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: 'Reasoning:
+
+    1. **Context Grounding**: The answer is well-supported by the provided document
+    and includes specific details that align with Haribabu Kommi''s email.
+
+    2. **Relevance**: The answer directly addresses the question by listing the changes
+    being made to the storage AM as mentioned in the email.
+
+    3. **Conciseness**: The answer is clear and to the point, avoiding unnecessary
+    information.
+
+
+    The initial statement captures Haribabu Kommi''s main points, and the follow-up
+    details provide the exact changes and enhance the completeness without deviating
+    from the topic.
+
+
+    Final Result:'
+- text: 'Reasoning:
+
+    1. **Context Grounding**: The answer accurately identifies Ning Zhongyan as the
+    gold medalist in the men''s 1,500m final at the speed skating World Cup. This
+    information matches the provided document where it is explicitly mentioned.
+
+    2. **Relevance**: The answer is directly relevant to the question asked, providing
+    the required information without straying into unrelated details.
+
+    3. **Conciseness**: The answer is concise and to the point, only mentioning the
+    necessary details about the winner and the event.
+
+
+    Final Result:'
+- text: 'Reasoning:
+
+
+    1. Context Grounding: The answer provided is well-supported by the provided document,
+    as it correctly lists the sizes specified in the text for both individual and
+    combined portraits.
+
+    2. Relevance: The answer is directly related to the question, addressing the specific
+    sizes for the individual and combined portraits without straying into unrelated
+    information.
+
+    3. Conciseness: The answer is clear and to the point, sticking strictly to the
+    sizes without adding unnecessary details.
+
+
+    Final Result:'
+- text: 'Reasoning:
+
+    1. Context Grounding: The answer accurately describes the components of the Student
+    Guide, which is well-supported by the provided document.
+
+    2. Relevance: The answer directly addresses the question by listing the components
+    of the British Medieval Student Guide.
+
+    3. Conciseness: The answer is concise and includes only the necessary details
+    regarding the components of the guide without extraneous information.
+
+
+    Final Result:'
+- text: 'Reasoning:
+
+    1. **Context Grounding**: The document explicitly names the first three Members
+    of Congress as Reps. Keith Ellison, Barbara Lee, and Danny Davis. The answer provided
+    refers to Rep. Andy Harris, Reps. Kyle Evans, and Jessica Smith, which does not
+    align with the information in the document.
+
+    2. **Relevance**: The answer does not correctly address the question based on
+    the information provided in the document.
+
+    3. **Conciseness**: Although the given answer is concise, it is incorrect as it
+    names individuals who are not mentioned in the provided document.
+
+
+    Final 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: 0.88
+      name: Accuracy
+---
+
+# SetFit with BAAI/bge-base-en-v1.5
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
+
+The model has been trained using an efficient few-shot learning technique that involves:
+
+1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
+2. Training a classification head with features from the fine-tuned Sentence Transformer.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SetFit
+- **Sentence Transformer body:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Classes:** 2 classes
+<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
+- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
+- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
+
+### Model Labels
+| Label | Examples                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
+|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 0     | <ul><li>"Reasoning:\n1. Context Grounding: The answer aligns well with the provided document, specifically discussing coach Brian Shaw's influence and changes in the team strategy, which are mentioned in the text.\n2. Relevance: The response directly addresses the question by focusing on the reasons behind the Nuggets' offensive success in January, such as the new gameplay strategy advocated by the coach and increased comfort and effectiveness.\n3. Conciseness: The answer is mostly concise but adds an unsubstantiated point about virtual reality training, which is not mentioned in the document and should be excluded to maintain briefing relevance.\n\nFinal result: ****."</li><li>"Reasoning:\n1. Context Grounding: The answer effectively uses specific details from the provided document, discussing the author's experience with digital and film photography, and technical differences such as how each medium handles exposure and color capture.\n2. Relevance: The answer is directly relevant to the question, enumerating specific differences mentioned by the author.\n3. Conciseness: While mostly concise, the answer could have been slightly more succinct. However, it largely avoids unnecessary information and remains clear and to the point.\n\nFinal Result:"</li><li>"Reasoning:\n\n1. **Context Grounding:** The answer given details the results of a mixed martial arts event, specifically highlighting Antonio Rogerio Nogueira's victory. However, the question asks about the main conflict in the third book of the Arcana Chronicles by Kresley Cole. There is no relevance in the provided document or the answer to the Arcana Chronicles.\n2. **Relevance:** The answer does not address the asked question at all. Instead, it provides information about an MMA fight, which is entirely unrelated to the Arcana Chronicles.\n3. **Conciseness:** While the answer is concise, it fails to answer the appropriate question, thus making its conciseness irrelevant in this context.\n\nFinal Result:"</li></ul> |
