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	---
	language:
	- en
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:557850
	- loss:MultipleNegativesRankingLoss
	base_model: FacebookAI/roberta-base
	widget:
	- source_sentence: A man is jumping unto his filthy bed.
	sentences:
	- A young male is looking at a newspaper while 2 females walks past him.
	- The bed is dirty.
	- The man is on the moon.
	- source_sentence: A carefully balanced male stands on one foot near a clean ocean
	beach area.
	sentences:
	- A man is ouside near the beach.
	- Three policemen patrol the streets on bikes
	- A man is sitting on his couch.
	- source_sentence: The man is wearing a blue shirt.
	sentences:
	- Near the trashcan the man stood and smoked
	- A man in a blue shirt leans on a wall beside a road with a blue van and red car
	with water in the background.
	- A man in a black shirt is playing a guitar.
	- source_sentence: The girls are outdoors.
	sentences:
	- Two girls riding on an amusement part ride.
	- a guy laughs while doing laundry
	- Three girls are standing together in a room, one is listening, one is writing
	on a wall and the third is talking to them.
	- source_sentence: A construction worker peeking out of a manhole while his coworker
	sits on the sidewalk smiling.
	sentences:
	- A worker is looking out of a manhole.
	- A man is giving a presentation.
	- The workers are both inside the manhole.
	datasets:
	- sentence-transformers/all-nli
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	---

	# SentenceTransformer based on FacebookAI/roberta-base

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) 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: [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) <!-- at revision e2da8e2f811d1448a5b465c236feacd80ffbac7b -->
	- Maximum Sequence Length: 256 tokens
	- Output Dimensionality: 768 dimensions
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
	- Language: en
	<!-- - 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': 256, 'do_lower_case': False}) with Transformer model: RobertaModel
	(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 = [
	'A construction worker peeking out of a manhole while his coworker sits on the sidewalk smiling.',
	'A worker is looking out of a manhole.',
	'The workers are both inside the manhole.',
	]
	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>
	-->

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	### 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

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	## Training Details

	### Training Dataset

	#### all-nli

	* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
	* Size: 557,850 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: 7 tokens</li><li>mean: 10.38 tokens</li><li>max: 45 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 12.8 tokens</li><li>max: 39 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 13.4 tokens</li><li>max: 50 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \|
	\|:---------------------------------------------------------------------------\|:-------------------------------------------------\|:-----------------------------------------------------------\|
	\| <code>A person on a horse jumps over a broken down airplane.</code> \| <code>A person is outdoors, on a horse.</code> \| <code>A person is at a diner, ordering an omelette.</code> \|
	\| <code>Children smiling and waving at camera</code> \| <code>There are children present</code> \| <code>The kids are frowning</code> \|
	\| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> \| <code>The boy does a skateboarding trick.</code> \| <code>The boy skates down the sidewalk.</code> \|
	* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim"
	}
	```

	### Evaluation Dataset

	#### all-nli

	* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
	* Size: 6,584 evaluation 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: 6 tokens</li><li>mean: 18.02 tokens</li><li>max: 66 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 9.81 tokens</li><li>max: 29 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 10.37 tokens</li><li>max: 29 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \|
	\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------\|:--------------------------------------------------------\|
	\| <code>Two women are embracing while holding to go packages.</code> \| <code>Two woman are holding packages.</code> \| <code>The men are fighting outside a deli.</code> \|
	\| <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> \| <code>Two kids in numbered jerseys wash their hands.</code> \| <code>Two kids in jackets walk to school.</code> \|
	\| <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> \| <code>A man selling donuts to a customer.</code> \| <code>A woman drinks her coffee in a small cafe.</code> \|
	* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim"
	}
	```

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

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 128
	- `per_device_eval_batch_size`: 128
	- `learning_rate`: 1e-05
	- `warmup_ratio`: 0.1
	- `batch_sampler`: no_duplicates

