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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:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:18963
+- loss:MultipleNegativesRankingLoss
+base_model: sentence-transformers/paraphrase-mpnet-base-v2
+widget:
+- source_sentence: If the comatose man had previously expressed a desire to be euthanized
+    in such a situation, respecting his autonomy would support euthanasia.
+  sentences:
+  - If the comatose man had previously expressed a desire for euthanasia in such circumstances,
+    there may be a duty to respect his autonomy, which would support the action.
+  - If the man is believed to be suffering in his comatose state or there is a significant
+    burden on his family, there may be a duty to alleviate suffering that supports
+    euthanasia.
+  - As a living being, the rat may warrant a duty of care from humans, which may include
+    providing it with appropriate medical treatment or humane euthanasia in case of
+    suffering.
+- source_sentence: Resisting authoritarianism can defend individual freedom and undermine
+    oppressive regimes.
+  sentences:
+  - Resisting authoritarianism can be a means of exercising the right to free speech
+    and expression, which may be suppressed by the government.
+  - If retreating serves to protect the lives of soldiers and civilians, then it upholds
+    the value of the duty to protect.
+  - Resisting authoritarianism could result in negative consequences for safety and
+    security if violence is used to resist.
+- source_sentence: Saving someone upholds their fundamental right to life, as it prevents
+    them from experiencing harm or death.
+  sentences:
+  - Donating the money to charity has the potential to benefit those in need and can
+    be seen as fulfilling a duty to improve the well-being of others.
+  - Saving someone may preserve their freedom and ability to make choices in their
+    life.
+  - If saving someone involves protecting their body from injury or harm, their right
+    to bodily integrity is respected.
+- source_sentence: Helping those in need, such as a starving person, promotes a sense
+    of community and responsibility towards fellow humans.
+  sentences:
+  - We have a moral responsibility to treat others with respect and dignity, regardless
+    of their race. Hanging out with black people allows for the opportunity to demonstrate
+    this respect.
+  - A starving person's right to life is at stake, and providing them with food can
+    help protect this fundamental right.
+  - Providing aid and resources to someone in need is an expression of the duty to
+    promote the well-being of others.
+- source_sentence: The marriage of Baptiste and Hannah demonstrates their commitment
+    to sharing their lives and supporting one another.
+  sentences:
+  - Helping others may be a moral duty, but using unethical means like cheating goes
+    against other moral principles.
+  - If the marriage brings happiness to Baptiste and Hannah, then they are pursuing
+    their right to happiness.
+  - By getting married, Baptiste and Hannah take on a duty to care for each other,
+    both emotionally and materially.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on sentence-transformers/paraphrase-mpnet-base-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) on the train dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) <!-- at revision 0446e4ee4c8cef910c1b1dd164b6276d66bd47c0 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - train
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel 
+  (1): Pooling({'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})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'The marriage of Baptiste and Hannah demonstrates their commitment to sharing their lives and supporting one another.',
+    'By getting married, Baptiste and Hannah take on a duty to care for each other, both emotionally and materially.',
+    'If the marriage brings happiness to Baptiste and Hannah, then they are pursuing their right to happiness.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### 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 Dataset
+
+#### train
+
+* Dataset: train
+* Size: 18,963 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                             | positive                                                                          | negative                                                                           |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                            | string                                                                             |
+  | details | <ul><li>min: 10 tokens</li><li>mean: 25.92 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 28.31 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 28.69 tokens</li><li>max: 67 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                                                                | positive                                                                                                                                                                     | negative                                                                                                                                                                                                                 |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Saving the group of people from harm by diverting the trolley supports the value of preserving life.</code>                                                                                     | <code>The group of people tied to the tracks have a right to life, which is protected when the trolley is diverted to save them.</code>                                      | <code>Diverting the trolley reduces overall harm by preventing the deaths of many people at the cost of one person's life.</code>                                                                                        |
