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 percentage in the response status column indicates the total amount of
+    successful completion of response actions.
+
+
+    Reasoning:
+
+    1. **Context Grounding**: The answer is well-supported by the document which states,
+    "percentage indicates the total amount of successful completion of response actions."
+
+    2. **Relevance**: The answer directly addresses the specific question asked about
+    what the percentage in the response status column indicates.
+
+    3. **Conciseness**: The answer is succinct and to the point without unnecessary
+    information.
+
+    4. **Specificity**: The answer is specific to what is being asked, detailing exactly
+    what the percentage represents.
+
+    5. **Accuracy**: The answer provides the correct key/value as per the document.
+
+
+    Final result: Good'
+- text: 'Reasoning:
+
+    1. **Context Grounding**: The provided document does outline steps to enable Endpoint
+    controls but doesn''t explicitly state their purpose.
+
+    2. **Relevance**: The answer acknowledges the lack of specific information in
+    the document about the purpose of Endpoint controls.
+
+    3. **Conciseness**: The answer is concise, directly addressing the lack of information.
+
+    4. **Specificity**: The answer directly states that the document doesn''t answer
+    the query, suggesting further sources should be checked.
+
+    5. **Detailed Key/Value/Event Name Check**: These elements do not apply to this
+    specific question.
+
+
+    Considering the criteria, the answer is accurate in indicating the document does
+    not provide the purpose of Endpoint controls and suggests looking for additional
+    sources.
+
+
+    Final Result: Good'
+- text: 'Reasoning:
+
+
+    1. **Context Grounding**: The answer refers to using the <ORGANIZATION> XDR to
+    collect and forward logs, but it does not directly mention the <ORGANIZATION>
+    XDR On-Site Collector Agent, although it is tangentially related.
+
+    2. **Relevance**: The question specifically inquires about the purpose of the
+    <ORGANIZATION> XDR On-Site Collector Agent, not the general functionality of <ORGANIZATION>
+    XDR. The answer provided does not address the agent itself.
+
+    3. **Conciseness**: The answer provided is concise but unfortunately lacks relevance
+    to the specific question being asked.
+
+    4. **Specificity**: The answer is too general and doesn''t provide the specific
+    purpose of the On-Site Collector Agent.
+
+    5. **Key/Value/Event Name**: The answer does not include any specific key, value,
+    or event name that would relate to discussing an On-Site Collector Agent.
+
+
+    Final result: **Bad**'
+- text: "Reasoning:\n\n1. **Context Grounding**: The provided answer mentions the\
+    \ purpose of the <ORGANIZATION_2> email notifications checkbox in relation to\
+    \ enabling or disabling email notifications for users. However, the document explicitly\
+    \ states that notifications about stale and archived sensors are managed separately\
+    \ from other email preferences. The checkbox in the Users section determines whether\
+    \ users receive these specific notifications, which indicates a more precise purpose.\n\
+    \  \n2. **Relevance**: The response does relate to the question but lacks specificity\
+    \ about the type of notifications (stale/archived sensors) governed by the checkbox.\
+    \ It also fails to mention that these notifications are managed independently\
+    \ of other email preferences.\n  \n3. **Conciseness**: The answer is concise but\
+    \ could be clearer about the specific type of notifications and their management.\n\
+    \  \n4. **Specificity**: The answer is somewhat general and does not fully capture\
+    \ the detailed function of the checkbox as described in the document.\n  \n5.\
+    \ **Correct Key/Value/Event Name**: The answer correctly identifies the purpose\
+    \ of the checkbox but does not reflect the detailed context provided in the document\
+    \ regarding specific notifications (stale/archived sensors).\n\nFinal Result:\
+    \ Bad"
+- text: "The provided answer \"..\\/..\\/_images\\/hunting_http://www.flores.net/\"\
+    \ does not match the correct URL as per the document content for the second query.\n\
+    \n**Reasoning:**\n1. **Context Grounding:**\n   - The URL provided \"..\\/..\\\
+    /_images\\/hunting_http://www.flores.net/\" is not found in the provided document.\n\
+    \   - Instead, the correct URL as per the document for Query 2 is \"..\\/..\\\
+    /_images\\/hunting_http://miller.co\".\n\n2. **Relevance:**\n   - The answer provided\
+    \ does not correspond to the specific question asked, which was about the URL\
+    \ for the second query. It deviates from the document and is incorrect.\n\n3.\
+    \ **Conciseness:**\n   - The answer does not provide any extraneous information,\
+    \ but being incorrect, it fails at providing the relevant and necessary detail\
+    \ concisely.\n\n4. **Specificity:**\n   - The answer is specific but incorrect.\
+    \ It provides a URL, but not the right one as required.\n\n5. **Accuracy of key/value/event\
+    \ name:**\n   - The correct event (image URL) for the second query is \"..\\/..\\\
+    /_images\\/hunting_http://miller.co\" according to the document.\n\nFinal result:\
+    \ **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: 0.5070422535211268
+      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:\n1. **Context Grounding**: The response "It provides a comprehensive understanding of the situation" is too vague and not well-supported by the document provided. The document specifically discusses the importance of considering all answers to comprehensively determine if behavior is malicious.\n2. **Relevance**: While the response is somewhat related, it does not specifically address why considering all the answers together is significant. The document talks about the threat qualification steps and emphasizes the importance of examining multiple indicators to correctly assess the situation.\n3. **Conciseness**: The answer is concise but lacks detail.\n4. **Specificity**: The response is too general and lacks the specific details necessary to fully answer the question as supported by the document.\n5. **Key/Value/Event Name**: Not applicable in this evaluation.\n\nFinal Result: Bad'</li><li>"The given answer does not address the specific question asked. The document contains detailed steps on how to exclude a MalOp during the remediation phase, which directly relates to the question. However, the answer provided claims that the information doesn't cover this specific query and suggests referring to additional sources, which is incorrect. \n\nReasoning:\n1. **Context Grounding:** The provided document clearly offers steps on how to exclude a MalOp, hence the answer is not grounded in the context of the document.