Add new SentenceTransformer model.

e9bf279 verified about 2 months ago

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	---
	language:
	- en
	license: apache-2.0
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:557850
	- loss:MultipleNegativesRankingLoss
	base_model: microsoft/mpnet-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
	metrics:
	- cosine_accuracy
	- dot_accuracy
	- manhattan_accuracy
	- euclidean_accuracy
	- max_accuracy
	model-index:
	- name: MPNet base trained on AllNLI triplets
	results:
	- task:
	type: triplet
	name: Triplet
	dataset:
	name: all nli dev
	type: all-nli-dev
	metrics:
	- type: cosine_accuracy
	value: 0.9155528554070473
	name: Cosine Accuracy
	- type: dot_accuracy
	value: 0.08475091130012151
	name: Dot Accuracy
	- type: manhattan_accuracy
	value: 0.912363304981774
	name: Manhattan Accuracy
	- type: euclidean_accuracy
	value: 0.9113001215066828
	name: Euclidean Accuracy
	- type: max_accuracy
	value: 0.9155528554070473
	name: Max Accuracy
	- task:
	type: triplet
	name: Triplet
	dataset:
	name: all nli test
	type: all-nli-test
	metrics:
	- type: cosine_accuracy
	value: 0.9261612952035103
	name: Cosine Accuracy
	- type: dot_accuracy
	value: 0.07262823422605538
	name: Dot Accuracy
	- type: manhattan_accuracy
	value: 0.9196550158874263
	name: Manhattan Accuracy
	- type: euclidean_accuracy
	value: 0.9201089423513391
	name: Euclidean Accuracy
	- type: max_accuracy
	value: 0.9261612952035103
	name: Max Accuracy
	---

	# MPNet base trained on AllNLI triplets

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-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: [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 tokens
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
	- Language: en
	- License: apache-2.0

