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

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+{
+  "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 +1,229 @@
+---
+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: Bir öğrenci her gün 5 sayfa kitap okuyor. 5 gün süresince kaç sayfa kitap
+    okur?
+- text: Bir inşaat firmasında 74 işçi vardı, Firma 29 işçi daha işe aldı. Firmada
+    şimdi kaç işçi var?
+- text: If you have 4 coins that total 35 cents, and 3 of them are dimes, what is
+    the other coin?
+- text: Bir üçgenin tabanı 12 cm, yüksekliği 8 cm'dir. Üçgenin alanı kaç cm²'dir?
+- text: Bir üçgenin kenar uzunlukları 8 cm, 10 cm ve 12 cm'dir. Üçgenin çevresi kaç
+    cm'dir?
+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: 1.0
+      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                                                                                                                                                                                                                                    |
+|:---------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| non_math | <ul><li>'Güneş sisteminde kaç gezegen vardır?'</li><li>'What is the largest ocean on Earth?'</li><li>'What is the intake of energy and nutrients by living organisms called?'</li></ul>                                                     |
+| math     | <ul><li>"Bir düzensiz çokgenin kenar uzunlukları 5 cm, 8 cm, 7 cm ve 6 cm'dir. Çokgenin çevresi kaç cm'dir?"</li><li>'27 ÷ 3 = ?'</li><li>'Bir altıgenin bir iç açısının ölçüsü 120° ise, tüm iç açıların toplamı kaç derecedir?'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 1.0      |
+
+## 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("serdarcaglar/primary-school-math-question-multi-lang")
+# Run inference
+preds = model("Bir üçgenin tabanı 12 cm, yüksekliği 8 cm'dir. Üçgenin alanı kaç cm²'dir?")
+```
+
+<!--
+### 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   | 10.3435 | 33  |
+
+| Label    | Training Sample Count |
+|:---------|:----------------------|
+| math     | 459                   |
+| non_math | 129                   |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (1, 1)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 20
+- body_learning_rate: (2e-05, 2e-05)
+- head_learning_rate: 2e-05
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0007 | 1    | 0.1982        | -               |
+| 0.0340 | 50   | 0.1009        | -               |
+| 0.0680 | 100  | 0.0054        | -               |
+| 0.1020 | 150  | 0.002         | -               |
+| 0.1361 | 200  | 0.001         | -               |
+| 0.1701 | 250  | 0.0023        | -               |
+| 0.2041 | 300  | 0.0002        | -               |
+| 0.2381 | 350  | 0.0013        | -               |
+| 0.2721 | 400  | 0.0004        | -               |
+| 0.3061 | 450  | 0.0004        | -               |
+| 0.3401 | 500  | 0.0002        | -               |
+| 0.3741 | 550  | 0.0001        | -               |
+| 0.4082 | 600  | 0.0001        | -               |
+| 0.4422 | 650  | 0.0002        | -               |
+| 0.4762 | 700  | 0.0001        | -               |
+| 0.5102 | 750  | 0.0001        | -               |
+| 0.5442 | 800  | 0.0002        | -               |
+| 0.5782 | 850  | 0.0002        | -               |
+| 0.6122 | 900  | 0.0001        | -               |
+| 0.6463 | 950  | 0.0005        | -               |
+| 0.6803 | 1000 | 0.0001        | -               |
+| 0.7143 | 1050 | 0.0001        | -               |
+| 0.7483 | 1100 | 0.0001        | -               |
+| 0.7823 | 1150 | 0.0001        | -               |
+| 0.8163 | 1200 | 0.0001        | -               |
+| 0.8503 | 1250 | 0.0001        | -               |
+| 0.8844 | 1300 | 0.0           | -               |
+| 0.9184 | 1350 | 0.0002        | -               |
+| 0.9524 | 1400 | 0.0001        | -               |
+| 0.9864 | 1450 | 0.0001        | -               |
+
+### 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 +1,26 @@
+{
+  "_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

@@ -0,0 +1,10 @@
+{
+  "__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

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

model.safetensors

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model_head.pkl

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modules.json

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+  {
+    "idx": 0,
+    "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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tokenizer.json

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tokenizer_config.json

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+  "do_lower_case": true,
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+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}

unigram.json

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