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 +1,241 @@
+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: Mujhe apne galtiyon ka ehsaas hai aur main unke liye maafi chahta hoon.
+- text: Mujhe yeh step samajhne mein dikkat ho rahi hai, kya aap madad kar sakte hain?
+- text: Mujhe abhi tak kuch update kyun nahi mila, yeh bahut frustrating hai.
+- text: Is app ka loading time mujhe thoda zyada lagta hai.
+- text: Kya aap mujhe is event ki timing bata sakte hain?
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: true
+base_model: MoritzLaurer/mDeBERTa-v3-base-mnli-xnli
+model-index:
+- name: SetFit with MoritzLaurer/mDeBERTa-v3-base-mnli-xnli
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.32
+      name: Accuracy
+---
+
+# SetFit with MoritzLaurer/mDeBERTa-v3-base-mnli-xnli
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) 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:** [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Classes:** 19 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                                                                                                                                                                                                                                           |
+|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 4     | <ul><li>'Yeh rahin wo steps jisse aap apni payment kar sakte hain.'</li><li>'Kya aap mujhe yeh batane ka tarika thoda aasan kar sakte hain?'</li><li>'Is option ke madhyam se aap apni queries kaise solve kar sakte hain, jaan lijiye.'</li></ul> |
+| 16    | <ul><li>'Aapke feedback ko humne dhyan mein rakha hai.'</li><li>'Yeh galti humare systems ki wajah se hui hai.'</li><li>'Mujhe is samasya ko suljhane mein zyada samay lena nahi chahiye tha.'</li></ul>                                           |
+| 8     | <ul><li>'Main aapko pareshan karne ke liye maafi chahta hoon.'</li><li>'Humein is samasya ke liye maafi chahiye.'</li><li>'Mere kaam se agar aapko takleef hui ho, toh mujhe maaf kar dijiye.'</li></ul>                                           |
+| 13    | <ul><li>'Mujhe yeh clarify karne ki zarurat hai ki agla step kya hai?'</li><li>'Mujhe pata karna hai ki maine jo complaint ki thi uska kya hua.'</li><li>'Mujhe bataye ki pehle kitne payments honge iss plan ke liye.'</li></ul>                  |
+| 15    | <ul><li>'Yeh features sahi hai, lekin kuch aur additional functionalities honi chahiye.'</li><li>'Product ke size ki jankari hamesha saaf honi chahiye.'</li><li>'Main chahunga ki online form aur simple ho.'</li></ul>                           |
+| 12    | <ul><li>'Mujhe product ke sath kuch samasya hai.'</li><li>'Mera phone charging nahi ho raha.'</li><li>'Mujhe courier service mein dikkat hai, report karna hai.'</li></ul>                                                                         |
+| 11    | <ul><li>'Mujhe samajh nahi aa raha, is offer mein koi chhupi shartein toh nahi hai?'</li><li>'Kis tarah se main feedback de sakta hoon?'</li><li>'Kya koi referral program hai jo mujhe join karna chahiye?'</li></ul>                             |
+| 2     | <ul><li>'Item ke sath saathi accessories nahi mil rahe hain.'</li><li>'Aap logon ne jo samay liya, wo bilkul zyada tha.'</li><li>'Meri order delivery mein bahut der ho gayi hai.'</li></ul>                                                       |
+| 18    | <ul><li>'Mujhe yeh bilkul pasand nahi hai ki meri baat ignore ki gayi.'</li><li>'Kam ke liye mera dosto ka support bahut sukhdayak hai.'</li><li>'Aaj ka din kaafi udaas beete raha hai.'</li></ul>                                                |
+| 14    | <ul><li>'Kya main kal ki delivery ko agle hafte reschedule kar sakta/sakti hoon?'</li><li>'Mujhe refund ke liye kya documents chahiye?'</li><li>'Kya main appointment ko dobara set kar sakta/sakti hoon?'</li></ul>                               |
+| 7     | <ul><li>'Main aapko dhanyavad dena chahta hoon, aapne meri madad ki.'</li><li>'Aapne jo kiya, uske liye aapko sabse pehle prashansha milni chahiye.'</li><li>'Aapka samay dene ke liye abhaar.'</li></ul>                                          |
+| 3     | <ul><li>'Mujhe kisi event ke tickets ka status check karna hai.'</li><li>'Kya aap mujhe customer support number de sakte hain?'</li><li>'Main apne account ka balance kaise check kar sakta/sakti hoon?'</li></ul>                                 |
