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 +1,7 @@
+{
+  "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
+}

README.md

@@ -0,0 +1,276 @@
+---
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+metrics:
+- accuracy
+widget:
+- text: Vem pra Irenil em Paratinga, bonitão
+- text: Salve Salve Senhor Governador JERÔNIMO RODRIGUES olhando para as TRADIÇÕES
+- text: Parabéns meu Governador! O foguete 🚀 não para . Muitas realizações entregue
+    em 7 meses , muito trabalho .
+- text: 👏👏👏
+- text: Bom demais governador sobre o piso da enfermagem o que o senhor diz para nos
+pipeline_tag: text-classification
+inference: true
+base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+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: 0.9042553191489362
+      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                                                                                                                                                                                                                                                                                                        |
+|:---------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Positive | <ul><li>'Enfim,Bonfim 🥳🥳🥳🥳🥳'</li><li>'👏👏👏👏'</li><li>'Pequenas ações fazem sonhos realidades #OhBrabo 💙💙💙'</li></ul>                                                                                                                                                                                             |
+| Negative | <ul><li>'@jeronimorodriguesba quando terá uma segunda convocação do concurso SECBA?'</li><li>'Cadê a MP do piso da enfermagem ministro'</li><li>'Sim !! A escola municipal aqui do bairro liberdade,30 crianças esperando até hoje as profissionais ADI para crianças que necessita acompanhamento..'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.9043   |
+
+## 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("Tarssio/modelo_setfit_politica_BA")
+# Run inference
+preds = model("👏👏👏")
+```
+
+<!--
+### 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   | 19.4813 | 313 |
+
+| Label    | Training Sample Count |
+|:---------|:----------------------|
+| Negative | 175                   |
+| Positive | 199                   |
+
+### Training Hyperparameters
+- batch_size: (4, 4)
+- num_epochs: (4, 4)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 5
+- 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 |
+|:-------:|:--------:|:-------------:|:---------------:|
+| 0.0011  | 1        | 0.3616        | -               |
+| 0.0535  | 50       | 0.3129        | -               |
+| 0.1070  | 100      | 0.2912        | -               |
+| 0.1604  | 150      | 0.191         | -               |
+| 0.2139  | 200      | 0.0907        | -               |
+| 0.2674  | 250      | 0.0086        | -               |
+| 0.3209  | 300      | 0.0042        | -               |
+| 0.3743  | 350      | 0.0161        | -               |
+| 0.4278  | 400      | 0.0007        | -               |
+| 0.4813  | 450      | 0.0403        | -               |
+| 0.5348  | 500      | 0.0055        | -               |
+| 0.5882  | 550      | 0.0057        | -               |
+| 0.6417  | 600      | 0.0002        | -               |
+| 0.6952  | 650      | 0.0002        | -               |
+| 0.7487  | 700      | 0.0           | -               |
+| 0.8021  | 750      | 0.0026        | -               |
+| 0.8556  | 800      | 0.0002        | -               |
+| 0.9091  | 850      | 0.0002        | -               |
+| 0.9626  | 900      | 0.0004        | -               |
+| 1.0     | 935      | -             | 0.1724          |
+| 1.0160  | 950      | 0.0001        | -               |
+| 1.0695  | 1000     | 0.0006        | -               |
+| 1.1230  | 1050     | 0.0001        | -               |
+| 1.1765  | 1100     | 0.0008        | -               |
+| 1.2299  | 1150     | 0.0002        | -               |
+| 1.2834  | 1200     | 0.0001        | -               |
+| 1.3369  | 1250     | 0.0002        | -               |
+| 1.3904  | 1300     | 0.0002        | -               |
+| 1.4439  | 1350     | 0.0002        | -               |
+| 1.4973  | 1400     | 0.0002        | -               |
+| 1.5508  | 1450     | 0.0           | -               |
+| 1.6043  | 1500     | 0.0002        | -               |
+| 1.6578  | 1550     | 0.2178        | -               |
+| 1.7112  | 1600     | 0.0002        | -               |
+| 1.7647  | 1650     | 0.0001        | -               |
+| 1.8182  | 1700     | 0.0001        | -               |
+| 1.8717  | 1750     | 0.0003        | -               |
+| 1.9251  | 1800     | 0.0359        | -               |
+| 1.9786  | 1850     | 0.0001        | -               |
+| 2.0     | 1870     | -             | 0.1601          |
+| 2.0321  | 1900     | 0.0001        | -               |
+| 2.0856  | 1950     | 0.0002        | -               |
+| 2.1390  | 2000     | 0.0001        | -               |
+| 2.1925  | 2050     | 0.0001        | -               |
+| 2.2460  | 2100     | 0.0002        | -               |
+| 2.2995  | 2150     | 0.0002        | -               |
+| 2.3529  | 2200     | 0.0003        | -               |
+| 2.4064  | 2250     | 0.0001        | -               |
+| 2.4599  | 2300     | 0.0002        | -               |
+| 2.5134  | 2350     | 0.0001        | -               |
+| 2.5668  | 2400     | 0.0           | -               |
+| 2.6203  | 2450     | 0.0001        | -               |
+| 2.6738  | 2500     | 0.0           | -               |
+| 2.7273  | 2550     | 0.0001        | -               |
+| 2.7807  | 2600     | 0.0001        | -               |
+| 2.8342  | 2650     | 0.0           | -               |
+| 2.8877  | 2700     | 0.0           | -               |
+| 2.9412  | 2750     | 0.0           | -               |
+| 2.9947  | 2800     | 0.0001        | -               |
+| **3.0** | **2805** | **-**         | **0.1568**      |
+| 3.0481  | 2850     | 0.0001        | -               |
+| 3.1016  | 2900     | 0.0001        | -               |
+| 3.1551  | 2950     | 0.0001        | -               |
+| 3.2086  | 3000     | 0.0001        | -               |
+| 3.2620  | 3050     | 0.0001        | -               |
+| 3.3155  | 3100     | 0.0045        | -               |
+| 3.3690  | 3150     | 0.0           | -               |
+| 3.4225  | 3200     | 0.0001        | -               |
+| 3.4759  | 3250     | 0.0002        | -               |
+| 3.5294  | 3300     | 0.0           | -               |
+| 3.5829  | 3350     | 0.0002        | -               |
+| 3.6364  | 3400     | 0.0           | -               |
+| 3.6898  | 3450     | 0.0           | -               |
+| 3.7433  | 3500     | 0.0002        | -               |
+| 3.7968  | 3550     | 0.0           | -               |
+| 3.8503  | 3600     | 0.0           | -               |
+| 3.9037  | 3650     | 0.0005        | -               |
+| 3.9572  | 3700     | 0.0001        | -               |
+| 4.0     | 3740     | -             | 0.1574          |
+
+* The bold row denotes the saved checkpoint.
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 2.2.2
+- Transformers: 4.35.2
+- PyTorch: 2.1.0+cu121
+- Datasets: 2.16.1
+- Tokenizers: 0.15.0
+
+## 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": "checkpoints/step_2805/",
+  "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": "4.35.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 250037
+}

config_sentence_transformers.json

@@ -0,0 +1,7 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  }
+}

config_setfit.json

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

model.safetensors

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model_head.pkl

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modules.json

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+    "name": "0",
+    "path": "",
+    "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
+}

special_tokens_map.json

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+{
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+    "lstrip": false,
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tokenizer.json

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tokenizer_config.json

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+  "truncation_strategy": "longest_first",
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+}

unigram.json

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