Add SetFit model

1_Pooling/config.json

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+{
+ "word_embedding_dimension": 1024,
+ "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-large-en-v1.5
+datasets:
+- nazhan/qa-lookup-dataset-iter-1
+library_name: setfit
+metrics:
+- accuracy
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: Get me the first names of employees working in the 'Legal' department.
+- text: Provide the value of the export tariff paid on shipments to 'Country Z' in
+ 2024.
+- text: Show me the value of the freight charges for the shipment made on October
+ 10, 2023.
+- text: Show me the value of the refund issued to 'Customer K' for a defective product.
+- text: Provide the value of the environmental compliance cost for 2023.
+inference: true
+model-index:
+- name: SetFit with BAAI/bge-large-en-v1.5
+ results:
+ - task:
+ type: text-classification
+ name: Text Classification
+ dataset:
+ name: nazhan/qa-lookup-dataset-iter-1
+ type: nazhan/qa-lookup-dataset-iter-1
+ split: test
+ metrics:
+ - type: accuracy
+ value: 1.0
+ name: Accuracy
+---
+
+# SetFit with BAAI/bge-large-en-v1.5
+
+This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [nazhan/qa-lookup-dataset-iter-1](https://huggingface.co/datasets/nazhan/qa-lookup-dataset-iter-1) dataset that can be used for Text Classification. This SetFit model uses [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-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-large-en-v1.5](https://huggingface.co/BAAI/bge-large-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:** 2 classes
+- **Training Dataset:** [nazhan/qa-lookup-dataset-iter-1](https://huggingface.co/datasets/nazhan/qa-lookup-dataset-iter-1)
+<!-- - **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 |
+|:-------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Lookup | <ul><li>'Get me the list of customers who placed their first order in 2024.'</li><li>"Filter by products in the 'Gadgets' category and show me their prices."</li><li>'Get me the email addresses of customers who have made a purchase.'</li></ul> |
+| qa | <ul><li>'Provide the value of the accrued vacation liability as of June 2023.'</li><li>'Show me the value of the courier service charges for November 2023.'</li><li>"Provide the value of the consulting contract with 'Client N' finalized in 2023."</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label | Accuracy |
+|:--------|:---------|
+| **all** | 1.0 |
+
+## 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-large-en-v1.5-brahmaputra-qa-lookup-iter-1-2-epoch")
+# Run inference
+preds = model("Provide the value of the environmental compliance cost for 2023.")
+```
+
+<!--
+### 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 | 8 | 12.8309 | 19 |
+
+| Label | Training Sample Count |
+|:-------|:----------------------|
+| Lookup | 65 |
+| qa | 71 |
+
+### Training Hyperparameters
+- batch_size: (32, 32)
+- num_epochs: (2, 2)
+- 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 |
+|:-------:|:-------:|:-------------:|:---------------:|
+| 0.0034 | 1 | 0.1823 | - |
+| 0.1701 | 50 | 0.0031 | - |
+| 0.3401 | 100 | 0.0012 | - |
+| 0.5102 | 150 | 0.0011 | - |
+| 0.6803 | 200 | 0.0009 | - |
+| 0.8503 | 250 | 0.0008 | - |
+| 1.0 | 294 | - | 0.0004 |
+| 1.0204 | 300 | 0.0008 | - |
+| 1.1905 | 350 | 0.0008 | - |
+| 1.3605 | 400 | 0.0007 | - |
+| 1.5306 | 450 | 0.0006 | - |
+| 1.7007 | 500 | 0.0006 | - |
+| 1.8707 | 550 | 0.0006 | - |
+| **2.0** | **588** | **-** | **0.0003** |
+
+* The bold row denotes the saved checkpoint.
+### Framework Versions
+- Python: 3.11.9
+- SetFit: 1.1.0.dev0
+- Sentence Transformers: 3.0.1
+- Transformers: 4.44.2
+- 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

@@ -0,0 +1,32 @@
+{
+ "_name_or_path": "bge-large-en-v1.5-brahmaputra-qa-lookup-iter-1-2-epoch/step_588",
+ "architectures": [
+ "BertModel"
+ ],
+ "attention_probs_dropout_prob": 0.1,
+ "classifier_dropout": null,
+ "gradient_checkpointing": false,
+ "hidden_act": "gelu",
+ "hidden_dropout_prob": 0.1,
+ "hidden_size": 1024,
+ "id2label": {
+ "0": "LABEL_0"
+ },
+ "initializer_range": 0.02,
+ "intermediate_size": 4096,
+ "label2id": {
+ "LABEL_0": 0
+ },
+ "layer_norm_eps": 1e-12,
+ "max_position_embeddings": 512,
+ "model_type": "bert",
+ "num_attention_heads": 16,
+ "num_hidden_layers": 24,
+ "pad_token_id": 0,
+ "position_embedding_type": "absolute",
+ "torch_dtype": "float32",
+ "transformers_version": "4.44.2",
+ "type_vocab_size": 2,
+ "use_cache": true,
+ "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+ "__version__": {
+ "sentence_transformers": "3.0.1",
+ "transformers": "4.44.2",
+ "pytorch": "2.4.0+cu121"
+ },
+ "prompts": {},
+ "default_prompt_name": null,
+ "similarity_fn_name": null
+}

config_setfit.json

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+{
+ "labels": [
+ "Lookup",
+ "qa"
+ ],
+ "normalize_embeddings": false
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8996e83ee682a78d7f4325241f6b13dcdcdd8775819643e781bce9c91b1da28e
+size 1340612432

model_head.pkl

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+oid sha256:aea2ca4272942ef841b42eba24e23a62761c8f1b642e1a15968ebab432370e7b
+size 9087

modules.json

@@ -0,0 +1,20 @@
+[
+ {
+ "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
+ },
+ "102": {
+ "content": "[SEP]",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "103": {
+ "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]"
+}