+| 5     | <ul><li>'Alvida, tumhara din acha rahe!'</li><li>'Hello! Aaj aap kaise hain?'</li><li>'Swagat hai! Kya main aapki kuch madad kar sakta hoon?'</li></ul>                                                                                            |
+| 0     | <ul><li>'Mujhe kuch samajh nahi aa raha hai, kya mujhe thoda aur samjha sakte hain?'</li><li>'Agar main aisa karoon, to kya kuch badal jaayega? Main sure nahi hoon.'</li><li>'Yeh product ki warranty ki details clear nahi hain.'</li></ul>      |
+| 6     | <ul><li>'Chalo, alvida bolte hain!'</li><li>'Phir se baat karte hain!'</li><li>'Adieu, aapka din shubh ho!'</li></ul>                                                                                                                              |
+| 17    | <ul><li>'Mere account mein login karne mein dikkat aa rahi hai, madad karein.'</li><li>'Mujhe apne account mein login karne mein madad chahiye.'</li><li>'Kya aap mujhe terms and conditions ke details de sakte hain?'</li></ul>                  |
+| 10    | <ul><li>'Main aapki baat se sehmat hoon.'</li><li>'Mujhe yeh batayein ki meri booking sahi hai na?'</li></ul>                                                                                                                                      |
+| 9     | <ul><li>'Kya aap mujhe yeh concept aur clear kar sakte hain?'</li><li>'Mujhe yeh samajhne mein dikkat ho rahi hai, kya aap vyakhya de sakte hain?'</li></ul>                                                                                       |
+| 1     | <ul><li>'Aaj dosto ke sath waqt bitana bahut acha laga.'</li><li>'Aaj baarish me bheegna bahut refreshing tha, mujhe yeh moment pasand aaya.'</li><li>'Aapka support bahut madadgar raha.'</li></ul>                                               |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.32     |
+
+## 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("setfit_model_id")
+# Run inference
+preds = model("Kya aap mujhe is event ki timing bata sakte hain?")
+```
+
+<!--
+### 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   | 3   | 9.76   | 15  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 6                     |
+| 1     | 3                     |
+| 2     | 3                     |
+| 3     | 5                     |
+| 4     | 7                     |
+| 5     | 3                     |
+| 6     | 6                     |
+| 7     | 8                     |
+| 8     | 6                     |
+| 9     | 2                     |
+| 10    | 2                     |
+| 11    | 5                     |
+| 12    | 6                     |
+| 13    | 5                     |
+| 14    | 9                     |
+| 15    | 9                     |
+| 16    | 9                     |
+| 17    | 3                     |
+| 18    | 3                     |
+
+### 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
+- l2_weight: 0.01
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0017 | 1    | 0.2335        | -               |
+| 0.0853 | 50   | 0.2514        | -               |
+| 0.1706 | 100  | 0.1619        | -               |
+| 0.2560 | 150  | 0.1124        | -               |
+| 0.3413 | 200  | 0.078         | -               |
+| 0.4266 | 250  | 0.0623        | -               |
+| 0.5119 | 300  | 0.0576        | -               |
+| 0.5973 | 350  | 0.0421        | -               |
+| 0.6826 | 400  | 0.0391        | -               |
+| 0.7679 | 450  | 0.0386        | -               |
+| 0.8532 | 500  | 0.0302        | -               |
+| 0.9386 | 550  | 0.0245        | -               |
+
+### Framework Versions
+- Python: 3.10.16
+- SetFit: 1.1.1
+- Sentence Transformers: 3.3.1
+- Transformers: 4.46.3
+- PyTorch: 2.5.1+cpu
+- 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.*
+-->

added_tokens.json

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config.json

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+  "architectures": [
+    "DebertaV2Model"
+  ],
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+  "hidden_act": "gelu",
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+}

config_sentence_transformers.json

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+    "sentence_transformers": "3.3.1",
+    "transformers": "4.46.3",
+    "pytorch": "2.5.1+cpu"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

config_setfit.json

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sentence_bert_config.json

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special_tokens_map.json

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