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1_Pooling/config.json

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+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "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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+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: <Question> What will the ministry of tourism do to boost the flow of tourists
+    to the country during the holiday season? </Question> <Answer> Anticipating a
+    surge in holiday travel, the Ministry of Tourism is rolling out a multi-pronged
+    strategy to attract tourists and ensure a memorable experience. The centerpiece
+    is the "Festive Wonderland" campaign, transforming major cities into enchanting
+    winter scenes with illuminated streets, snow machines, and festive markets overflowing
+    with local crafts and delicacies. </Answer> <Question> Was the cost of such a
+    strategy announced by the ministry? </Question>
+- text: <Question> How does the company offer help for parents with their children?
+    </Question> <Answer> At Jack Track, we understand the importance of supporting
+    our employees who are parents. We offer a range of assistance programs to help
+    parents with their children. Our comprehensive benefits package includes flexible
+    work schedules and remote work options, allowing parents to balance their professional
+    and family responsibilities effectively. </Answer> <Question> How often can we
+    work remotely? </Question>
+- text: <Question> Is Store Manager considered rank 3 or rank 2? </Question> <Answer>
+    In our organization's hierarchical structure, the position of Store Manager is
+    considered as a Rank 2 role. </Answer> <Question> What does this level of responsibility
+    typically involves? </Question>
+- text: <Question> How many days off do we get during Easter? </Question> <Answer>
+    During Easter, employees typically enjoy a generous 15-day break, which includes
+    weekends and public holidays. This extended period allows for ample time to relax
+    and celebrate the holiday season with family and friends. </Answer> <Question>
+    What about Christmas? </Question>
+- text: <Question> What is the highest grossing movie at the box office? </Question>
+    <Answer> The highest-grossing movie at the box office is Avatar. </Answer> <Question>
+    How much money did the movie make? </Question>
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: true
+base_model: sentence-transformers/all-mpnet-base-v2
+model-index:
+- name: SetFit with sentence-transformers/all-mpnet-base-v2
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.9347826086956522
+      name: Accuracy
+---
+
+# SetFit with sentence-transformers/all-mpnet-base-v2
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 384 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>'<Question> Who was the Germany national team captain during the 2006 World cup? </Question> <Answer> Michael Ballack was the Germany national team captrain during the 2006 world cup </Answer> <Question> How old was he? </Question>'</li><li>'<Question> Who was the Germany national team captain during the 2006 World cup? </Question> <Answer> Michael Ballack was the Germany national team captrain during the 2006 world cup </Answer> <Question> Who won it back then? </Question>'</li><li>'<Question> How old was Ronaldo when he moved to Real Madrid? </Question> <Answer> Ronaldo moved to Real Madrid after leaving Inter when he was 25 years old. </Answer> <Question> What year did he leave? </Question>'</li></ul>                                                                                                                                                                                                                              |
+| 0     | <ul><li>'<Question> Which ocean surrounds Antarctica? </Question> <Answer> The ocean that surrounds Antarctica is the Southern Ocean. </Answer> <Question> What challenges do scientists face when conducting research in Antarctica? </Question>'</li><li>'<Question> Name a country in Oceania. </Question> <Answer> A country in Oceania is Australia. </Answer> <Question> What are some of the popular tourist attractions in Oceania? </Question>'</li><li>"<Question> What's the significance of the Suez Canal? </Question> <Answer> The Suez Canal holds great importance as a crucial Egyptian waterway that links the Mediterranean Sea to the Red Sea. It plays a pivotal role in enhancing maritime trade and transportation between Europe and Asia, providing ships with a shorter and safer route compared to the arduous journey around the southern tip of Africa. </Answer> <Question> How has the Suez Canal impacted global trade? </Question>"</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.9348   |
+
+## 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("setfit_model_id")
+# Run inference
+preds = model("<Question> What is the highest grossing movie at the box office? </Question> <Answer> The highest-grossing movie at the box office is Avatar. </Answer> <Question> How much money did the movie make? </Question>")
+```
+
+<!--
+### 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   | 14  | 44.4406 | 221 |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 240                   |
+| 1     | 248                   |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (3, 3)
+- 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    | 0.5762        | -               |
+| 0.0410 | 50   | 0.2742        | -               |
+| 0.0820 | 100  | 0.2188        | -               |
+| 0.1230 | 150  | 0.0586        | -               |
+| 0.1639 | 200  | 0.0194        | -               |
+| 0.2049 | 250  | 0.0028        | -               |
+| 0.2459 | 300  | 0.0004        | -               |
+| 0.2869 | 350  | 0.0003        | -               |
+| 0.3279 | 400  | 0.0002        | -               |
+| 0.3689 | 450  | 0.0001        | -               |
+| 0.4098 | 500  | 0.0001        | -               |
