Add SetFit model

1_Pooling/config.json

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+{
+  "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

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+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: '"e-Allahabad Journey loan application workflow?"'
+- text: '"Relief Bonds redemption during OD tenure?"'
+- text: '"Chief General Managers'' discretionary powers?"'
+- text: '"Digital Journey e-Allahabad nominee update steps?"'
+- text: '"SGB partial withdrawal during loan period?"'
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: true
+base_model: sentence-transformers/paraphrase-mpnet-base-v2
+model-index:
+- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.975
+      name: Accuracy
+---
+
+# SetFit with sentence-transformers/paraphrase-mpnet-base-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
+- **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:** 10 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                                                                                                                                                                                                  |
+|:-----------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Disclaimer                                                       | <ul><li>'"Terms of Use API restrictions?"'</li><li>'"Terms of Use age restrictions?"'</li><li>'"Disclaimer update alerts?"'</li></ul>                                                                     |
+| IB Loan against Sovereign Gold Bond                              | <ul><li>'"Sovereign Jewel Bond loan margin requirements?"'</li><li>'"Sovereign Gold Bond joint holder rules?"'</li><li>'"Sovereign Jewel Bond nomination process?"'</li></ul>                             |
+| Ind Advantage (Reward Program)                                   | <ul><li>'"Advantage Rewards international redemption fees?"'</li><li>'"Blackout dates for reward travel bookings?"'</li><li>'"Advantage Program customer support channels?"'</li></ul>                    |
+| Amalgamation                                                     | <ul><li>'"Merger documentation checklist for branches?"'</li><li>'"Banking Amalgamation customer notification process?"'</li><li>'"Amalgamation loan portfolio transfer details?"'</li></ul>              |
+| Loan / OD against NSC / KVP / Relief bonds of RBI / LIC policies | <ul><li>'"Relief Bonds OD interest payment frequency?"'</li><li>'"KVP valuation for overdraft approval criteria?"'</li><li>'"NSC loan documentation checklist?"'</li></ul>                                |
+| Chief General Managers                                           | <ul><li>'"Chief General Managers\' office working hours?"'</li><li>'"How to contact Chief General Managers for escalations?"'</li><li>'"Senior General Managers\' regional jurisdiction list?"'</li></ul> |
+| Point of Sale (PoS)                                              | <ul><li>'"Offline PoS transaction capabilities?"'</li><li>'"PoS transaction audit trails?"'</li><li>'"PoS batch settlement timing?"'</li></ul>                                                            |
+| Featured Products / Services / Schemes                           | <ul><li>'"Highlighted Products insurance coverage details?"'</li><li>'"Highlighted Products loan-to-value ratio?"'</li><li>'"Featured schemes disbursement timeline?"'</li></ul>                          |
+| e-Allahabad Bank Journey                                         | <ul><li>'"e-Allahabad Experience customer support channels?"'</li><li>'"Allahabad Online Journey QR code payments?"'</li><li>'"Allahabad Online Journey statement download process?"'</li></ul>           |
+|  Centralized Pension Processing Centre                           | <ul><li>'"Processing time for pension applications?"'</li><li>'"QR code payments at Payment Office?"'</li><li>'"Central Pension Management Centre contact details?"'</li></ul>                            |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.975    |
+
+## 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("kneau007/my-classifier")
+# Run inference
+preds = model("\"Relief Bonds redemption during OD tenure?\"")
+```
+
+<!--
+### 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   | 5.2062 | 8   |
+
+| Label                                                            | Training Sample Count |
+|:-----------------------------------------------------------------|:----------------------|
+| Amalgamation                                                     | 14                    |
+| Chief General Managers                                           | 16                    |
+| Disclaimer                                                       | 11                    |
+| Featured Products / Services / Schemes                           | 18                    |
+| IB Loan against Sovereign Gold Bond                              | 18                    |
+| Ind Advantage (Reward Program)                                   | 19                    |
+| Loan / OD against NSC / KVP / Relief bonds of RBI / LIC policies | 16                    |
+| Point of Sale (PoS)                                              | 16                    |
+| e-Allahabad Bank Journey                                         | 15                    |
+|  Centralized Pension Processing Centre                           | 17                    |
+
+### 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
+- l2_weight: 0.01
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0025 | 1    | 0.172         | -               |
+| 0.125  | 50   | 0.1198        | -               |
+| 0.25   | 100  | 0.0251        | -               |
+| 0.375  | 150  | 0.0068        | -               |
+| 0.5    | 200  | 0.003         | -               |
+| 0.625  | 250  | 0.0018        | -               |
+| 0.75   | 300  | 0.0015        | -               |
+| 0.875  | 350  | 0.0013        | -               |
+| 1.0    | 400  | 0.0013        | -               |
+
+### Framework Versions
+- Python: 3.11.11
+- SetFit: 1.1.1
+- Sentence Transformers: 3.4.1
+- Transformers: 4.48.3
+- PyTorch: 2.5.1+cu124
+- Datasets: 3.3.2
+- Tokenizers: 0.21.0
+
+## 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

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+{
+  "_name_or_path": "sentence-transformers/paraphrase-mpnet-base-v2",
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.3",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.48.3",
+    "pytorch": "2.5.1+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

config_setfit.json

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+{
+  "normalize_embeddings": false,
+  "labels": [
+    "Amalgamation",
+    "Chief General Managers",
+    "Disclaimer",
+    "Featured Products / Services / Schemes",
+    "IB Loan against Sovereign Gold Bond",
+    "Ind Advantage (Reward Program)",
+    "Loan / OD against NSC / KVP / Relief bonds of RBI / LIC policies",
+    "Point of Sale (PoS)",
+    "e-Allahabad Bank Journey",
+    "\u00a0Centralized Pension Processing Centre"
+  ]
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:13a0675b2dce7ba6d013cd17d0953fd9bdb2b90a4dc886a780f611b15ba2b82f
+size 437967672

model_head.pkl

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+oid sha256:da06733b12907c4bb5bc78dd0a319a0b3936228fc03354872731df911615f8ba
+size 64919

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

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "104": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "30526": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "unk_token": "[UNK]"
+}