Add SetFit model

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 384,
+  "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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+---
+base_model: sentence-transformers/all-MiniLM-L6-v2
+library_name: setfit
+metrics:
+- accuracy
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: How does technology impact our daily lives and what benefits can it bring
+    to various activities?
+- text: How do organizations effectively deploy and manage machine learning algorithms
+    to drive business value?
+- text: What are the key considerations for organizing and managing computer lab resources
+    and tracking their status?
+- text: How can batch processing improve the efficiency of data lake operations?
+- text: What is the purpose of setting up a CUPS on a server?
+inference: true
+model-index:
+- name: SetFit with sentence-transformers/all-MiniLM-L6-v2
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.8947368421052632
+      name: Accuracy
+---
+
+# SetFit with sentence-transformers/all-MiniLM-L6-v2
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 256 tokens
+- **Number of Classes:** 2 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                                                                                                                                                                                                                                      |
+|:---------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| lexical  | <ul><li>"How does Happeo's search AI work to provide answers to user queries?"</li><li>'What are the primary areas of focus in the domain of Data Science and Analysis?'</li><li>'How can one organize a running event in Belgium?'</li></ul> |
+| semantic | <ul><li>'What changes can be made to a channel header?'</li><li>'How can hardware capabilities impact the accuracy of motion and object detections?'</li><li>'Who is responsible for managing guarantees and prolongations?'</li></ul>        |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.8947   |
+
+## 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("yaniseuranova/setfit-rag-hybrid-search-query-router-test")
+# Run inference
+preds = model("What is the purpose of setting up a CUPS on a server?")
+```
+
+<!--
+### 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   | 4   | 13.7407 | 28  |
+
+| Label    | Training Sample Count |
+|:---------|:----------------------|
+| lexical  | 44                    |
+| semantic | 118                   |
+
+### Training Hyperparameters
+- batch_size: (8, 8)
+- num_epochs: (1, 1)
+- 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 |
+|:-------:|:--------:|:-------------:|:---------------:|
+| 0.0005  | 1        | 0.431         | -               |
+| 0.0250  | 50       | 0.2596        | -               |
+| 0.0499  | 100      | 0.2718        | -               |
+| 0.0749  | 150      | 0.1733        | -               |
+| 0.0999  | 200      | 0.0706        | -               |
+| 0.1248  | 250      | 0.04          | -               |
+| 0.1498  | 300      | 0.003         | -               |
+| 0.1747  | 350      | 0.0008        | -               |
+| 0.1997  | 400      | 0.0004        | -               |
+| 0.2247  | 450      | 0.0004        | -               |
+| 0.2496  | 500      | 0.0021        | -               |
+| 0.2746  | 550      | 0.0004        | -               |
+| 0.2996  | 600      | 0.0006        | -               |
+| 0.3245  | 650      | 0.0002        | -               |
+| 0.3495  | 700      | 0.0008        | -               |
+| 0.3744  | 750      | 0.0003        | -               |
+| 0.3994  | 800      | 0.0003        | -               |
+| 0.4244  | 850      | 0.0002        | -               |
+| 0.4493  | 900      | 0.0003        | -               |
+| 0.4743  | 950      | 0.0002        | -               |
+| 0.4993  | 1000     | 0.0001        | -               |
+| 0.5242  | 1050     | 0.0001        | -               |
+| 0.5492  | 1100     | 0.0001        | -               |
+| 0.5741  | 1150     | 0.0001        | -               |
+| 0.5991  | 1200     | 0.0002        | -               |
+| 0.6241  | 1250     | 0.0005        | -               |
+| 0.6490  | 1300     | 0.0002        | -               |
+| 0.6740  | 1350     | 0.0002        | -               |
+| 0.6990  | 1400     | 0.0002        | -               |
+| 0.7239  | 1450     | 0.0001        | -               |
+| 0.7489  | 1500     | 0.0002        | -               |
+| 0.7738  | 1550     | 0.0001        | -               |
+| 0.7988  | 1600     | 0.0003        | -               |
+| 0.8238  | 1650     | 0.0002        | -               |
+| 0.8487  | 1700     | 0.0004        | -               |
+| 0.8737  | 1750     | 0.0002        | -               |
+| 0.8987  | 1800     | 0.0003        | -               |
+| 0.9236  | 1850     | 0.0001        | -               |
+| 0.9486  | 1900     | 0.0001        | -               |
+| 0.9735  | 1950     | 0.0001        | -               |
+| 0.9985  | 2000     | 0.0001        | -               |
+| **1.0** | **2003** | **-**         | **0.1757**      |
+
+* The bold row denotes the saved checkpoint.
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 2.6.1
+- Transformers: 4.39.0
+- PyTorch: 2.3.1+cu121
+- Datasets: 2.18.0
+- Tokenizers: 0.15.2
+
+## 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_2003",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.39.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.6.1",
+    "pytorch": "1.8.1"
+  },
+  "prompts": {},
+  "default_prompt_name": null
+}

config_setfit.json

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+{
+  "labels": [
+    "lexical",
+    "semantic"
+  ],
+  "normalize_embeddings": false
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b90d001c504859e63c5345e451800a56c6274cb3bab0ebae4eb006771e7c51b4
+size 90864192

model_head.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:af9e4d74f361d24cf740f9e115a0c437a7fbe436892b1bde1338d09660e1f961
+size 3983

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": 256,
+  "do_lower_case": false
+}

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": 128,
+  "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]"
+}