+| 0.4508 | 550  | 0.0001        | -               |
+| 0.4918 | 600  | 0.0001        | -               |
+| 0.5328 | 650  | 0.0006        | -               |
+| 0.5738 | 700  | 0.0001        | -               |
+| 0.6148 | 750  | 0.0001        | -               |
+| 0.6557 | 800  | 0.0001        | -               |
+| 0.6967 | 850  | 0.0001        | -               |
+| 0.7377 | 900  | 0.0001        | -               |
+| 0.7787 | 950  | 0.0001        | -               |
+| 0.8197 | 1000 | 0.0001        | -               |
+| 0.8607 | 1050 | 0.0001        | -               |
+| 0.9016 | 1100 | 0.0001        | -               |
+| 0.9426 | 1150 | 0.0001        | -               |
+| 0.9836 | 1200 | 0.0           | -               |
+| 0.0008 | 1    | 0.0           | -               |
+| 0.0410 | 50   | 0.0           | -               |
+| 0.0820 | 100  | 0.0003        | -               |
+| 0.1230 | 150  | 0.0005        | -               |
+| 0.1639 | 200  | 0.0013        | -               |
+| 0.2049 | 250  | 0.0008        | -               |
+| 0.2459 | 300  | 0.0           | -               |
+| 0.2869 | 350  | 0.0           | -               |
+| 0.3279 | 400  | 0.0           | -               |
+| 0.3689 | 450  | 0.0           | -               |
+| 0.4098 | 500  | 0.0           | -               |
+| 0.4508 | 550  | 0.0           | -               |
+| 0.4918 | 600  | 0.0           | -               |
+| 0.5328 | 650  | 0.0           | -               |
+| 0.5738 | 700  | 0.0           | -               |
+| 0.6148 | 750  | 0.0           | -               |
+| 0.6557 | 800  | 0.008         | -               |
+| 0.6967 | 850  | 0.0285        | -               |
+| 0.7377 | 900  | 0.012         | -               |
+| 0.7787 | 950  | 0.0073        | -               |
+| 0.8197 | 1000 | 0.0013        | -               |
+| 0.8607 | 1050 | 0.0           | -               |
+| 0.9016 | 1100 | 0.0           | -               |
+| 0.9426 | 1150 | 0.0           | -               |
+| 0.9836 | 1200 | 0.0013        | -               |
+| 1.0246 | 1250 | 0.0013        | -               |
+| 1.0656 | 1300 | 0.0           | -               |
+| 1.1066 | 1350 | 0.0           | -               |
+| 1.1475 | 1400 | 0.0           | -               |
+| 1.1885 | 1450 | 0.0           | -               |
+| 1.2295 | 1500 | 0.0           | -               |
+| 1.2705 | 1550 | 0.0           | -               |
+| 1.3115 | 1600 | 0.0           | -               |
+| 1.3525 | 1650 | 0.0022        | -               |
+| 1.3934 | 1700 | 0.0           | -               |
+| 1.4344 | 1750 | 0.0           | -               |
+| 1.4754 | 1800 | 0.0           | -               |
+| 1.5164 | 1850 | 0.0013        | -               |
+| 1.5574 | 1900 | 0.0           | -               |
+| 1.5984 | 1950 | 0.0           | -               |
+| 1.6393 | 2000 | 0.0           | -               |
+| 1.6803 | 2050 | 0.0           | -               |
+| 1.7213 | 2100 | 0.0           | -               |
+| 1.7623 | 2150 | 0.0           | -               |
+| 1.8033 | 2200 | 0.0           | -               |
+| 1.8443 | 2250 | 0.0048        | -               |
+| 1.8852 | 2300 | 0.0023        | -               |
+| 1.9262 | 2350 | 0.0049        | -               |
+| 1.9672 | 2400 | 0.0012        | -               |
+| 2.0082 | 2450 | 0.0           | -               |
+| 2.0492 | 2500 | 0.0           | -               |
+| 2.0902 | 2550 | 0.0           | -               |
+| 2.1311 | 2600 | 0.0           | -               |
+| 2.1721 | 2650 | 0.0           | -               |
+| 2.2131 | 2700 | 0.0           | -               |
+| 2.2541 | 2750 | 0.0           | -               |
+| 2.2951 | 2800 | 0.0           | -               |
+| 2.3361 | 2850 | 0.0           | -               |
+| 2.3770 | 2900 | 0.0           | -               |
+| 2.4180 | 2950 | 0.0           | -               |
+| 2.4590 | 3000 | 0.0           | -               |
+| 2.5    | 3050 | 0.0           | -               |
+| 2.5410 | 3100 | 0.0           | -               |
+| 2.5820 | 3150 | 0.0           | -               |
+| 2.6230 | 3200 | 0.0           | -               |
+| 2.6639 | 3250 | 0.0           | -               |
+| 2.7049 | 3300 | 0.0           | -               |
+| 2.7459 | 3350 | 0.0           | -               |
+| 2.7869 | 3400 | 0.0           | -               |
+| 2.8279 | 3450 | 0.0           | -               |
+| 2.8689 | 3500 | 0.0           | -               |
+| 2.9098 | 3550 | 0.0007        | -               |
+| 2.9508 | 3600 | 0.0           | -               |
+| 2.9918 | 3650 | 0.0           | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.1.0
+- Sentence Transformers: 3.2.1
+- Transformers: 4.42.2
+- PyTorch: 2.5.1+cu121
+- Datasets: 3.1.0
+- 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

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+{
+  "_name_or_path": "sentence-transformers/all-mpnet-base-v2",
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.42.2",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.2.1",
+    "transformers": "4.42.2",
+    "pytorch": "2.5.1+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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+size 437967672

model_head.pkl

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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": 384,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
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+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "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": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "normalized": true,
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+    },
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+    "30526": {
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+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "max_length": 128,
+  "model_max_length": 384,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}