les empreses locals per competir
+    a nivell internacional.
+- text: Suggeriria que es realitzessin campanyes de recompensa per incentivar els
+    ciutadans a informar de fuites d'aigua, oferint descomptes en la factura d'aigua
+    o altres incentius.
+pipeline_tag: text-classification
+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:** 20 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>'Aquest text és Aigües.'</li><li>'Aquest text és Aigües.'</li><li>'Aquest text és Aigües.'</li></ul>                                                          |
+| 1     | <ul><li>'Aquest text és Consum, comerç i mercats.'</li><li>'Aquest text és Consum, comerç i mercats.'</li><li>'Aquest text és Consum, comerç i mercats.'</li></ul>    |
+| 2     | <ul><li>'Aquest text és Cultura.'</li><li>'Aquest text és Cultura.'</li><li>'Aquest text és Cultura.'</li></ul>                                                       |
+| 3     | <ul><li>'Aquest text és Economia.'</li><li>'Aquest text és Economia.'</li><li>'Aquest text és Economia.'</li></ul>                                                    |
+| 4     | <ul><li>'Aquest text és Educació.'</li><li>'Aquest text és Educació.'</li><li>'Aquest text és Educació.'</li></ul>                                                    |
+| 5     | <ul><li>'Aquest text és Enllumenat públic.'</li><li>'Aquest text és Enllumenat públic.'</li><li>'Aquest text és Enllumenat públic.'</li></ul>                         |
+| 6     | <ul><li>'Aquest text és Esports.'</li><li>'Aquest text és Esports.'</li><li>'Aquest text és Esports.'</li></ul>                                                       |
+| 7     | <ul><li>'Aquest text és Habitatge.'</li><li>'Aquest text és Habitatge.'</li><li>'Aquest text és Habitatge.'</li></ul>                                                 |
+| 8     | <ul><li>'Aquest text és Horta.'</li><li>'Aquest text és Horta.'</li><li>'Aquest text és Horta.'</li></ul>                                                             |
+| 9     | <ul><li>'Aquest text és Informació general.'</li><li>'Aquest text és Informació general.'</li><li>'Aquest text és Informació general.'</li></ul>                      |
+| 10    | <ul><li>'Aquest text és Informàtica.'</li><li>'Aquest text és Informàtica.'</li><li>'Aquest text és Informàtica.'</li></ul>                                           |
+| 11    | <ul><li>'Aquest text és Joventut.'</li><li>'Aquest text és Joventut.'</li><li>'Aquest text és Joventut.'</li></ul>                                                    |
+| 12    | <ul><li>'Aquest text és Medi ambient.'</li><li>'Aquest text és Medi ambient.'</li><li>'Aquest text és Medi ambient.'</li></ul>                                        |
+| 13    | <ul><li>'Aquest text és Neteja de la via pública.'</li><li>'Aquest text és Neteja de la via pública.'</li><li>'Aquest text és Neteja de la via pública.'</li></ul>    |
+| 14    | <ul><li>'Aquest text és Salut pública i Cementiri.'</li><li>'Aquest text és Salut pública i Cementiri.'</li><li>'Aquest text és Salut pública i Cementiri.'</li></ul> |
+| 15    | <ul><li>'Aquest text és Seguretat.'</li><li>'Aquest text és Seguretat.'</li><li>'Aquest text és Seguretat.'</li></ul>                                                 |
+| 16    | <ul><li>'Aquest text és Serveis socials.'</li><li>'Aquest text és Serveis socials.'</li><li>'Aquest text és Serveis socials.'</li></ul>                               |
+| 17    | <ul><li>'Aquest text és Tramitacions.'</li><li>'Aquest text és Tramitacions.'</li><li>'Aquest text és Tramitacions.'</li></ul>                                        |
+| 18    | <ul><li>'Aquest text és Urbanisme.'</li><li>'Aquest text és Urbanisme.'</li><li>'Aquest text és Urbanisme.'</li></ul>                                                 |
+| 19    | <ul><li>'Aquest text és Via pública i mobilitat.'</li><li>'Aquest text és Via pública i mobilitat.'</li><li>'Aquest text és Via pública i mobilitat.'</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/fs_setfit_dummy")
+# Run inference
+preds = model("Suggeriria que es realitzessin campanyes de recompensa per incentivar els ciutadans a informar de fuites d'aigua, oferint descomptes en la factura d'aigua o altres incentius.")
+```
+
+<!--
+### 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   | 4   | 4.85   | 8   |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 8                     |
+| 1     | 8                     |
+| 2     | 8                     |
+| 3     | 8                     |
+| 4     | 8                     |
+| 5     | 8                     |
+| 6     | 8                     |
+| 7     | 8                     |
+| 8     | 8                     |
+| 9     | 8                     |
+| 10    | 8                     |
+| 11    | 8                     |
+| 12    | 8                     |
+| 13    | 8                     |
+| 14    | 8                     |
+| 15    | 8                     |
+| 16    | 8                     |
+| 17    | 8                     |
+| 18    | 8                     |
+| 19    | 8                     |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (3, 3)
+- 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 |
+|:-------:|:--------:|:-------------:|:---------------:|
+| 0.0007  | 1        | 0.1362        | -               |
+| 0.0329  | 50       | 0.0344        | -               |
+| 0.0658  | 100      | 0.0017        | -               |
+| 0.0987  | 150      | 0.0013        | -               |
+| 0.1316  | 200      | 0.0013        | -               |
+| 0.1645  | 250      | 0.0007        | -               |
+| 0.1974  | 300      | 0.0004        | -               |
+| 0.2303  | 350      | 0.0004        | -               |
+| 0.2632  | 400      | 0.0006        | -               |
+| 0.2961  | 450      | 0.0005        | -               |
+| 0.3289  | 500      | 0.0003        | -               |
