Add SetFit model

.gitattributes

@@ -33,3 +33,5 @@ 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
+unigram.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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 +1,300 @@
+---
+base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+library_name: setfit
+metrics:
+- accuracy
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: Para saber si un negocio va a funcionar, es necesario realizar un estudio
+    de mercado, valorar la economía local durante un año, considerar la afluencia
+    de personas y la ubicación, así como determinar el tamaño de la inversión.
+- text: Apoyo la opinión de Tyrexito y también reclamo al Banco Sabadell por sus comisiones.
+- text: Los resultados del Banco Sabadell impulsan al IBEX 35.
+- text: Aunque no pude retirar el bono de festividad en el cajero, ING y AKBANK rechazaron
+    mis quejas, pero tras anunciar una denuncia, me transfirieron el dinero en una
+    hora; si tienes razón, no te rindas.
+- text: El Gobierno presentará al nuevo gobernador del Banco de España en una Comisión
+    del Congreso este jueves.
+inference: true
+model-index:
+- name: SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.7739130434782608
+      name: Accuracy
+---
+
+# SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) 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:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
+- **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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+|:---------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| relevant | <ul><li>'Nuevo caso de phishing relacionado con Abanca, registrado el 23 de julio de 2024, con la URL: /www.inicio-abanca.com/es/WELE200M_Logon_Ini.aspx.'</li><li>'Una alumna que trabajó en Bancomer reveló un esquema de robo en el que dos cajeros afirmaban que un cliente había depositado mil pesos en un pago de dos mil y se quedaban con la mitad cada uno.'</li><li>'Las previsiones de crecimiento de España para 2024 han mejorado según diversas organizaciones, con estimaciones que oscilan entre el 1,8% y el 2,4%, impulsadas por turismo, exportaciones y trabajadores extranjeros.'</li></ul> |
+| discard  | <ul><li>'Banco Santander ofrece una cuenta en línea sin comisiones y un bono de 400€ por domiciliar tu nómina.'</li><li>'El BBVA fue el banco que peor me trató al tener que contratar productos innecesarios para conseguir mi primera hipoteca de funcionario.'</li><li>'CaixaBank se destaca como líder del sector bancario gracias a su sólido crecimiento y eficiencia operativa, convirtiéndose en una opción atractiva para inversores.'</li></ul>                                                                                                                                                         |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.7739   |
+
+## 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("saraestevez/setfit-minilm-bank-tweets-processed-200")
+# Run inference
+preds = model("Los resultados del Banco Sabadell impulsan al IBEX 35.")
+```
+
+<!--
+### 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   | 21.3275 | 41  |
+
+| Label    | Training Sample Count |
+|:---------|:----------------------|
+| discard  | 200                   |
+| relevant | 200                   |
+
+### Training Hyperparameters
+- batch_size: (16, 2)
+- num_epochs: (1, 16)
+- 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: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0002 | 1    | 0.4199        | -               |
+| 0.0100 | 50   | 0.3357        | -               |
+| 0.0199 | 100  | 0.3198        | -               |
+| 0.0299 | 150  | 0.2394        | -               |
+| 0.0398 | 200  | 0.2411        | -               |
+| 0.0498 | 250  | 0.2277        | -               |
+| 0.0597 | 300  | 0.1876        | -               |
+| 0.0697 | 350  | 0.1481        | -               |
+| 0.0796 | 400  | 0.1533        | -               |
+| 0.0896 | 450  | 0.0145        | -               |
+| 0.0995 | 500  | 0.0113        | -               |
+| 0.1095 | 550  | 0.0045        | -               |
+| 0.1194 | 600  | 0.0201        | -               |
+| 0.1294 | 650  | 0.0008        | -               |
+| 0.1393 | 700  | 0.0003        | -               |
+| 0.1493 | 750  | 0.0003        | -               |
+| 0.1592 | 800  | 0.0003        | -               |
+| 0.1692 | 850  | 0.0001        | -               |
+| 0.1791 | 900  | 0.0001        | -               |
+| 0.1891 | 950  | 0.0001        | -               |
+| 0.1990 | 1000 | 0.0001        | -               |
+| 0.2090 | 1050 | 0.0001        | -               |
+| 0.2189 | 1100 | 0.0002        | -               |
+| 0.2289 | 1150 | 0.0001        | -               |
+| 0.2388 | 1200 | 0.0001        | -               |
+| 0.2488 | 1250 | 0.0001        | -               |
+| 0.2587 | 1300 | 0.0           | -               |
+| 0.2687 | 1350 | 0.0001        | -               |
+| 0.2786 | 1400 | 0.0001        | -               |
+| 0.2886 | 1450 | 0.0001        | -               |
+| 0.2985 | 1500 | 0.0           | -               |
+| 0.3085 | 1550 | 0.0001        | -               |
