Add SetFit model

1_Pooling/config.json

@@ -0,0 +1,7 @@
+{
+  "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
+}

README.md

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+---
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+metrics:
+- accuracy
+widget:
+- text: Nur Digital Studio
+- text: Sultanas Makeover And Training Center
+- text: Kajol Lota Restaurant
+- text: Loveria Cafe & Restaurant
+- text: Robiul And Brothers Departmental Store
+pipeline_tag: text-classification
+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: 0.48
+      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:** 17 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                                                                                                                                      |
+|:----------------|:----------------------------------------------------------------------------------------------------------------------------------------------|
+| Bank            | <ul><li>'Ific Bank Limited Sadar'</li><li>'Uttara Bank Limited Patuakhali Sadar'</li><li>'Eastern Bank Limited Uttara Branch (EBL)'</li></ul> |
+| Office          | <ul><li>'Technometrics Limited - Banani Office'</li><li>'Land Survieur Vendor Office'</li><li>'Saint Maritn Travels'</li></ul>                |
+| Religious Place | <ul><li>'Summa Ajmeri Khaja Baba Khanka Sharif'</li><li>'Baytul Mukaddas Jame Masjid'</li><li>'Paharpur Masjid'</li></ul>                     |
+| Education       | <ul><li>'Shajalal Model Madrasa'</li><li>'Physics Private Care'</li><li>'Batikadanga Primar School'</li></ul>                                 |
+| Recreation      | <ul><li>'Surjo Dighol Resort'</li><li>'Bangladesh National Monument (Sriti Soudho)'</li><li>'Eco Park Jamun'</li></ul>                        |
+| Healthcare      | <ul><li>'Nagar Shasthyo Bhaban'</li><li>'Laser Smile Dental Clinic'</li><li>'Haque Eye Care Centre'</li></ul>                                 |
+| Agricultural    | <ul><li>'Fram'</li><li>'Fruit Garden'</li></ul>                                                                                               |
+| Food            | <ul><li>'Longhorn Steak & Pizza'</li><li>'Ghati Cha'</li><li>'Banaful And Con'</li></ul>                                                      |
+| Construction    | <ul><li>'Shahjalal Sanitary'</li><li>'Modern Hardware And Paint'</li><li>'KLH Hardware'</li></ul>                                             |
+| Industry        | <ul><li>'Mka Enterprise'</li><li>'Firoz Indoor Fish Firm'</li><li>'Abdullah Industrial Park'</li></ul>                                        |
+| Government      | <ul><li>'Upazila Ansar And VDP Karjalay'</li><li>'Bof Officers Mess'</li><li>'Saheber Bazar Post Office'</li></ul>                            |
+| Transportation  | <ul><li>'Cantonment Railway Station Dhaka'</li><li>'Mosharrof Counter'</li><li>'GR Transport Agency'</li></ul>                                |
+| Shop            | <ul><li>'Kajol Watch Service'</li><li>'Glamour Parlour'</li><li>'Ma Baba Workshop'</li></ul>                                                  |
+| Residential     | <ul><li>'Tri Noyon Villa'</li><li>'Mohammad Ali Sawdagar Colony'</li><li>'Afia Cottage'</li></ul>                                             |
+| Hotel           | <ul><li>'Hotel Bondor Ga'</li><li>'Hotel Moon Moon Abashik'</li><li>'Warisan Residential Hotel'</li></ul>                                     |
+| Landmark        | <ul><li>'Rampura Bazar Moar'</li><li>'Mohipal Square'</li></ul>                                                                               |
+| Commercial      | <ul><li>'Mohammadpur Geneva Camp Kacha Bazar'</li><li>'Mohila College Bhaban'</li><li>'Singer Plus Mohammadpur'</li></ul>                     |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.48     |
+
+## 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("rafi138/setfit-paraphrase-mpnet-base-v2-type")
+# Run inference
+preds = model("Nur Digital Studio")
+```
+
+<!--
+### 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   | 3.5254 | 7   |
+
+| Label                                                                                                                                                               | Training Sample Count |
+|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------|
+| ShopCommercialGovernmentHealthcareEducationFoodOfficeReligious PlaceBankTransportationConstructionIndustryResidentialLandmarkRecreationFuelHotelUtilityAgricultural | 0                     |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (4, 4)
+- 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
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: True
+
+### Training Results
+| Epoch   | Step    | Training Loss | Validation Loss |
+|:-------:|:-------:|:-------------:|:---------------:|
+| 0.0012  | 1       | 0.2662        | -               |
+| 0.0613  | 50      | 0.2335        | -               |
+| 0.1227  | 100     | 0.1324        | -               |
+| 0.1840  | 150     | 0.1617        | -               |
