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	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:5749
	- loss:CosineSimilarityLoss
	base_model: sentence-transformers/all-mpnet-base-v2
	widget:
	- source_sentence: Young woman in riding gear on top of horse.
	sentences:
	- Italy‚Äôs centre-left splinters in presidential vote
	- The woman is riding on the brown horse.
	- Mali's Interim President Sworn Into Office
	- source_sentence: Sony reports record annual loss
	sentences:
	- A woman is playing a flute.
	- A man and a woman kiss.
	- Sony forecasts record annual loss of $6.4bn
	- source_sentence: A clear plastic chair in front of a bookcase.
	sentences:
	- Allen defends self against Farrow's abuse claims
	- Ehud Olmert sentenced to six years in Israel
	- a clear plastic chair in front of book shelves.
	- source_sentence: KLCI Futures traded mixed at mid-day
	sentences:
	- KL shares mixed at mid-day
	- NATO helicopter makes hard landing in E. Afghanistan
	- Sewol ferry crew faces trial
	- source_sentence: We in Britain think differently to Americans.
	sentences:
	- south korea has had a bullet train system since the 1980s.
	- Originally Posted by zaf We in Britain think differently to Americans.
	- Car bombings kill 13 civilians in Iraqi capital
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	metrics:
	- pearson_cosine
	- spearman_cosine
	model-index:
	- name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: Unknown
	type: unknown
	metrics:
	- type: pearson_cosine
	value: 0.9075334661878893
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.9060484206473507
	name: Spearman Cosine
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev
	type: sts-dev
	metrics:
	- type: pearson_cosine
	value: 0.9075334589342524
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.9060484206473507
	name: Spearman Cosine
	---

	# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). 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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 9a3225965996d404b775526de6dbfe85d3368642 -->
	- Maximum Sequence Length: 384 tokens
	- Output Dimensionality: 768 dimensions
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	<!-- - 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': 384, '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})
	(2): Normalize()
	)
	```

	## 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 = [
	'We in Britain think differently to Americans.',
	'Originally Posted by zaf We in Britain think differently to Americans.',
	'south korea has had a bullet train system since the 1980s.',
	]
	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)

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	</details>
	-->

	<!--
	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

	<details><summary>Click to expand</summary>

	</details>
	-->

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	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
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	## Evaluation

	### Metrics

	#### Semantic Similarity

	* Datasets: `` and `sts-dev`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| \| sts-dev \|
	\|:--------------------\|:----------\|:----------\|
	\| pearson_cosine \| 0.9075 \| 0.9075 \|
	\| spearman_cosine \| 0.906 \| 0.906 \|

	<!--
	## 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.
	-->

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	### Recommendations

