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": 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 +1,218 @@
+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: '"ഒരുപാട് ഇഷ്ട്ടപെട്ട പോലെ ഒരുപാട് വെറുത്ത് പോയി, ഡോക്ടറെ കിട്ടാനുള്ള ഭാഗ്യം
+    ഇല്ല"'
+- text: ഒരു കരുണയും ഇല്ലാതെ ഒരാളുടെ ഫീലിംഗ് വെച്ച് കളിക്കുക... ആത്മാർഥതക്ക് ഒരു വിലയും
+    കൊടുക്കാതെ അയാളെ തൂത്തെറിയുക.... എല്ലാവർക്കും എല്ലാം മനസ്സിൽ ആയി.. ഇനി കൂടുതൽ
+    ഒന്നും പറയണ്ട...
+- text: சொத்துக்கே வழி இல்லாம இங்க கஸ்டப்பற்றாங்க இதுங்களுக்கு அறிப்பு பிடிச்சி அலைதுங்க
+    தூ தூ மூதேவிங்களா
+- text: അടിപൊളി കൊള്ളാം സൂപ്പർ ഡയലോഗ് ആരാണ് ഈ അമ്മമാരും ചേച്ചിമാരും ചേട്ടന്മാരും ഞങ്ങൾക്കറിയില്ല
+- text: 24×7 എല്ലാം കാണണം കേട്ടോ. അപ്പോൾ അറിയാം. അവിടെ എന്തൊക്കെയാ കാട്ടികൂട്ടിയതെന്ന്.
+    എങ്കിലുംഡോക്റ്റരോട് നീ ഇത് ചെയ്യും എന്ന് ഞങ്ങൾ ആരും കരുതിയില്ല.
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: true
+base_model: microsoft/Multilingual-MiniLM-L12-H384
+model-index:
+- name: SetFit with microsoft/Multilingual-MiniLM-L12-H384
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.6875
+      name: Accuracy
+---
+
+# SetFit with microsoft/Multilingual-MiniLM-L12-H384
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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:** [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384)
+- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
+- **Maximum Sequence Length:** 512 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                                                                                                                                                                                                                                                                |
+|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 0     | <ul><li>'Madam divyaக்கு 1கிலோ colgate paste வாங்கி கொடுங்க videoவில் வாய் நாற்றம் தாங்கல'</li><li>'ഇനി ഇതുപോലുള്ള സാദനം ആയി വന്നാൽ ഞാൻ ഡിസ്ക്രൈബ് ചെയ്യും'</li><li>'ஏன்பா behindwoods தயவு செய்து இப்படி கேவலமான programme ஐ telecast பண்ணாதீங்க ராஜா'</li></ul>       |
+| 1     | <ul><li>'கம்பிய பழுக்க வச்சு சூத்துல வைங்க சார்'</li><li>'ഇനി റെഡ് സ്ട്ര���റ്റ്റിലും കൂടി പോയി ഇന്റർവ്യൂ എടുക്ക് ചേച്ചി'</li><li>'നിങ്ങൾ പണ്ടേ വിവരക്കേടാണ്. ബോധം ഇല്ലായ്മ കാണിക്കാതെ സ്ത്രീ. മറ്റുള്ളവരുടെ കിഡ്ണി കളയിപ്പിച്ചിട്ടുവേണോ നിന്റെ കഞ്ഞി കുടിക്കൽ.'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.6875   |
+
+## 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("livinNector/m-minilm-l12-h384-dra-tam-mal-aw-setfit-finetune")
+# Run inference
+preds = model("\"ഒരുപാട് ഇഷ്ട്ടപെട്ട പോലെ ഒരുപാട് വെറുത്ത് പോയി, ഡോക്ടറെ കിട്ടാനുള്ള ഭാഗ്യം ഇല്ല\"")
+```
+
+<!--
+### 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   | 2   | 15.4375 | 123 |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 132                   |
+| 1     | 124                   |
+
+### Training Hyperparameters
+- batch_size: (64, 64)
+- num_epochs: (10, 10)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 2
+- body_learning_rate: (2e-05, 1e-05)
+- head_learning_rate: 0.01
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: True
+- use_amp: False
+- warmup_proportion: 0.1
+- l2_weight: 0.01
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: True
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0625 | 1    | 0.422         | -               |
+| 0.625  | 10   | -             | 0.4029          |
+| 1.25   | 20   | -             | 0.2799          |
+| 1.875  | 30   | -             | 0.2464          |
+| 2.5    | 40   | -             | 0.2480          |
+| 3.125  | 50   | 0.2964        | 0.2451          |
+| 3.75   | 60   | -             | 0.2368          |
+| 4.375  | 70   | -             | 0.2444          |
+| 5.0    | 80   | -             | 0.2393          |
+| 5.625  | 90   | -             | 0.2382          |
+| 6.25   | 100  | 0.1825        | 0.2395          |
+| 6.875  | 110  | -             | 0.2405          |
+| 7.5    | 120  | -             | 0.2424          |
+| 8.125  | 130  | -             | 0.2468          |
+| 8.75   | 140  | -             | 0.2432          |
+| 9.375  | 150  | 0.1308        | 0.2451          |
+| 10.0   | 160  | -             | 0.2454          |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.1.0
+- Sentence Transformers: 3.3.1
+- Transformers: 4.45.2
+- PyTorch: 2.5.1+cu121
+- Datasets: 3.2.0
+- Tokenizers: 0.20.3
+
+## 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": "microsoft/Multilingual-MiniLM-L12-H384",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "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",
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "torch_dtype": "float32",
+  "transformers_version": "4.45.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 250037
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.45.2",
+    "pytorch": "2.5.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

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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+  {
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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": 512,
+  "do_lower_case": false
+}

sentencepiece.bpe.model

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special_tokens_map.json

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+}

tokenizer.json

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tokenizer_config.json

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+  "sp_model_kwargs": {},
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+}