+| 0.2454  | 200     | 0.0733        | -               |
+| 0.3067  | 250     | 0.0743        | -               |
+| 0.3681  | 300     | 0.0186        | -               |
+| 0.4294  | 350     | 0.0103        | -               |
+| 0.4908  | 400     | 0.0214        | -               |
+| 0.5521  | 450     | 0.0042        | -               |
+| 0.6135  | 500     | 0.0062        | -               |
+| 0.6748  | 550     | 0.0027        | -               |
+| 0.7362  | 600     | 0.0021        | -               |
+| 0.7975  | 650     | 0.0014        | -               |
+| 0.8589  | 700     | 0.0016        | -               |
+| 0.9202  | 750     | 0.0059        | -               |
+| 0.9816  | 800     | 0.0009        | -               |
+| **1.0** | **815** | **-**         | **0.2969**      |
+| 1.0429  | 850     | 0.0008        | -               |
+| 1.1043  | 900     | 0.0014        | -               |
+| 1.1656  | 950     | 0.0008        | -               |
+| 1.2270  | 1000    | 0.001         | -               |
+| 1.2883  | 1050    | 0.001         | -               |
+| 1.3497  | 1100    | 0.0017        | -               |
+| 1.4110  | 1150    | 0.0007        | -               |
+| 1.4724  | 1200    | 0.0006        | -               |
+| 1.5337  | 1250    | 0.0008        | -               |
+| 1.5951  | 1300    | 0.0006        | -               |
+| 1.6564  | 1350    | 0.0005        | -               |
+| 1.7178  | 1400    | 0.0005        | -               |
+| 1.7791  | 1450    | 0.001         | -               |
+| 1.8405  | 1500    | 0.0005        | -               |
+| 1.9018  | 1550    | 0.0006        | -               |
+| 1.9632  | 1600    | 0.0005        | -               |
+| 2.0     | 1630    | -             | 0.3073          |
+| 2.0245  | 1650    | 0.0007        | -               |
+| 2.0859  | 1700    | 0.0016        | -               |
+| 2.1472  | 1750    | 0.0006        | -               |
+| 2.2086  | 1800    | 0.0008        | -               |
+| 2.2699  | 1850    | 0.0006        | -               |
+| 2.3313  | 1900    | 0.0005        | -               |
+| 2.3926  | 1950    | 0.0009        | -               |
+| 2.4540  | 2000    | 0.0008        | -               |
+| 2.5153  | 2050    | 0.0004        | -               |
+| 2.5767  | 2100    | 0.0005        | -               |
+| 2.6380  | 2150    | 0.0005        | -               |
+| 2.6994  | 2200    | 0.0009        | -               |
+| 2.7607  | 2250    | 0.0006        | -               |
+| 2.8221  | 2300    | 0.0008        | -               |
+| 2.8834  | 2350    | 0.0004        | -               |
+| 2.9448  | 2400    | 0.0004        | -               |
+| 3.0     | 2445    | -             | 0.3198          |
+| 3.0061  | 2450    | 0.0003        | -               |
+| 3.0675  | 2500    | 0.0004        | -               |
+| 3.1288  | 2550    | 0.0002        | -               |
+| 3.1902  | 2600    | 0.0003        | -               |
+| 3.2515  | 2650    | 0.0004        | -               |
+| 3.3129  | 2700    | 0.0005        | -               |
+| 3.3742  | 2750    | 0.0003        | -               |
+| 3.4356  | 2800    | 0.0003        | -               |
+| 3.4969  | 2850    | 0.0005        | -               |
+| 3.5583  | 2900    | 0.0006        | -               |
+| 3.6196  | 2950    | 0.0005        | -               |
+| 3.6810  | 3000    | 0.0007        | -               |
+| 3.7423  | 3050    | 0.0004        | -               |
+| 3.8037  | 3100    | 0.0003        | -               |
+| 3.8650  | 3150    | 0.0005        | -               |
+| 3.9264  | 3200    | 0.0003        | -               |
+| 3.9877  | 3250    | 0.0007        | -               |
+| 4.0     | 3260    | -             | 0.3176          |
+
+* The bold row denotes the saved checkpoint.
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 2.2.2
+- Transformers: 4.36.2
+- PyTorch: 2.1.2+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

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+{
+  "_name_or_path": "checkpoints/step_815/",
+  "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": "4.36.2",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  }
+}

config_setfit.json

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+{
+  "labels": [
+    "ShopCommercialGovernmentHealthcareEducationFoodOfficeReligious PlaceBankTransportationConstructionIndustryResidentialLandmarkRecreationFuelHotelUtilityAgricultural"
+  ],
+  "normalize_embeddings": false
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bd1773f5fd2f194efddbd2719668e66db3d716fbedb9283623c4413a6f18c3de
+size 437967672

model_head.pkl

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+oid sha256:ea13d8379d11439103aee052643d1978cc5c31b1ab881ae4e47aac094adb6515
+size 106447

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
+    },
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+      "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": true,
+  "cls_token": "<s>",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "max_length": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}