+| 0.3618  | 550      | 0.0005        | -               |
+| 0.3947  | 600      | 0.0006        | -               |
+| 0.4276  | 650      | 0.0004        | -               |
+| 0.4605  | 700      | 0.0003        | -               |
+| 0.4934  | 750      | 0.0001        | -               |
+| 0.5263  | 800      | 0.0002        | -               |
+| 0.5592  | 850      | 0.0002        | -               |
+| 0.5921  | 900      | 0.0002        | -               |
+| 0.625   | 950      | 0.0002        | -               |
+| 0.6579  | 1000     | 0.0002        | -               |
+| 0.6908  | 1050     | 0.0002        | -               |
+| 0.7237  | 1100     | 0.0002        | -               |
+| 0.7566  | 1150     | 0.0002        | -               |
+| 0.7895  | 1200     | 0.0002        | -               |
+| 0.8224  | 1250     | 0.0003        | -               |
+| 0.8553  | 1300     | 0.0002        | -               |
+| 0.8882  | 1350     | 0.0001        | -               |
+| 0.9211  | 1400     | 0.0001        | -               |
+| 0.9539  | 1450     | 0.0002        | -               |
+| 0.9868  | 1500     | 0.0002        | -               |
+| 1.0     | 1520     | -             | 0.1669          |
+| 1.0197  | 1550     | 0.0002        | -               |
+| 1.0526  | 1600     | 0.0001        | -               |
+| 1.0855  | 1650     | 0.0003        | -               |
+| 1.1184  | 1700     | 0.0002        | -               |
+| 1.1513  | 1750     | 0.0002        | -               |
+| 1.1842  | 1800     | 0.0001        | -               |
+| 1.2171  | 1850     | 0.0002        | -               |
+| 1.25    | 1900     | 0.0003        | -               |
+| 1.2829  | 1950     | 0.0002        | -               |
+| 1.3158  | 2000     | 0.0001        | -               |
+| 1.3487  | 2050     | 0.0002        | -               |
+| 1.3816  | 2100     | 0.0001        | -               |
+| 1.4145  | 2150     | 0.0001        | -               |
+| 1.4474  | 2200     | 0.0001        | -               |
+| 1.4803  | 2250     | 0.0002        | -               |
+| 1.5132  | 2300     | 0.0002        | -               |
+| 1.5461  | 2350     | 0.0002        | -               |
+| 1.5789  | 2400     | 0.0001        | -               |
+| 1.6118  | 2450     | 0.0001        | -               |
+| 1.6447  | 2500     | 0.0002        | -               |
+| 1.6776  | 2550     | 0.0002        | -               |
+| 1.7105  | 2600     | 0.0002        | -               |
+| 1.7434  | 2650     | 0.0001        | -               |
+| 1.7763  | 2700     | 0.0001        | -               |
+| 1.8092  | 2750     | 0.0001        | -               |
+| 1.8421  | 2800     | 0.0001        | -               |
+| 1.875   | 2850     | 0.0001        | -               |
+| 1.9079  | 2900     | 0.0001        | -               |
+| 1.9408  | 2950     | 0.0001        | -               |
+| 1.9737  | 3000     | 0.0001        | -               |
+| 2.0     | 3040     | -             | 0.1629          |
+| 2.0066  | 3050     | 0.0001        | -               |
+| 2.0395  | 3100     | 0.0001        | -               |
+| 2.0724  | 3150     | 0.0001        | -               |
+| 2.1053  | 3200     | 0.0001        | -               |
+| 2.1382  | 3250     | 0.0001        | -               |
+| 2.1711  | 3300     | 0.0001        | -               |
+| 2.2039  | 3350     | 0.0001        | -               |
+| 2.2368  | 3400     | 0.0001        | -               |
+| 2.2697  | 3450     | 0.0001        | -               |
+| 2.3026  | 3500     | 0.0002        | -               |
+| 2.3355  | 3550     | 0.0001        | -               |
+| 2.3684  | 3600     | 0.0001        | -               |
+| 2.4013  | 3650     | 0.0001        | -               |
+| 2.4342  | 3700     | 0.0001        | -               |
+| 2.4671  | 3750     | 0.0001        | -               |
+| 2.5     | 3800     | 0.0001        | -               |
+| 2.5329  | 3850     | 0.0001        | -               |
+| 2.5658  | 3900     | 0.0001        | -               |
+| 2.5987  | 3950     | 0.0           | -               |
+| 2.6316  | 4000     | 0.0           | -               |
+| 2.6645  | 4050     | 0.0001        | -               |
+| 2.6974  | 4100     | 0.0           | -               |
+| 2.7303  | 4150     | 0.0001        | -               |
+| 2.7632  | 4200     | 0.0001        | -               |
+| 2.7961  | 4250     | 0.0001        | -               |
+| 2.8289  | 4300     | 0.0001        | -               |
+| 2.8618  | 4350     | 0.0001        | -               |
+| 2.8947  | 4400     | 0.0001        | -               |
+| 2.9276  | 4450     | 0.0001        | -               |
+| 2.9605  | 4500     | 0.0001        | -               |
+| 2.9934  | 4550     | 0.0           | -               |
+| **3.0** | **4560** | **-**         | **0.1625**      |
+
+* The bold row denotes the saved checkpoint.
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 3.0.0
+- Transformers: 4.39.0
+- PyTorch: 2.3.0+cu121
+- Datasets: 2.19.1
+- 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 @@
+{
+  "_name_or_path": "checkpoints/step_4560",
+  "architectures": [
+    "XLMRobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "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

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

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

model.safetensors

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model_head.pkl

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modules.json

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+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
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+    "name": "1",
+    "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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+  "tokenizer_class": "XLMRobertaTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}