Add SetFit model

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 384,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "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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+---
+base_model: BAAI/bge-small-en-v1.5
+library_name: setfit
+metrics:
+- accuracy
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: What’s the total number of orders placed by each customer?
+- text: I like to read books and listen to music in my free time. How about you?
+- text: Get company-wise intangible asset ratio.
+- text: Show me data_asset_001_ta by product.
+- text: Show me average asset value.
+inference: true
+model-index:
+- name: SetFit with BAAI/bge-small-en-v1.5
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.9915254237288136
+      name: Accuracy
+---
+
+# SetFit with BAAI/bge-small-en-v1.5
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) 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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
+- **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:** 7 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                                                                                                                                                                                                                                                                                                                             |
+|:-------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Aggregation  | <ul><li>'Please show med CostVariance_Actual_vs_Forecast.'</li><li>'Get me data_asset_001_kpm group by metrics.'</li><li>'Provide data_asset_kpi_cf group by quarter.'</li></ul>                                                                                                                                                     |
+| Tablejoin    | <ul><li>'Join data_asset_kpi_cf with data_asset_001_kpm tables.'</li><li>'Could you link the Products and Orders tables to track sales trends for different product categories?'</li><li>'Can I have a merge of income statement and key performance metrics tables?'</li></ul>                                                      |
+| Lookup       | <ul><li>"Filter by the 'Sales' department and show me the employees."</li><li>"Filter by the 'Toys' category and get me the product names."</li><li>'Can you get me the products with a price above 100?'</li></ul>                                                                                                                  |
+| Rejection    | <ul><li>"Let's avoid generating additional reports."</li><li>"I'd rather not filter this dataset."</li><li>"I'd prefer not to apply any filters."</li></ul>                                                                                                                                                                          |
+| Lookup_1     | <ul><li>'Show me key income statement metrics.'</li><li>'can I have kpm table'</li><li>'Retrieve data_asset_kpi_ma_product records.'</li></ul>                                                                                                                                                                                       |
+| Generalreply | <ul><li>"Hey! It's going pretty well, thanks for asking. How about yours?"</li><li>'Not much, just taking it one day at a time. How about you?'</li><li>"'What is your favorite quote?'"</li></ul>                                                                                                                                   |
+| Viewtables   | <ul><li>'What are the table names that relate to customer service in the starhub_data_asset database?'</li><li>'What tables are available in the starhub_data_asset database that can be joined to track user behavior?'</li><li>'What are the tables that are available for analysis in the starhub_data_asset database?'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.9915   |
+
+## 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("nazhan/bge-small-en-v1.5-brahmaputra-iter-10-3rd")
+# Run inference
+preds = model("Show me average asset value.")
+```
+
+<!--
+### 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   | 8.7839 | 62  |
+
+| Label        | Training Sample Count |
+|:-------------|:----------------------|
+| Tablejoin    | 127                   |
+| Rejection    | 76                    |
+| Aggregation  | 281                   |
+| Lookup       | 59                    |
+| Generalreply | 71                    |
+| Viewtables   | 75                    |
+| Lookup_1     | 158                   |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (1, 1)
+- max_steps: 2450
+- 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 |
+|:----------:|:--------:|:-------------:|:---------------:|
+| 0.0000     | 1        | 0.2317        | -               |
+| 0.0025     | 50       | 0.2478        | -               |
+| 0.0050     | 100      | 0.2213        | -               |
+| 0.0075     | 150      | 0.0779        | -               |
+| 0.0100     | 200      | 0.1089        | -               |
+| 0.0125     | 250      | 0.0372        | -               |
+| 0.0149     | 300      | 0.0219        | -               |
+| 0.0174     | 350      | 0.0344        | -               |
+| 0.0199     | 400      | 0.012         | -               |
+| 0.0224     | 450      | 0.0049        | -               |
+| 0.0249     | 500      | 0.0041        | -               |
+| 0.0274     | 550      | 0.0083        | -               |
+| 0.0299     | 600      | 0.0057        | -               |
+| 0.0324     | 650      | 0.0047        | -               |
+| 0.0349     | 700      | 0.0022        | -               |
+| 0.0374     | 750      | 0.0015        | -               |
+| 0.0399     | 800      | 0.0032        | -               |
+| 0.0423     | 850      | 0.002         | -               |
+| 0.0448     | 900      | 0.0028        | -               |
+| 0.0473     | 950      | 0.0017        | -               |
+| 0.0498     | 1000     | 0.0017        | -               |
+| 0.0523     | 1050     | 0.0027        | -               |
+| 0.0548     | 1100     | 0.0022        | -               |
+| 0.0573     | 1150     | 0.0018        | -               |
+| 0.0598     | 1200     | 0.001         | -               |
+| 0.0623     | 1250     | 0.002         | -               |
+| 0.0648     | 1300     | 0.001         | -               |
+| 0.0673     | 1350     | 0.0013        | -               |
+| 0.0697     | 1400     | 0.0012        | -               |
+| 0.0722     | 1450     | 0.0018        | -               |
+| 0.0747     | 1500     | 0.0012        | -               |
+| 0.0772     | 1550     | 0.0016        | -               |
+| 0.0797     | 1600     | 0.0012        | -               |
+| 0.0822     | 1650     | 0.0016        | -               |
+| 0.0847     | 1700     | 0.0027        | -               |
+| 0.0872     | 1750     | 0.0014        | -               |
+| 0.0897     | 1800     | 0.0011        | -               |
+| 0.0922     | 1850     | 0.0011        | -               |
+| 0.0947     | 1900     | 0.0012        | -               |
+| 0.0971     | 1950     | 0.0014        | -               |
+| 0.0996     | 2000     | 0.0014        | -               |
+| 0.1021     | 2050     | 0.0015        | -               |
+| 0.1046     | 2100     | 0.0009        | -               |
+| 0.1071     | 2150     | 0.0015        | -               |
+| 0.1096     | 2200     | 0.0013        | -               |
+| 0.1121     | 2250     | 0.0013        | -               |
+| 0.1146     | 2300     | 0.001         | -               |
+| 0.1171     | 2350     | 0.0017        | -               |
+| 0.1196     | 2400     | 0.0013        | -               |
+| **0.1221** | **2450** | **0.0008**    | **0.0323**      |
+
+* The bold row denotes the saved checkpoint.
+### Framework Versions
+- Python: 3.11.9
+- SetFit: 1.0.3
+- Sentence Transformers: 2.7.0
+- Transformers: 4.42.4
+- PyTorch: 2.4.0+cu121
+- Datasets: 2.21.0
+- Tokenizers: 0.19.1
+
+## 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_2450",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "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": "4.42.4",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.2.2",
+    "transformers": "4.28.1",
+    "pytorch": "1.13.0+cu117"
+  },
+  "prompts": {},
+  "default_prompt_name": null
+}

config_setfit.json

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+{
+  "labels": [
+    "Tablejoin",
+    "Rejection",
+    "Aggregation",
+    "Lookup",
+    "Generalreply",
+    "Viewtables",
+    "Lookup_1"
+  ],
+  "normalize_embeddings": false
+}

model.safetensors

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+oid sha256:53921010e01a1324daf8ce5823e4646507a52cec2aaa309b45a4d38186766241
+size 133462128

model_head.pkl

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+oid sha256:ae0c54974c6c2828dc12852ec58658f8f106c1956c86f457816094a8402bff1c
+size 22735

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"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": true
+}

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "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": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "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
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+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "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": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}