Add SetFit model

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 768,
+  "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-base-en-v1.5
+library_name: setfit
+metrics:
+- accuracy
+pipeline_tag: text-classification
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 'Reasoning:
+
+    The answer directly addresses the question and is correctly grounded in the document.
+    The percentage indeed refers to the total amount of successful completion of response
+    actions.
+
+    Evaluation: Good'
+- text: 'Reasoning:
+
+    From the provided document, it is clear that Endpoint controls relate to Device
+    Control, Personal Firewall Control, and Full Disk Encryption Visibility. The purpose
+    or intent behind enabling endpoint controls is to manage and secure endpoint devices
+    through various specific features. The given answer does not address the query
+    effectively, overlooking the information from the document that provides insight
+    into the purpose of endpoint controls.
+
+    Evaluation: Bad'
+- text: 'Reasoning:
+
+    The answer provided--"To collect logs and securely forward them to <ORGANIZATION>
+    XDR"--is precisely aligned with the information given in the document. The document
+    explicitly states that the On-Site Collector Agent is installed to collect logs
+    and securely forward them to <ORGANIZATION> (XDR). This answer is accurate and
+    specific, addressing the question without ambiguity or omission.
+
+
+    Evaluation: Good'
+- text: 'Reasoning:
+
+    The answer ''The purpose of the <ORGANIZATION_2> email notifications checkbox
+    in the Users section is to <ORGANIZATION_2> or disable email notifications for
+    users'' attempts to leverage the document''s information but fails to correctly
+    incorporate the specifics. The correct description based on the document should
+    detail how this checkbox allows toggling email notifications for users with the
+    System Admin role regarding stale or archived sensors.
+
+
+    Evaluation: Bad'
+- text: 'Reasoning:
+
+    The answered string `..\/..\/_images\/hunting_http://www.flores.net/ does not
+    appear in the document, nor does it match the provided URLs. The correct URL for
+    the image is `..\/..\/_images\/hunting_http://miller.co, which corresponds to
+    the second query.
+
+    Evaluation: Bad'
+inference: true
+model-index:
+- name: SetFit with BAAI/bge-base-en-v1.5
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.5492957746478874
+      name: Accuracy
+---
+
+# SetFit with BAAI/bge-base-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-base-en-v1.5](https://huggingface.co/BAAI/bge-base-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-base-en-v1.5](https://huggingface.co/BAAI/bge-base-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:** [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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 0     | <ul><li>'Reasoning:\nThe provided answer "It provides a comprehensive understanding of the situation" partially aligns with the intended concept, suggesting a holistic evaluation. However, it lacks direct specificity in relation to the document\'s key aspects, which emphasize deciding malicious behavior and subsequent actions, such as remediation, based on a collective assessment of significant machines, behaviors, and users affected. The answer misses the emphasis on prioritization and context provided by evaluating all factors collectively.\n\nEvaluation: Bad'</li><li>'Reasoning:\nThe given document explicitly outlines the steps required to exclude a MalOp during the remediation process. The answer, however, incorrectly states that the information is not covered in the document and suggests referring to additional sources. This contradicts the clear instructions provided in the document.\n\nEvaluation: Bad'</li><li>'Reasoning:\nThe answer directly addresses the question by stating that a quarantined file should be un-quarantined before being submitted, which aligns with the information provided in the document. The specific instruction to un-quarantine the file first if it is quarantined is accurately reflected in the response.\nEvaluation: Good'</li></ul> |
+| 1     | <ul><li>'Reasoning:\nThe answer is precise, correct, and directly addresses the question. It matches the information provided in the "Result" section of the document, which states that the computer will generate a dump file containing the entire contents of the sensor\'s RAM at the time of the failure.\n\nEvaluation: Good'</li><li>'Reasoning:\nThe provided answer "To identify cyber security threats" directly aligns with the information in the document which talks about the primary function of the <ORGANIZATION_2> platform being to identify cyber security threats using advanced technologies and methods.\nEvaluation: Good'</li><li>'Reasoning:\nThere is no fifth scenario detailed in the provided document, and the answer correctly identifies that the specific query is not covered in the provided information.\nEvaluation: Good'</li></ul>                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.5493   |
+
+## 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("Netta1994/setfit_baai_cybereason_gpt-4o_improved-cot_chat_few_shot_only_reasoning_1726752428.08")
+# Run inference
+preds = model("Reasoning:
+The answer directly addresses the question and is correctly grounded in the document. The percentage indeed refers to the total amount of successful completion of response actions.
+Evaluation: Good")
+```
+
+<!--
+### 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   | 18  | 43.0580 | 94  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 34                    |
+| 1     | 35                    |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (5, 5)
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0058 | 1    | 0.246         | -               |
+| 0.2890 | 50   | 0.2593        | -               |
+| 0.5780 | 100  | 0.2385        | -               |
+| 0.8671 | 150  | 0.0897        | -               |
+| 1.1561 | 200  | 0.004         | -               |
+| 1.4451 | 250  | 0.0022        | -               |
+| 1.7341 | 300  | 0.002         | -               |
+| 2.0231 | 350  | 0.0017        | -               |
+| 2.3121 | 400  | 0.0017        | -               |
+| 2.6012 | 450  | 0.0014        | -               |
+| 2.8902 | 500  | 0.0013        | -               |
+| 3.1792 | 550  | 0.0013        | -               |
+| 3.4682 | 600  | 0.0012        | -               |
+| 3.7572 | 650  | 0.0012        | -               |
+| 4.0462 | 700  | 0.0013        | -               |
+| 4.3353 | 750  | 0.0012        | -               |
+| 4.6243 | 800  | 0.0012        | -               |
+| 4.9133 | 850  | 0.0011        | -               |
+
+### Framework Versions
+- Python: 3.10.14
+- SetFit: 1.1.0
+- Sentence Transformers: 3.1.0
+- Transformers: 4.44.0
+- PyTorch: 2.4.1+cu121
+- Datasets: 2.19.2
+- 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": "BAAI/bge-base-en-v1.5",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "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": "4.44.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.1.0",
+    "transformers": "4.44.0",
+    "pytorch": "2.4.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

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+{
+  "normalize_embeddings": false,
+  "labels": null
+}

model.safetensors

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+oid sha256:9d33e9699d9660f98ec5e8c766bc14f56d93eb3f6f9667bc5c3eb18a23abe166
+size 437951328

model_head.pkl

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+size 7007

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": {
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+      "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
+    },
+    "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]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}