+  | <code>The bake sale could be seen as an expression of support for a particular cause, and the right to freely express oneself and associate with others who share the same views is important.</code> | <code>The bake sale might be seen as a form of protest or support for a specific cause, and individuals have the right to engage in peaceful protest or show support.</code> | <code>If the bake sale directly or indirectly promotes religious discrimination, this can infringe on the fundamental right of individuals to be free from discrimination or harm due to their religious beliefs.</code> |
+  | <code>Children have a right to life, and saving them from danger upholds this right.</code>                                                                                                           | <code>Children should be protected from harm, abuse, and danger, and saving them ensures this right is respected.</code>                                                     | <code>Children have a right to grow up with access to healthcare, education, and a nurturing environment. Saving them may help secure these rights.</code>                                                               |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 40,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `overwrite_output_dir`: True
+- `per_device_train_batch_size`: 32
+- `learning_rate`: 2.1456771788455288e-05
+- `num_train_epochs`: 2
+- `warmup_ratio`: 0.03254893834779507
+- `fp16`: True
+- `dataloader_num_workers`: 4
+- `remove_unused_columns`: False
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: True
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 32
+- `per_device_eval_batch_size`: 8
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2.1456771788455288e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 2
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.03254893834779507
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 4
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: False
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss |
+|:------:|:----:|:-------------:|
+| 0.0337 | 20   | 0.2448        |
+| 0.0675 | 40   | 0.1918        |
+| 0.1012 | 60   | 0.14          |
+| 0.1349 | 80   | 0.186         |
+| 0.1686 | 100  | 0.1407        |
+| 0.2024 | 120  | 0.1672        |
+| 0.2361 | 140  | 0.1832        |
+| 0.2698 | 160  | 0.116         |
+| 0.3035 | 180  | 0.1341        |
+| 0.3373 | 200  | 0.2118        |
+| 0.3710 | 220  | 0.1274        |
+| 0.4047 | 240  | 0.1993        |
+| 0.4384 | 260  | 0.1561        |
+| 0.4722 | 280  | 0.1517        |
+| 0.5059 | 300  | 0.1635        |
+| 0.5396 | 320  | 0.1646        |
+| 0.5734 | 340  | 0.1337        |
+| 0.6071 | 360  | 0.1406        |
+| 0.6408 | 380  | 0.1114        |
+| 0.6745 | 400  | 0.1314        |
+| 0.7083 | 420  | 0.1481        |
+| 0.7420 | 440  | 0.1932        |
+| 0.7757 | 460  | 0.1568        |
+| 0.8094 | 480  | 0.1319        |
+| 0.8432 | 500  | 0.1536        |
+| 0.8769 | 520  | 0.1462        |
+| 0.9106 | 540  | 0.1336        |
+| 0.9444 | 560  | 0.1453        |
+| 0.9781 | 580  | 0.2005        |
+| 1.0118 | 600  | 0.1265        |
+| 1.0455 | 620  | 0.0702        |
+| 1.0793 | 640  | 0.0739        |
+| 1.1130 | 660  | 0.049         |
+| 1.1467 | 680  | 0.0613        |
+| 1.1804 | 700  | 0.0663        |
+| 1.2142 | 720  | 0.0726        |
+| 1.2479 | 740  | 0.0822        |
+| 1.2816 | 760  | 0.0651        |
+| 1.3153 | 780  | 0.0603        |
+| 1.3491 | 800  | 0.0468        |
+| 1.3828 | 820  | 0.061         |
+| 1.4165 | 840  | 0.0891        |
+| 1.4503 | 860  | 0.0607        |
+| 1.4840 | 880  | 0.0673        |
+| 1.5177 | 900  | 0.0728        |
+| 1.5514 | 920  | 0.065         |
+| 1.5852 | 940  | 0.0824        |
+| 1.6189 | 960  | 0.0695        |
+| 1.6526 | 980  | 0.0626        |
+| 1.6863 | 1000 | 0.0525        |
+| 1.7201 | 1020 | 0.0482        |
+| 1.7538 | 1040 | 0.0968        |
+| 1.7875 | 1060 | 0.0717        |
+| 1.8212 | 1080 | 0.0704        |
+| 1.8550 | 1100 | 0.0666        |
+| 1.8887 | 1120 | 0.0841        |
+| 1.9224 | 1140 | 0.0682        |
+| 1.9562 | 1160 | 0.0584        |
+| 1.9899 | 1180 | 0.0423        |
+
+
+### Framework Versions
+- Python: 3.9.21
+- Sentence Transformers: 4.1.0
+- Transformers: 4.52.4
+- PyTorch: 2.6.0+cu124
+- Accelerate: 1.5.2
+- Datasets: 3.4.1
+- Tokenizers: 0.21.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## 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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+{
+  "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": "4.52.4",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "4.1.0",
+    "transformers": "4.52.4",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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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"
+  }
+]

optimizer.pt

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rng_state.pth

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scaler.pt

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scheduler.pt

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sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "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": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "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
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "104": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "30526": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
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