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: 0.7183098591549296
+      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 answer is well-supported by the provided document. The document indicates that a dime is "one-tenth of a dollar" and has a monetary value of "10¢."\n2. **Relevance**: The answer directly addresses the specific question of the monetary value of a dime.\n3. **Conciseness**: The answer is clear, to the point, and does not include unnecessary information.\n\nFinal Result:'</li><li>'Reasoning:\n1. Context Grounding: The document lists "Set the <ORGANIZATION> investigation status" as a topic, indicating that it is possible to set the investigation status.\n2. Relevance: The answer "Yes" directly addresses the question of whether one can set the <ORGANIZATION> investigation status.\n3. Conciseness: The answer is brief and to the point.\n4. Specificity: The document explicitly mentions setting the <ORGANIZATION> investigation status, so the answer is specific to the asked question.\n5. Key/Value/Event Name: The relevant key/event is "Set the <ORGANIZATION> investigation status" which is correctlyidentified as being possible.\n\nFinal result:'</li><li>'**Reasoning:**\n\n1. **Context Grounding:** The answer is well-supported by the document, aligning perfectly with the specific benefits mentioned by the author in the document. It includes benefits like unapologetic "me" time, health, self-growth, patience, taking time to be still, accepting changing moods, responsibility for happiness, appreciation for the body, yoga\'s presence off the mat, and the importance of being open. These points are directly extracted from the provided content.\n \n2. **Relevance:** The answer focuses squarely on addressing the specific question asked — what benefits the author has experienced from their regular yoga practice. It avoids unrelated topics and stays on point.\n\n3. **Conciseness:** The answer is clear, concise, and directly lists the benefits without unnecessary elaboration. Each mentioned benefit corresponds to a specific point from the document, making it easyto verify and understand.\n\n**Final Result: **'</li></ul>                                                                                                                                                                                                                                |
+| 0     | <ul><li>'Reasoning:\n1. Context Grounding: The answer is consistent with the suggestions found in the provided document. It mentions reducing salt intake, cutting processed foods and alcohol, and drinking more water, all of which are present in the document.\n2. Relevance: The answer stays focused on the main query about losing the last 10 pounds and provides actionable advice directly related to the document.\n3. Conciseness: The answer is a bit lengthy but stays mostly on point without much deviation.\n\nThe document provides a variety of methods for tackling the last 10 pounds, and the answer effectively consolidates some of these points. Although the response could be shorter and more concise, it appropriately addresses the question based on the document.\n\nFinal Result:'</li><li>'The given answer `..\\/..\\/_images\\/bal_https://elliott.biz/` does not match any of the Image URLs provided in the document. For step 5, the correct Image URL is `..\\/..\\/_images\\/bal_http://osborn-mendoza.info/`.\n\nReasoning:\n1. **Context Grounding**: The answer is not supported by the provided document. The correct Image URL should be found in the list under the relevant step.\n2. **Relevance**: The answer must directly address the specific question by identifying the correct Image URL corresponding to step 5.\n3. **Conciseness**: The answer should be concise but accurate. The provided answer adds irrelevant information.\n4. **Specifics**: The provided document includes specific Image URLs for each step that must be matched correctly to the steps provided.\n5. **Key, Value, and Event Name**: The correct identification of the image URL for step 5 is critical here.\n\nFinal result:'</li><li>'Reasoning:\n1. **Context Grounding:** The provided answer is rooted in the document, which mentions that Amy Bloom finds starting a project hard and having to clear mental space, recalibrate, and become less involved in her everyday life.\n2. **Relevance:** The response accurately focuses on the challenges Bloom faces when starting a significant writing project, without deviating into irrelevant areas.\n3. **Conciseness:** The answer effectively summarizes the relevant information from the document, staying clear and to the point while avoiding unnecessary detail.\n\nFinal Result:'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.7183   |
+
+## 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_e2_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   | 33  | 94.4664 | 198 |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 129                   |
+| 1     | 139                   |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (2, 2)
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0015 | 1    | 0.1648        | -               |
+| 0.0746 | 50   | 0.2605        | -               |
+| 0.1493 | 100  | 0.2538        | -               |
+| 0.2239 | 150  | 0.2244        | -               |
+| 0.2985 | 200  | 0.1409        | -               |
+| 0.3731 | 250  | 0.0715        | -               |
+| 0.4478 | 300  | 0.0238        | -               |
+| 0.5224 | 350  | 0.0059        | -               |
+| 0.5970 | 400  | 0.0032        | -               |
+| 0.6716 | 450  | 0.0025        | -               |
+| 0.7463 | 500  | 0.0024        | -               |
+| 0.8209 | 550  | 0.0019        | -               |
+| 0.8955 | 600  | 0.0017        | -               |
+| 0.9701 | 650  | 0.0016        | -               |
+| 1.0448 | 700  | 0.0015        | -               |
+| 1.1194 | 750  | 0.0015        | -               |
+| 1.1940 | 800  | 0.0013        | -               |
+| 1.2687 | 850  | 0.0013        | -               |
+| 1.3433 | 900  | 0.0013        | -               |
+| 1.4179 | 950  | 0.0012        | -               |
+| 1.4925 | 1000 | 0.0013        | -               |
+| 1.5672 | 1050 | 0.0012        | -               |
+| 1.6418 | 1100 | 0.0011        | -               |
+| 1.7164 | 1150 | 0.0011        | -               |
+| 1.7910 | 1200 | 0.0011        | -               |
+| 1.8657 | 1250 | 0.0012        | -               |
+| 1.9403 | 1300 | 0.0011        | -               |
+
+### 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 sha256:24b00f52d8ff6c405d9497fb7871ae30e86d534c773cc8ac4df81c8ace9996f4
+size 437951328

model_head.pkl

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+oid sha256:ca2778338e113b7e68a547881a6f055a8cf72f5fe85b63a621c8d9cbfcf2a5bb
+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
+    },
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+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "102": {
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+      "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]"
+}