	What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.
	-->

	## Training Details

	### Training Dataset

	#### Unnamed Dataset


	* Size: 5,749 training samples
	* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence_0 \| sentence_1 \| label \|
	\|:--------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 6 tokens</li><li>mean: 14.16 tokens</li><li>max: 45 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 14.18 tokens</li><li>max: 49 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \| label \|
	\|:----------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------\|:--------------------------------\|
	\| <code>US Senate to vote on fiscal cliff deal as deadline nears</code> \| <code>Fiscal cliff: House delays vote on fiscal cliff deal - live</code> \| <code>0.5599999904632569</code> \|
	\| <code>This is America, my friends, and it should not happen here," he said to loud applause.</code> \| <code>"This is America, my friends, and it should not happen here."</code> \| <code>0.65</code> \|
	\| <code>Books To Help Kids Talk About Boston Marathon News</code> \| <code>Report of two explosions at finish line of Boston Marathon</code> \| <code>0.1600000023841858</code> \|
	* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
	```json
	{
	"loss_fct": "torch.nn.modules.loss.MSELoss"
	}
	```

	### Training Hyperparameters
	#### Non-Default Hyperparameters

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 32
	- `num_train_epochs`: 10
	- `multi_dataset_batch_sampler`: round_robin

	#### All Hyperparameters
	<details><summary>Click to expand</summary>

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: steps
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 32
	- `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`: 5e-05
	- `weight_decay`: 0.0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1
	- `num_train_epochs`: 10
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.0
	- `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`: False
	- `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`: 0
	- `dataloader_prefetch_factor`: None
	- `past_index`: -1
	- `disable_tqdm`: False
	- `remove_unused_columns`: True
	- `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
	- `dispatch_batches`: None
	- `split_batches`: 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`: round_robin

	</details>

	### Training Logs
	<details><summary>Click to expand</summary>

	\| Epoch \| Step \| Training Loss \| spearman_cosine \| sts-dev_spearman_cosine \|
	\|:------:\|:----:\|:-------------:\|:---------------:\|:-----------------------:\|
	\| 0 \| 0 \| - \| 0.8811 \| - \|
	\| 0.1 \| 18 \| - \| - \| 0.8816 \|
	\| 0.2 \| 36 \| - \| - \| 0.8834 \|
	\| 0.3 \| 54 \| - \| - \| 0.8847 \|
	\| 0.4 \| 72 \| - \| - \| 0.8894 \|
	\| 0.5 \| 90 \| - \| - \| 0.8933 \|
	\| 0.6 \| 108 \| - \| - \| 0.8966 \|
	\| 0.7 \| 126 \| - \| - \| 0.9005 \|
	\| 0.8 \| 144 \| - \| - \| 0.9020 \|
	\| 0.9 \| 162 \| - \| - \| 0.9010 \|
	\| 1.0 \| 180 \| - \| - \| 0.9001 \|
	\| 1.1 \| 198 \| - \| - \| 0.9022 \|
	\| 1.2 \| 216 \| - \| - \| 0.9018 \|
	\| 1.3 \| 234 \| - \| - \| 0.9015 \|
	\| 1.4 \| 252 \| - \| - \| 0.9029 \|
	\| 1.5 \| 270 \| - \| - \| 0.9044 \|
	\| 1.6 \| 288 \| - \| - \| 0.9049 \|
	\| 1.7 \| 306 \| - \| - \| 0.9051 \|
	\| 1.8 \| 324 \| - \| - \| 0.9033 \|
	\| 1.9 \| 342 \| - \| - \| 0.9039 \|
	\| 2.0 \| 360 \| - \| - \| 0.9050 \|
	\| 2.1 \| 378 \| - \| - \| 0.9042 \|
	\| 2.2 \| 396 \| - \| - \| 0.9041 \|
