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

@@ -0,0 +1,263 @@
+---
+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 is direct, concise, and accurately picks out the relevant information
+    from the document concerning the percentage in the response status column.
+
+    Evaluation: Good'
+- text: 'Reasoning:
+
+    The document provides specific steps to enable and configure Endpoint controls,
+    but it doesn''t explicitly state the purpose or objective of Endpoint controls.
+    The answer appropriately recognizes that the provided information does not cover
+    the specific query regarding the purpose. This shows strong context grounding
+    and relevance to the question, as the document indeed lacks the required detail.
+
+
+    Evaluation: Good'
+- text: 'Reasoning:
+
+    The answer directly refers to a specific function mentioned in the document, which
+    is to collect logs and securely forward them to the <ORGANIZATION> XDR. This is
+    concise and appropriately addresses the question based on the given text.
+
+
+    Evaluation: Good'
+- text: 'Reasoning:
+
+    The answer correctly identifies the purpose of the <ORGANIZATION_2> email notifications
+    checkbox as enabling or disabling email notifications for users. This is supported
+    by the document, specifically the section that details that if <ORGANIZATION_2>
+    notifications is set to On, users with the System Admin role will receive email
+    notifications about stale or archived sensors, even if the user notification checkbox
+    is not selected.
+
+
+    Evaluation: Good'
+- text: 'Reasoning:
+
+    1. Context Grounding: The provided answer is not adequately supported by the document.
+    The document provides the correct URL, which is `..\/..\/_images\/hunting_http://miller.co
+    for the second query, but the answer presented is incorrect and refers only to
+    the first query image.
+
+    2. Relevance: The answer is not relevant; it does not identify that the appropriate
+    URL for the image is `..\/..\/_images\/hunting_http://miller.co.
+
+    3. Conciseness: The answer is concise but inaccurate.
+
+    4. Specificity: The answer lacks the specifics necessary to directly respond to
+    the question, it does not pertain to the second query image URL.
+
+
+    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](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 response fails to fully address the specifics of the question based on the provided document. The question is about the significance of considering all answers together when determining if the behavior in a MalOp is malicious. The response should have included how analyzing various factors, such as the significance of machines, behavior severity, and the users involved, helps in forming a comprehensive understanding of whether the behavior is malicious and requires further action.\n\nThe answer given is overly vague and does not capture the detailed reasoning found in the document. It lacks essential details concerning the factors that need to be considered, such as machine significance, activity severity, and user importance.\n\nEvaluation: Bad'</li><li>'Reasoning:\nThe document provides a detailed process to exclude a MalOp during the remediation phase, and the answer fails to incorporate this information. The answer is also quite dismissive, suggesting the document does not cover the query when it does. Thus, the answer does not address the specific question asked and is not grounded in the context of the provided document.\n\nFinal Result: Bad'</li><li>'Reasoning:\nThe answer correctly states that a file should be un-quarantined before submitting it to the respective organization, which aligns with the information provided in the document. The answer is concise, specific, and directly addresses the question without any deviation.\n\nEvaluation: Good'</li></ul> |
+| 1     | <ul><li>"Reasoning:\nThe answer is directly grounded in the provided document, accurately conveying the information that the computer will generate a dump file containing the entire contents of the sensor's RAM at the time of the failure. The response is concise and specific to the question asked without including unnecessary information.\n\nEvaluation: Good"</li><li>'Reasoning:\n1. Context Grounding: The provided answer "To identify cyber security threats" is directly supported by the document, which states that threat detection is a core capability that aims to identify cyber security threats by analyzing data.\n2. Relevance: The answer directly addresses the specific question about the purpose of the <ORGANIZATION_2> platform\'s threat detection abilities.\n3. Conciseness: The answer is clear and to the point, summarizing the purpose effectively without unnecessary information.\n4. Specificity: The answer provides a specific purpose as outlined in the document.\n\nEvaluation: Good'</li><li>'Reasoning:\nThe given answer asserts that the provided information does not cover the specific query, advising to refer to additional sources. However, the document contains clear information regarding four different scenarios and their corresponding severity scores. The answer also does not directly address what is clearly stated in the document and evades the question.\n\nEvaluation: Bad'</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_improved-cot-instructions_chat_few_shot_only_reasoning_")
+# Run inference
+preds = model("Reasoning:
+The answer is direct, concise, and accurately picks out the relevant information from the document concerning the percentage in the response status column.
+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   | 2   | 48.4638 | 115 |
+
+| 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    | 0.2563        | -               |
+| 0.2890 | 50   | 0.2638        | -               |
+| 0.5780 | 100  | 0.242         | -               |
+| 0.8671 | 150  | 0.1521        | -               |
+| 1.1561 | 200  | 0.0056        | -               |
+| 1.4451 | 250  | 0.0025        | -               |
+| 1.7341 | 300  | 0.0022        | -               |
+| 2.0231 | 350  | 0.0019        | -               |
+| 2.3121 | 400  | 0.0019        | -               |
+| 2.6012 | 450  | 0.0016        | -               |
+| 2.8902 | 500  | 0.0015        | -               |
+| 3.1792 | 550  | 0.0014        | -               |
+| 3.4682 | 600  | 0.0014        | -               |
+| 3.7572 | 650  | 0.0014        | -               |
+| 4.0462 | 700  | 0.0014        | -               |
+| 4.3353 | 750  | 0.0014        | -               |
+| 4.6243 | 800  | 0.0013        | -               |
+| 4.9133 | 850  | 0.0013        | -               |
+
+### 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

@@ -0,0 +1,4 @@
+{
+  "normalize_embeddings": false,
+  "labels": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2da66776db189c68cb35016faa0455bb209d59510717ce5e5b6c95647c600146
+size 437951328

model_head.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:32b3387b68fb92b4c22c44c583dfa042128e90551cc01e5c6e192beaae51836b
+size 7007

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

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": true
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "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

@@ -0,0 +1,57 @@
+{
+  "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]",
+  "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]"
+}