metadata

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:


      1. **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.
        
      2. **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.
        
      3. **Conciseness**: The answer is concise but could be clearer about the
      specific type of notifications and their management.
        
      4. **Specificity**: The answer is somewhat general and does not fully
      capture the detailed function of the checkbox as described in the
      document.
        
      5. **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).


      Final 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.


      **Reasoning:**

      1. **Context Grounding:**
         - The URL provided "..\/..\/_images\/hunting_http://www.flores.net/" is not found in the provided document.
         - Instead, the correct URL as per the document for Query 2 is "..\/..\/_images\/hunting_http://miller.co".

      2. **Relevance:**
         - 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.

      3. **Conciseness:**
         - The answer does not provide any extraneous information, but being incorrect, it fails at providing the relevant and necessary detail concisely.

      4. **Specificity:**
         - The answer is specific but incorrect. It provides a URL, but not the right one as required.

      5. **Accuracy of key/value/event name:**
         - The correct event (image URL) for the second query is "..\/..\/_images\/hunting_http://miller.co" according to the document.

      Final 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 model that can be used for Text Classification. This SetFit model uses BAAI/bge-base-en-v1.5 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

Fine-tuning a Sentence Transformer with contrastive learning.
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
Classification head: a LogisticRegression instance
Maximum Sequence Length: 512 tokens
Number of Classes: 2 classes

Model Sources

Repository: SetFit on GitHub
Paper: Efficient Few-Shot Learning Without Prompts
Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Model Labels

Label	Examples
0	'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' "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" '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'
1	"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" "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 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 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" "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"

Label

Examples

'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'
"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"
'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'

"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"
"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 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 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"
"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"

Evaluation

Metrics

Label	Accuracy
all	0.5070

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

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")

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

@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}
}