\n2. **Relevance:** The question asked is directly related to the steps to exclude a MalOp, yet the response does not address these steps.\n3. **Conciseness:** The response suggests looking elsewhere, which is not concise or needed since the information is already present in the document.\n4. **Specificity:** The document contains specific steps and details regarding the MalOp exclusion process, which the answer fails to capture.\n\nFinal Result: **Bad**"</li><li>'Reasoning:\n\n1. **Context Grounding**: The answer directly reflects a step in the document which states that if a file is quarantined, it should be un-quarantined before submission.\n2. **Relevance**: The answer specifically addresses the asked question regarding the procedure to follow if a file is quarantined.\n3. **Conciseness**: The response is very concise and directly addresses the action to take.\n4. **Specificity**: The answer pinpoints the exact action required for quarantined files as mentioned in the document.\n\nFinal result: Good'</li></ul>                                                                                                                                     |
+| 1     | <ul><li>"Reasoning:\n1. **Context Grounding**: The provided document specifies that after configuring a sensor, the computer will generate a memory dump file containing the RAM contents at the time of failure, which supports the given answer.\n2. **Relevance**: The answer directly responds to the question by stating what the computer will generate in the event of a system failure.\n3. **Conciseness**: The answer is brief and directly answers the question without any extraneous information.\n4. **Specificity**: The answer is not overly general; it correctly identifies that the dump file will contain the contents of the sensor's RAM at the time of the failure, aligning with the document.\n5. **Key/Value/Event name**: The answer correctly identifies the relevant outcome, which is the generation of a memory dump file containing the sensor's RAM contents.\n\nFinal Result: Good"</li><li>"Reasoning:\n\n1. Context Grounding: The answer is concise and based on the provided document. Both the detected purpose (identify cyber security threats) and the mechanism (using the <ORGANIZATION> engine with AI, ML, and behavioral analysis) are aligned with the document's contents.\n2. Relevance: The answer directly addresses the specific question asked about the purpose of the <ORGANIZATION_2> platforms threat detection abilities.\n3. Conciseness: The answer is clear and to the point without unnecessary information. \n4. Specificity: The answer accurately identifies the relevant purpose mentioned in the document. \n5. Key/Value/Event: The question does not prompt for key, value, or event name, so this criterion is not applicable here.\n\nFinal Result: Good"</li><li>"The information provided directly addresses the question by assessing the presence of relevant text in the given document. The response accurately identifies that the document does not mention or cover a fifth scenario.\n\n1. **Context Grounding**: The answer is well-supported by the document and maintains a clear link to the given text, confirming the absence of a fifth scenario.\n2. **Relevance**: The answer clearly addresses the specific question asked, ensuring no deviation.\n3. **Conciseness**: The answer is clear and to the point, avoiding any unnecessary information.\n4. **Specifics**: The answer is specific in confirming the lack of content related to a fifth scenario, ensuring correctness.\n5. **Key/Value/Event Name Identification**: Given the document only contains four scenarios, the identification of a fifth scenario's severity score is inherently impossible and is aptly noted.\n\nFinal Verdict: **Good**"</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.5070   |
+
+## 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-instructions_only_reasoning_1726752054.560885")
+# Run inference
+preds = model("The percentage in the response status column indicates the total amount of successful completion of response actions.
+
+Reasoning:
+1. **Context Grounding**: The answer is well-supported by the document which states, \"percentage indicates the total amount of successful completion of response actions.\"
+2. **Relevance**: The answer directly addresses the specific question asked about what the percentage in the response status column indicates.
+3. **Conciseness**: The answer is succinct and to the point without unnecessary information.
+4. **Specificity**: The answer is specific to what is being asked, detailing exactly what the percentage represents.
+5. **Accuracy**: The answer provides the correct key/value as per the document.
+
+Final result: 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   | 70  | 112.1739 | 168 |
+
+| 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    | 0.2538        | -               |
+| 0.2890 | 50   | 0.2672        | -               |
+| 0.5780 | 100  | 0.2355        | -               |
+| 0.8671 | 150  | 0.0836        | -               |
+| 1.1561 | 200  | 0.0038        | -               |
+| 1.4451 | 250  | 0.0024        | -               |
+| 1.7341 | 300  | 0.0021        | -               |
+| 2.0231 | 350  | 0.0018        | -               |
+| 2.3121 | 400  | 0.0017        | -               |
+| 2.6012 | 450  | 0.0015        | -               |
+| 2.8902 | 500  | 0.0014        | -               |
+| 3.1792 | 550  | 0.0014        | -               |
+| 3.4682 | 600  | 0.0013        | -               |
+| 3.7572 | 650  | 0.0013        | -               |
+| 4.0462 | 700  | 0.0013        | -               |
+| 4.3353 | 750  | 0.0013        | -               |
+| 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

@@ -0,0 +1,10 @@
+{
+  "__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 sha256:9031e514bc3a2175cd17303d484d4fe48fbfef604576bc5aeb918c472da12e09
+size 437951328

model_head.pkl

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+oid sha256:c4158d18d42ec33df74ebc374891ae197f8cab798c5af4fba2a12936920d69f0
+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

@@ -0,0 +1,4 @@
+{
+  "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": {
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+    "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": {
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+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
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+    "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]"
+}