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:'
+- 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:'
+- 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: ****'
+- 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:"
+- 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:\
+    \ ****"
+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: 0.6901408450704225
+      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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
+|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 1     | <ul><li>'Reasoning:\n1. Context Grounding: The provided answer ("Amy Williams") is directly taken from the document.\n2. Relevance: The document specifies "Event name is Amy Williams and Triggering action is User added a registry events collection inclusion in the Collection configuration section of a sensor policy," which matches the question\'s request.\n3. Conciseness: The answer is concise and directly responds to the question without unnecessary information.\n4. Specifics: The given answer appropriately identifies the event name from the provided document.\n5. Key/Value/Event Name: The answer correctly identifies the "Event name" associated with the provided action.\n\nFinal Result:'</li><li>'Reasoning:\n\n1. Context Grounding: The answer refers directly to WPForms and is well-supported by the provided document, which describes WPForms as a WordPress forms plugin with a drag & drop builder.\n2. Relevance: The answer addresses the specific question of the function of the WPForms plugin for WordPress without deviating into unrelated topics.\n3. Conciseness: The answer is clear and to the point, succinctlyexplaining what WPForms does.\n\nFinal Result:'</li><li>'Reasoning:\n1. Context Grounding: The answer, "The main tool of conventional monetary policy in the USA is the federal funds rate," is directly supported by the document, which explicitly states that "The main tool of conventional monetary policy in the USA is the federal funds rate."\n2. Relevance: The answer directly addresses the specific question, "What is the main tool of conventional monetary policy in the USA?"\n3. Conciseness: The answer is clear and to the point, providing the necessary information without any extraneous details.\n\nFinal result:'</li></ul>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
+| 0     | <ul><li>'Reasoning:\n\n1. Context Grounding: The answer is based on information provided in the document, which mentions the first performance created by Hélène Langevin for young audiences with Brouhaha Danse.\n2. Relevance: The answer is directly relevant to the question, identifying the specific performance "Pierres, Papier, Ciseaux" (although the name format is slightly different in the document as "Roche, Papier, Ciseaux").\n3. Conciseness: The answer is brief and to the point, focusing only on the necessary information and not deviating into unrelated topics.\n\nUpon reviewing the document, the accurate name of the first performance should be "Roche, Papier, Ciseaux". The date provided in the answer (1998) is incorrect according to the document, which states 1996.\n\nFinal result:'</li><li>'Reasoning:\n1. Context Grounding: The answer is directly grounded in the provided document. It accurately reflects the reasons for sensors disconnecting from the <ORGANIZATION> platform.\n2. Relevance: The answer exclusively addresses the specific question asked—possible reasons for sensor disconnection.\n3. Conciseness: The answer is concise and avoids unnecessary information, sticking to the key reasons provided in the document.\n4. Specificity: The answer includes all specific reasons mentioned in the document, ensuring completeness.\n5. Key/Value/Event Name: The response correctly identifies the relevant reasons (key) for sensor disconnection without straying from the context.\n\nFinal Result:'</li><li>'Reasoning:\n\n1. **Context Grounding:** The document mentions the capability to search rule IDs within the Behavioral document protection settings.\n2. **Relevance:** The answer “Yes” is directly addressing the specific question about searching for rule IDs in Behavioral document protection.\n3. **Conciseness:** The answer is clear and to the point.\n4. **Specificity:** The answer is specific to the question asked. The document provides the exact steps and confirms the functionality, ensuring the answer is not too general or lacking specifics.\n5. **Key Identifiers:** While the answer is brief, it correctly identifies the core functionality (searching rule IDs) discussed in the provided document.\n\nFinal Result:'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.6901   |
+
+## 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_remove_final_evaluation_e1_larger_trai")
+# 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:")
+```
+
+<!--
+### 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   | 32  | 91.6109 | 198 |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 234                   |
+| 1     | 244                   |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (1, 1)
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0008 | 1    | 0.258         | -               |
+| 0.0418 | 50   | 0.2658        | -               |
+| 0.0837 | 100  | 0.2539        | -               |
+| 0.1255 | 150  | 0.2093        | -               |
+| 0.1674 | 200  | 0.1701        | -               |
+| 0.2092 | 250  | 0.1329        | -               |
+| 0.2510 | 300  | 0.1123        | -               |
+| 0.2929 | 350  | 0.0676        | -               |
+| 0.3347 | 400  | 0.0486        | -               |
+| 0.3766 | 450  | 0.0208        | -               |
+| 0.4184 | 500  | 0.0099        | -               |
+| 0.4603 | 550  | 0.0053        | -               |
+| 0.5021 | 600  | 0.0051        | -               |
+| 0.5439 | 650  | 0.0069        | -               |
+| 0.5858 | 700  | 0.0065        | -               |
+| 0.6276 | 750  | 0.0029        | -               |
+| 0.6695 | 800  | 0.0053        | -               |
+| 0.7113 | 850  | 0.005         | -               |
+| 0.7531 | 900  | 0.0027        | -               |
+| 0.7950 | 950  | 0.0026        | -               |
+| 0.8368 | 1000 | 0.0036        | -               |
+| 0.8787 | 1050 | 0.0018        | -               |
+| 0.9205 | 1100 | 0.0015        | -               |
+| 0.9623 | 1150 | 0.0016        | -               |
+
+### Framework Versions
+- Python: 3.10.14
+- SetFit: 1.1.0
+- Sentence Transformers: 3.1.1
+- Transformers: 4.44.0
+- PyTorch: 2.4.0+cu121
+- Datasets: 3.0.0
+- 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": "3.1.1",
+    "transformers": "4.44.0",
+    "pytorch": "2.4.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

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

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c8932eaf41f1a5499387ef4953b0592ff76ebe7560b7daca1be30ee75fcdcd99
+size 437951328

model_head.pkl

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+oid sha256:d6c4115dc140f87c7cb0267475b6c778d4189797afc3c8919383340f78ce122b
+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

@@ -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

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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
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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]"
+}