+| 0.3184 | 1600 | 0.0           | -               |
+| 0.3284 | 1650 | 0.0           | -               |
+| 0.3383 | 1700 | 0.0           | -               |
+| 0.3483 | 1750 | 0.0001        | -               |
+| 0.3582 | 1800 | 0.0           | -               |
+| 0.3682 | 1850 | 0.0           | -               |
+| 0.3781 | 1900 | 0.0           | -               |
+| 0.3881 | 1950 | 0.0           | -               |
+| 0.3980 | 2000 | 0.0           | -               |
+| 0.4080 | 2050 | 0.0           | -               |
+| 0.4179 | 2100 | 0.0           | -               |
+| 0.4279 | 2150 | 0.0           | -               |
+| 0.4378 | 2200 | 0.0           | -               |
+| 0.4478 | 2250 | 0.0           | -               |
+| 0.4577 | 2300 | 0.0           | -               |
+| 0.4677 | 2350 | 0.0           | -               |
+| 0.4776 | 2400 | 0.0           | -               |
+| 0.4876 | 2450 | 0.0           | -               |
+| 0.4975 | 2500 | 0.0           | -               |
+| 0.5075 | 2550 | 0.0           | -               |
+| 0.5174 | 2600 | 0.0           | -               |
+| 0.5274 | 2650 | 0.0           | -               |
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+| 0.5771 | 2900 | 0.0           | -               |
+| 0.5871 | 2950 | 0.0           | -               |
+| 0.5970 | 3000 | 0.0           | -               |
+| 0.6070 | 3050 | 0.0           | -               |
+| 0.6169 | 3100 | 0.0           | -               |
+| 0.6269 | 3150 | 0.0           | -               |
+| 0.6368 | 3200 | 0.0           | -               |
+| 0.6468 | 3250 | 0.0           | -               |
+| 0.6567 | 3300 | 0.0           | -               |
+| 0.6667 | 3350 | 0.0           | -               |
+| 0.6766 | 3400 | 0.0           | -               |
+| 0.6866 | 3450 | 0.0           | -               |
+| 0.6965 | 3500 | 0.0           | -               |
+| 0.7065 | 3550 | 0.0           | -               |
+| 0.7164 | 3600 | 0.0           | -               |
+| 0.7264 | 3650 | 0.0           | -               |
+| 0.7363 | 3700 | 0.0           | -               |
+| 0.7463 | 3750 | 0.0           | -               |
+| 0.7562 | 3800 | 0.0           | -               |
+| 0.7662 | 3850 | 0.0           | -               |
+| 0.7761 | 3900 | 0.0           | -               |
+| 0.7861 | 3950 | 0.0           | -               |
+| 0.7960 | 4000 | 0.0           | -               |
+| 0.8060 | 4050 | 0.0           | -               |
+| 0.8159 | 4100 | 0.0           | -               |
+| 0.8259 | 4150 | 0.0           | -               |
+| 0.8358 | 4200 | 0.0           | -               |
+| 0.8458 | 4250 | 0.0           | -               |
+| 0.8557 | 4300 | 0.0           | -               |
+| 0.8657 | 4350 | 0.0           | -               |
+| 0.8756 | 4400 | 0.0           | -               |
+| 0.8856 | 4450 | 0.0           | -               |
+| 0.8955 | 4500 | 0.0           | -               |
+| 0.9055 | 4550 | 0.0           | -               |
+| 0.9154 | 4600 | 0.0           | -               |
+| 0.9254 | 4650 | 0.0           | -               |
+| 0.9353 | 4700 | 0.0           | -               |
+| 0.9453 | 4750 | 0.0           | -               |
+| 0.9552 | 4800 | 0.0           | -               |
+| 0.9652 | 4850 | 0.0           | -               |
+| 0.9751 | 4900 | 0.0           | -               |
+| 0.9851 | 4950 | 0.0           | -               |
+| 0.9950 | 5000 | 0.0           | -               |
+
+### Framework Versions
+- Python: 3.11.0rc1
+- SetFit: 1.0.3
+- Sentence Transformers: 2.7.0
+- Transformers: 4.39.0
+- PyTorch: 2.3.1+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 +1,26 @@
+{
+  "_name_or_path": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
+  "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": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.39.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 250037
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  },
+  "prompts": {},
+  "default_prompt_name": null
+}

config_setfit.json

@@ -0,0 +1,7 @@
+{
+  "labels": [
+    "discard",
+    "relevant"
+  ],
+  "normalize_embeddings": false
+}

model.safetensors

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model_head.pkl

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modules.json

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+[
+  {
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+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "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
+}

special_tokens_map.json

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+{
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+    "lstrip": false,
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+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
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+}

tokenizer.json

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tokenizer_config.json

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+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
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+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}

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