Add SetFit model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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: 'The answer effectively captures the essence of what the percentage in the
+    response status column indicates:
+
+
+    1. **Accurate Summary**: The provided answer accurately highlights that the percentage
+    indicates the total amount of successful completion of response actions.
+
+    2. **Document Grounding**: It is well-grounded in the document, which states:
+    "Column is Response status and Description is ... percentage indicates the total
+    amount of successful completion of response actions."
+
+
+    Therefore, the answer correctly and precisely responds to the query as per the
+    document.
+
+
+    Final Evaluation: Good'
+- text: 'The provided answer states that the question is not covered by the information
+    provided in the document. However, the document does give some context about what
+    Endpoint controls are and their configuration process—namely, they relate to Device
+    Control, Personal Firewall Control, and Full Disk Encryption Visibility. The provided
+    answer fails to derive the purpose from this information. An adequate response
+    would have explained that the purpose of Endpoint controls is to manage and configure
+    aspects like device control, personal firewall control, and full disk encryption
+    visibility.
+
+
+    Final evaluation: Bad'
+- text: "The answer provided only partially addresses the question and lacks a thorough\
+    \ explanation. The purpose of the <ORGANIZATION> XDR On-Site Collector Agent is\
+    \ indeed to collect logs and securely forward them to <ORGANIZATION> XDR. However,\
+    \ the document further elaborates that the collector agent is specifically used\
+    \ for integrations that do not use a cloud feed, such as firewalls. \n\nThe answer\
+    \ should have included this additional context to fully explain the specific role\
+    \ of the on-site collector in comparison to cloud feed integrations. Including\
+    \ this information makes the response comprehensive and directly aligned with\
+    \ the document.\n\nFinal evaluation: Bad"
+- text: "**Evaluation:**\n\nThe answer provided is, \"The purpose of the <ORGANIZATION_2>\
+    \ email notifications checkbox in the Users section is to <ORGANIZATION_2> or\
+    \ disable email notifications for users.\" \n\nThis response:\n\n1. **Accurate\
+    \ Use of Source Information**: The answer attempts to explain the purpose of the\
+    \ <ORGANIZATION_2> email notifications checkbox. However, it lacks the specific\
+    \ and detailed information present in the document.\n\n2. **Completeness and Specificity**:\
+    \ The document indicates that when the <ORGANIZATION_2> email notifications checkbox\
+    \ is set to On, users with the System Admin role receive email notifications about\
+    \ stale or archived sensors. The answer misses these critical details.\n\n3. **Terminology\
+    \ and Context**: The answer uses placeholders (\"<ORGANIZATION_2>\") which might\
+    \ have been intended to demonstrate variable data but does not reflect the actual\
+    \ functioning or purpose as part of a specific context.\n\n4. **Clarity and Detail**:\
+    \ The provided response lacks clarity and fails to fully describe the function\
+    \ and implications of toggling the checkbox for email notifications as presented\
+    \ in the document.\n\nGiven these considerations, the answer does not accurately\
+    \ represent the critical details and specific functionalities described in the\
+    \ document.\n\n**Final Evaluation: Bad**"
+- text: 'The given answer is "..\/..\/_images\/hunting_http://www.flores.net/". However,
+    the document clearly outlines the URLs for image examples relating to the queries.
+    For the second query, the URL provided in the document is ..\/..\/_images\/hunting_http://miller.co.
+    The answer provided does not match the correct URL from the document.
+
+
+    Final 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: 0.5915492957746479
+      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>"The answer provided is vague and does not directly address the question asked. The question specifically seeks to understand the significance of considering all the answers together when determining if the behavior in a MalOp is malicious. The document suggests that the combined answers help in determining whether the behavior requires remediation, identifying vital machines, addressing severe activities, and determining the significance of users involved. This nuanced consideration is critical to accurately identifying and prioritizing threats. The answer lacks these specific references and explanations and fails to provide a clear connection to the document's content.\n\nFinal evaluation: Bad"</li><li>'Evaluation:\nThe answer given by the user does not directly respond to the question. The provided document details the steps involved in excluding a MalOp during the remediation phase, but the user’s answer states that the information is insufficient and suggests seeking additional sources. This is incorrect, as the document contains the necessary steps to answer the question.\n\nFinal evaluation: Bad'</li><li>'The answer provided is accurate and correctly grounded in the document. The query asks what should be done if a file is quarantined, and the given response clearly states that the file should be un-quarantined before submitting it to the relevant organization. This matches the specific instruction found in the article, fulfilling the requirement of the evaluation.\n\nFinal Verdict: Good'</li></ul> |
+| 1     | <ul><li>"Evaluation:\n\nThe given answer is accurate and correctly reflects the content of the document. It specifies what the computer will generate (a dump file containing the entire contents of the sensor's RAM) in the event of a system failure, which aligns with the information provided in the document.\n\nFinal evaluation: Good"</li><li>'Evaluation:\nThe answer directly addresses the question by stating that the purpose of the platform\'s threat detection abilities is "to identify cyber security threats," which aligns with the information provided in the document. The document elaborates on the capabilities of the <ORGANIZATION_2> platform\'s threat detection, including the identification of cyber security threats using various methods like artificial intelligence, machine learning, and behavioral analysis.\n\nThe final evaluation: Good'</li><li>'The answer provided is unhelpful and does not directly address the question of identifying the severity score for the fifth scenario. The document briefing detailed examples of scoring for different scenarios, but the response fails to utilize that information. Instead, it defers to seeking additional sources or context, which is unnecessary here.\n\nFinal evaluation: Bad'</li></ul>                                                                                                                                                                                                                                                                                                  |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.5915   |
+
+## 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_cot-few_shot_only_reasoning_1726751740.333197")
+# Run inference
+preds = model("The given answer is \"..\/..\/_images\/hunting_http://www.flores.net/\". However, the document clearly outlines the URLs for image examples relating to the queries. For the second query, the URL provided in the document is ..\/..\/_images\/hunting_http://miller.co. The answer provided does not match the correct URL from the document.
+
+Final evaluation: Bad")
+```
+
+<!--
+### 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   | 20  | 58.5072 | 183 |
+
+| 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    | 0.2075        | -               |
+| 0.2890 | 50   | 0.2549        | -               |
+| 0.5780 | 100  | 0.2371        | -               |
+| 0.8671 | 150  | 0.084         | -               |
+| 1.1561 | 200  | 0.0034        | -               |
+| 1.4451 | 250  | 0.0021        | -               |
+| 1.7341 | 300  | 0.0019        | -               |
+| 2.0231 | 350  | 0.0016        | -               |
+| 2.3121 | 400  | 0.0016        | -               |
+| 2.6012 | 450  | 0.0014        | -               |
+| 2.8902 | 500  | 0.0013        | -               |
+| 3.1792 | 550  | 0.0013        | -               |
+| 3.4682 | 600  | 0.0013        | -               |
+| 3.7572 | 650  | 0.0013        | -               |
+| 4.0462 | 700  | 0.0013        | -               |
+| 4.3353 | 750  | 0.0012        | -               |
+| 4.6243 | 800  | 0.0012        | -               |
+| 4.9133 | 850  | 0.0012        | -               |
+
+### 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

@@ -0,0 +1,32 @@
+{
+  "_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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+version https://git-lfs.github.com/spec/v1
+oid sha256:584bb5d0615c2efabff98721ba5d439b840ff34a8438a89cbe1645191dc9f2e7
+size 437951328

model_head.pkl

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+oid sha256:e104b0725b55436b197b4e8323558ac768fc688be9cf8a3bb0765131377d6fc6
+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,
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+      "single_word": false,
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+      "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]"
+}