	#### 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`: 128
	- `per_device_eval_batch_size`: 128
	- `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`: 1e-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`: 3
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.1
	- `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`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| Validation Loss \|
	\|:------:\|:----:\|:-------------:\|:---------------:\|
	\| 0.0011 \| 5 \| - \| 5.1316 \|
	\| 0.0023 \| 10 \| - \| 5.1293 \|
	\| 0.0034 \| 15 \| - \| 5.1253 \|
	\| 0.0046 \| 20 \| - \| 5.1196 \|
	\| 0.0057 \| 25 \| - \| 5.1120 \|
	\| 0.0069 \| 30 \| - \| 5.1025 \|
	\| 0.0080 \| 35 \| - \| 5.0908 \|
	\| 0.0092 \| 40 \| - \| 5.0768 \|
	\| 0.0103 \| 45 \| - \| 5.0603 \|
	\| 0.0115 \| 50 \| - \| 5.0409 \|
	\| 0.0126 \| 55 \| - \| 5.0183 \|
	\| 0.0138 \| 60 \| - \| 4.9921 \|
	\| 0.0149 \| 65 \| - \| 4.9616 \|
	\| 0.0161 \| 70 \| - \| 4.9262 \|
	\| 0.0172 \| 75 \| - \| 4.8847 \|
	\| 0.0184 \| 80 \| - \| 4.8359 \|
	\| 0.0195 \| 85 \| - \| 4.7789 \|
	\| 0.0206 \| 90 \| - \| 4.7131 \|
	\| 0.0218 \| 95 \| - \| 4.6367 \|
	\| 0.0229 \| 100 \| 5.1885 \| 4.5468 \|
	\| 0.0241 \| 105 \| - \| 4.4403 \|
	\| 0.0252 \| 110 \| - \| 4.3148 \|
	\| 0.0264 \| 115 \| - \| 4.1678 \|
	\| 0.0275 \| 120 \| - \| 3.9960 \|
	\| 0.0287 \| 125 \| - \| 3.7965 \|
	\| 0.0298 \| 130 \| - \| 3.5700 \|
	\| 0.0310 \| 135 \| - \| 3.3183 \|
	\| 0.0321 \| 140 \| - \| 3.0434 \|
	\| 0.0333 \| 145 \| - \| 2.7582 \|
	\| 0.0344 \| 150 \| - \| 2.4786 \|
	\| 0.0356 \| 155 \| - \| 2.2217 \|
	\| 0.0367 \| 160 \| - \| 1.9959 \|
	\| 0.0379 \| 165 \| - \| 1.8082 \|
	\| 0.0390 \| 170 \| - \| 1.6611 \|
	\| 0.0401 \| 175 \| - \| 1.5397 \|
	\| 0.0413 \| 180 \| - \| 1.4406 \|
	\| 0.0424 \| 185 \| - \| 1.3592 \|
	\| 0.0436 \| 190 \| - \| 1.2935 \|
	\| 0.0447 \| 195 \| - \| 1.2393 \|
	\| 0.0459 \| 200 \| 3.2102 \| 1.1935 \|
	\| 0.0470 \| 205 \| - \| 1.1555 \|
	\| 0.0482 \| 210 \| - \| 1.1221 \|
	\| 0.0493 \| 215 \| - \| 1.0947 \|
	\| 0.0505 \| 220 \| - \| 1.0703 \|
	\| 0.0516 \| 225 \| - \| 1.0504 \|
	\| 0.0528 \| 230 \| - \| 1.0319 \|
	\| 0.0539 \| 235 \| - \| 1.0165 \|
	\| 0.0551 \| 240 \| - \| 1.0011 \|
	\| 0.0562 \| 245 \| - \| 0.9874 \|
	\| 0.0574 \| 250 \| - \| 0.9739 \|
	\| 0.0585 \| 255 \| - \| 0.9596 \|
	\| 0.0596 \| 260 \| - \| 0.9462 \|
	\| 0.0608 \| 265 \| - \| 0.9348 \|
	\| 0.0619 \| 270 \| - \| 0.9237 \|
	\| 0.0631 \| 275 \| - \| 0.9136 \|
	\| 0.0642 \| 280 \| - \| 0.9036 \|
	\| 0.0654 \| 285 \| - \| 0.8938 \|
	\| 0.0665 \| 290 \| - \| 0.8842 \|
	\| 0.0677 \| 295 \| - \| 0.8755 \|
	\| 0.0688 \| 300 \| 1.6043 \| 0.8665 \|
	\| 0.0700 \| 305 \| - \| 0.8554 \|
	\| 0.0711 \| 310 \| - \| 0.8430 \|
	\| 0.0723 \| 315 \| - \| 0.8302 \|
	\| 0.0734 \| 320 \| - \| 0.8176 \|
	\| 0.0746 \| 325 \| - \| 0.8079 \|
	\| 0.0757 \| 330 \| - \| 0.7993 \|
	\| 0.0769 \| 335 \| - \| 0.7927 \|
	\| 0.0780 \| 340 \| - \| 0.7864 \|
	\| 0.0791 \| 345 \| - \| 0.7797 \|
	\| 0.0803 \| 350 \| - \| 0.7713 \|
	\| 0.0814 \| 355 \| - \| 0.7635 \|
	\| 0.0826 \| 360 \| - \| 0.7564 \|
	\| 0.0837 \| 365 \| - \| 0.7484 \|
	\| 0.0849 \| 370 \| - \| 0.7418 \|
	\| 0.0860 \| 375 \| - \| 0.7329 \|
	\| 0.0872 \| 380 \| - \| 0.7236 \|
	\| 0.0883 \| 385 \| - \| 0.7142 \|
	\| 0.0895 \| 390 \| - \| 0.7050 \|
	\| 0.0906 \| 395 \| - \| 0.6964 \|
	\| 0.0918 \| 400 \| 1.3624 \| 0.6888 \|


	### Framework Versions
	- Python: 3.12.8
	- Sentence Transformers: 3.4.1
	- Transformers: 4.48.3
	- PyTorch: 2.2.0+cu121
	- Accelerate: 1.3.0
	- 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",
	}
	```

	#### 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}
	}
	```

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