	\| 2.3 \| 414 \| - \| - \| 0.9040 \|
	\| 2.4 \| 432 \| - \| - \| 0.9048 \|
	\| 2.5 \| 450 \| - \| - \| 0.9045 \|
	\| 2.6 \| 468 \| - \| - \| 0.9046 \|
	\| 2.7 \| 486 \| - \| - \| 0.9047 \|
	\| 2.7778 \| 500 \| 0.0153 \| - \| - \|
	\| 2.8 \| 504 \| - \| - \| 0.9057 \|
	\| 2.9 \| 522 \| - \| - \| 0.9065 \|
	\| 3.0 \| 540 \| - \| - \| 0.9074 \|
	\| 3.1 \| 558 \| - \| - \| 0.9073 \|
	\| 3.2 \| 576 \| - \| - \| 0.9065 \|
	\| 3.3 \| 594 \| - \| - \| 0.9046 \|
	\| 3.4 \| 612 \| - \| - \| 0.9057 \|
	\| 3.5 \| 630 \| - \| - \| 0.9069 \|
	\| 3.6 \| 648 \| - \| - \| 0.9062 \|
	\| 3.7 \| 666 \| - \| - \| 0.9061 \|
	\| 3.8 \| 684 \| - \| - \| 0.9050 \|
	\| 3.9 \| 702 \| - \| - \| 0.9050 \|
	\| 4.0 \| 720 \| - \| - \| 0.9048 \|
	\| 4.1 \| 738 \| - \| - \| 0.9052 \|
	\| 4.2 \| 756 \| - \| - \| 0.9055 \|
	\| 4.3 \| 774 \| - \| - \| 0.9060 \|
	\| 4.4 \| 792 \| - \| - \| 0.9059 \|
	\| 4.5 \| 810 \| - \| - \| 0.9064 \|
	\| 4.6 \| 828 \| - \| - \| 0.9063 \|
	\| 4.7 \| 846 \| - \| - \| 0.9063 \|
	\| 4.8 \| 864 \| - \| - \| 0.9067 \|
	\| 4.9 \| 882 \| - \| - \| 0.9059 \|
	\| 5.0 \| 900 \| - \| - \| 0.9052 \|
	\| 5.1 \| 918 \| - \| - \| 0.9061 \|
	\| 5.2 \| 936 \| - \| - \| 0.9057 \|
	\| 5.3 \| 954 \| - \| - \| 0.9053 \|
	\| 5.4 \| 972 \| - \| - \| 0.9060 \|
	\| 5.5 \| 990 \| - \| - \| 0.9050 \|
	\| 5.5556 \| 1000 \| 0.0051 \| - \| - \|
	\| 5.6 \| 1008 \| - \| - \| 0.9053 \|
	\| 5.7 \| 1026 \| - \| - \| 0.9052 \|
	\| 5.8 \| 1044 \| - \| - \| 0.9056 \|
	\| 5.9 \| 1062 \| - \| - \| 0.9062 \|
	\| 6.0 \| 1080 \| - \| - \| 0.9056 \|
	\| 6.1 \| 1098 \| - \| - \| 0.9054 \|
	\| 6.2 \| 1116 \| - \| - \| 0.9058 \|
	\| 6.3 \| 1134 \| - \| - \| 0.9058 \|
	\| 6.4 \| 1152 \| - \| - \| 0.9056 \|
	\| 6.5 \| 1170 \| - \| - \| 0.9057 \|
	\| 6.6 \| 1188 \| - \| - \| 0.9055 \|
	\| 6.7 \| 1206 \| - \| - \| 0.9055 \|
	\| 6.8 \| 1224 \| - \| - \| 0.9053 \|
	\| 6.9 \| 1242 \| - \| - \| 0.9053 \|
	\| 7.0 \| 1260 \| - \| - \| 0.9053 \|
	\| 7.1 \| 1278 \| - \| - \| 0.9057 \|
	\| 7.2 \| 1296 \| - \| - \| 0.9055 \|
	\| 7.3 \| 1314 \| - \| - \| 0.9053 \|
	\| 7.4 \| 1332 \| - \| - \| 0.9056 \|
	\| 7.5 \| 1350 \| - \| - \| 0.9059 \|
	\| 7.6 \| 1368 \| - \| - \| 0.9060 \|
	\| 7.7 \| 1386 \| - \| - \| 0.9057 \|
	\| 7.8 \| 1404 \| - \| - \| 0.9058 \|
	\| 7.9 \| 1422 \| - \| - \| 0.9057 \|
	\| 8.0 \| 1440 \| - \| - \| 0.9058 \|
	\| 8.1 \| 1458 \| - \| - \| 0.9059 \|
	\| 8.2 \| 1476 \| - \| - \| 0.9060 \|
	\| 8.3 \| 1494 \| - \| - \| 0.9056 \|
	\| 8.3333 \| 1500 \| 0.0031 \| - \| - \|
	\| 8.4 \| 1512 \| - \| - \| 0.9057 \|
	\| 8.5 \| 1530 \| - \| - \| 0.9060 \|
	\| 8.6 \| 1548 \| - \| - \| 0.9058 \|
	\| 8.7 \| 1566 \| - \| - \| 0.9060 \|
	\| 8.8 \| 1584 \| - \| - \| 0.9062 \|
	\| 8.9 \| 1602 \| - \| - \| 0.9061 \|
	\| 9.0 \| 1620 \| - \| - \| 0.9061 \|
	\| 9.1 \| 1638 \| - \| - \| 0.9061 \|
	\| 9.2 \| 1656 \| - \| - \| 0.9059 \|
	\| 9.3 \| 1674 \| - \| - \| 0.9060 \|
	\| 9.4 \| 1692 \| - \| - \| 0.9061 \|
	\| 9.5 \| 1710 \| - \| - \| 0.9061 \|
	\| 9.6 \| 1728 \| - \| - \| 0.9061 \|
	\| 9.7 \| 1746 \| - \| - \| 0.9060 \|
	\| 9.8 \| 1764 \| - \| - \| 0.9061 \|
	\| 9.9 \| 1782 \| - \| - \| 0.9061 \|
	\| 10.0 \| 1800 \| - \| 0.9060 \| 0.9060 \|

	</details>

	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.3.1
	- Transformers: 4.47.1
	- PyTorch: 2.5.1+cu121
	- Accelerate: 1.2.1
	- Datasets: 3.2.0
	- Tokenizers: 0.21.0

	## 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",
	}
	```

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