+| 1     | <ul><li>'Reasoning:\n\n1. Context Grounding: The answer provided is well-supported by the document and grounded in the text, which discusses best practices for web designers to avoid unnecessary revisions and conflicts. It specifically addresses parts of the document that highlight getting to know the client, signing a contract, and being honest and diplomatic.\n   \n2. Relevance: The answer directly addresses the question of best practices a web designer can incorporate into their client discovery and web design process. It does not deviate into unrelated topics and remains relevant throughout.\n\n3. Conciseness: The answer is clear and concise. It covers the main points without unnecessary elaboration or inclusion of extraneous information.\n\nFinal Result:'</li><li>"Reasoning:\n\n1. Context Grounding: The answer provided is well-supported by the document. The document discusses the importance of drawing from one's own experiences, particularly those involving pain and emotion, in order to create genuine and relatable characters.\n2. Relevance: The answer directly addresses the question of what the author believes is the key to creating a connection between the reader and the characters.\n3. Conciseness: The answer is clear and to the point, avoiding unnecessary information.\n\nFinal Result:"</li><li>'Reasoning:\n1. Context Grounding: The answer directly refers to the document, which mentions Mauro Rubin as the CEO of JoinPad during the event.\n2. Relevance: The answer specifically addresses the question asked about the CEO of JoinPad during the event.\n3. Conciseness: The answer is clear, direct, and does not include unnecessary information.\n\nFinal result:'</li></ul>                                                                                                                                                                                                                                                                                                                 |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.88     |
+
+## 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_rag_ds_gpt-4o_cot-instructions_remove_final_evaluation_e1_1726759371.6896")
+# Run inference
+preds = model("Reasoning:
+1. Context Grounding: The answer accurately describes the components of the Student Guide, which is well-supported by the provided document.
+2. Relevance: The answer directly addresses the question by listing the components of the British Medieval Student Guide.
+3. Conciseness: The answer is concise and includes only the necessary details regarding the components of the guide without extraneous information.
+
+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  | 87.0704 | 188 |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 34                    |
+| 1     | 37                    |
+
+### 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0056 | 1    | 0.2278        | -               |
+| 0.2809 | 50   | 0.2597        | -               |
+| 0.5618 | 100  | 0.2455        | -               |
+| 0.8427 | 150  | 0.1585        | -               |
+
+### 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

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+{
+  "_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

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+{
+  "__version__": {
+    "sentence_transformers": "3.1.0",
+    "transformers": "4.44.0",
+    "pytorch": "2.4.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

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+{
+  "labels": null,
+  "normalize_embeddings": false
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e625638d55bd3b6f156d737bcbb75cdd7ec9074d2547ca9615a72d1db0e28915
+size 437951328

model_head.pkl

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+oid sha256:8463b306fb306cad8751510f3aeca7975047852afe4f45e4045f20fead8861fe
+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

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+{
+  "max_seq_length": 512,
+  "do_lower_case": true
+}

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "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": {
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+      "lstrip": false,
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+      "single_word": false,
+      "special": true
+    },
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+      "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]"
+}