	### 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("bingcheng9/mpnet-base-all-nli-triplet")
	# 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]
	```

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	## Evaluation

	### Metrics

	#### Triplet
	* Dataset: `all-nli-dev`
	* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

	\| Metric \| Value \|
	\|:-------------------\|:-----------\|
	\| cosine_accuracy \| 0.9156 \|
	\| dot_accuracy \| 0.0848 \|
	\| manhattan_accuracy \| 0.9124 \|
	\| euclidean_accuracy \| 0.9113 \|
	\| max_accuracy \| 0.9156 \|

	#### Triplet
	* Dataset: `all-nli-test`
	* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

	\| Metric \| Value \|
	\|:-------------------\|:-----------\|
	\| cosine_accuracy \| 0.9262 \|
	\| dot_accuracy \| 0.0726 \|
	\| manhattan_accuracy \| 0.9197 \|
	\| euclidean_accuracy \| 0.9201 \|
	\| max_accuracy \| 0.9262 \|

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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: 10.46 tokens</li><li>max: 46 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 12.81 tokens</li><li>max: 40 tokens</li></ul> \| <ul><li>min: 5 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: 17.95 tokens</li><li>max: 63 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 9.78 tokens</li><li>max: 29 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 10.35 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`: 16
	- `per_device_eval_batch_size`: 16
	- `learning_rate`: 2e-05
	- `num_train_epochs`: 1
	- `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`: 16
	- `per_device_eval_batch_size`: 16
	- `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`: 2e-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`: 1
	- `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`: False
	- `hub_always_push`: False
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `include_inputs_for_metrics`: False
	- `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
	- `batch_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| loss \| all-nli-dev_max_accuracy \| all-nli-test_max_accuracy \|
	\|:-----:\|:----:\|:-------------:\|:------:\|:------------------------:\|:-------------------------:\|
	\| 0 \| 0 \| - \| - \| 0.6832 \| - \|
	\| 0.016 \| 100 \| 3.0282 \| 1.5784 \| 0.7751 \| - \|
	\| 0.032 \| 200 \| 1.2537 \| 0.9115 \| 0.7983 \| - \|
	\| 0.048 \| 300 \| 1.435 \| 0.7883 \| 0.8095 \| - \|
	\| 0.064 \| 400 \| 0.8952 \| 0.7637 \| 0.8112 \| - \|
	\| 0.08 \| 500 \| 0.8482 \| 0.8154 \| 0.8086 \| - \|
	\| 0.096 \| 600 \| 1.056 \| 0.8993 \| 0.8033 \| - \|
	\| 0.112 \| 700 \| 0.967 \| 0.8740 \| 0.8007 \| - \|
	\| 0.128 \| 800 \| 1.1139 \| 1.0261 \| 0.7930 \| - \|
	\| 0.144 \| 900 \| 1.1765 \| 0.9142 \| 0.8127 \| - \|
	\| 0.16 \| 1000 \| 1.1022 \| 0.8580 \| 0.7980 \| - \|
	\| 0.176 \| 1100 \| 1.1095 \| 1.0273 \| 0.7889 \| - \|
	\| 0.192 \| 1200 \| 1.0725 \| 0.9443 \| 0.7998 \| - \|
	\| 0.208 \| 1300 \| 0.9075 \| 0.8191 \| 0.8070 \| - \|
	\| 0.224 \| 1400 \| 0.7504 \| 0.8069 \| 0.8104 \| - \|
	\| 0.24 \| 1500 \| 0.815 \| 0.7824 \| 0.8193 \| - \|
	\| 0.256 \| 1600 \| 0.6089 \| 0.8256 \| 0.8168 \| - \|
	\| 0.272 \| 1700 \| 0.8689 \| 0.8470 \| 0.8079 \| - \|
	\| 0.288 \| 1800 \| 0.8359 \| 0.8588 \| 0.8103 \| - \|
	\| 0.304 \| 1900 \| 0.8157 \| 0.7955 \| 0.8129 \| - \|
	\| 0.32 \| 2000 \| 0.7511 \| 0.7027 \| 0.8467 \| - \|
	\| 0.336 \| 2100 \| 0.603 \| 0.7624 \| 0.8467 \| - \|
	\| 0.352 \| 2200 \| 0.6005 \| 0.7071 \| 0.8686 \| - \|
	\| 0.368 \| 2300 \| 0.8079 \| 0.7497 \| 0.8492 \| - \|
	\| 0.384 \| 2400 \| 0.7237 \| 0.6801 \| 0.8586 \| - \|
	\| 0.4 \| 2500 \| 0.669 \| 0.6595 \| 0.8694 \| - \|
	\| 0.416 \| 2600 \| 0.6013 \| 0.6700 \| 0.8587 \| - \|
	\| 0.432 \| 2700 \| 0.8929 \| 0.7217 \| 0.8645 \| - \|
	\| 0.448 \| 2800 \| 0.8627 \| 0.6720 \| 0.8521 \| - \|
	\| 0.464 \| 2900 \| 0.8279 \| 0.6561 \| 0.8698 \| - \|
	\| 0.48 \| 3000 \| 0.6893 \| 0.6243 \| 0.8692 \| - \|
	\| 0.496 \| 3100 \| 0.7609 \| 0.6052 \| 0.8711 \| - \|
	\| 0.512 \| 3200 \| 0.5704 \| 0.6042 \| 0.8677 \| - \|
	\| 0.528 \| 3300 \| 0.6117 \| 0.5398 \| 0.8827 \| - \|
	\| 0.544 \| 3400 \| 0.5231 \| 0.5743 \| 0.8797 \| - \|
	\| 0.56 \| 3500 \| 0.5231 \| 0.5817 \| 0.8923 \| - \|
	\| 0.576 \| 3600 \| 0.4825 \| 0.5309 \| 0.8911 \| - \|
	\| 0.592 \| 3700 \| 0.5464 \| 0.5261 \| 0.8961 \| - \|
	\| 0.608 \| 3800 \| 0.4846 \| 0.5017 \| 0.8979 \| - \|
	\| 0.624 \| 3900 \| 0.4896 \| 0.5280 \| 0.8947 \| - \|
	\| 0.64 \| 4000 \| 0.7499 \| 0.5435 \| 0.9061 \| - \|
	\| 0.656 \| 4100 \| 0.916 \| 0.5268 \| 0.9060 \| - \|
	\| 0.672 \| 4200 \| 0.8733 \| 0.4855 \| 0.9074 \| - \|
	\| 0.688 \| 4300 \| 0.6963 \| 0.4717 \| 0.9105 \| - \|
	\| 0.704 \| 4400 \| 0.5907 \| 0.4567 \| 0.9142 \| - \|
	\| 0.72 \| 4500 \| 0.5768 \| 0.4702 \| 0.9111 \| - \|
	\| 0.736 \| 4600 \| 0.6173 \| 0.4491 \| 0.9151 \| - \|
	\| 0.752 \| 4700 \| 0.6802 \| 0.4680 \| 0.9124 \| - \|
	\| 0.768 \| 4800 \| 0.6099 \| 0.4372 \| 0.9130 \| - \|
	\| 0.784 \| 4900 \| 0.5689 \| 0.4480 \| 0.9066 \| - \|
	\| 0.8 \| 5000 \| 0.6554 \| 0.4603 \| 0.9118 \| - \|
	\| 0.816 \| 5100 \| 0.511 \| 0.4356 \| 0.9116 \| - \|
	\| 0.832 \| 5200 \| 0.5725 \| 0.4246 \| 0.9092 \| - \|
	\| 0.848 \| 5300 \| 0.5196 \| 0.4359 \| 0.9107 \| - \|
	\| 0.864 \| 5400 \| 0.6112 \| 0.4403 \| 0.9104 \| - \|
	\| 0.88 \| 5500 \| 0.5233 \| 0.4236 \| 0.9115 \| - \|
	\| 0.896 \| 5600 \| 0.5467 \| 0.4217 \| 0.9127 \| - \|
	\| 0.912 \| 5700 \| 0.6109 \| 0.4199 \| 0.9156 \| - \|
	\| 0.928 \| 5800 \| 0.54 \| 0.4077 \| 0.9148 \| - \|
	\| 0.944 \| 5900 \| 0.6739 \| 0.4111 \| 0.9145 \| - \|
	\| 0.96 \| 6000 \| 0.723 \| 0.4170 \| 0.9154 \| - \|
	\| 0.976 \| 6100 \| 0.6753 \| 0.4162 \| 0.9154 \| - \|
	\| 0.992 \| 6200 \| 0.0591 \| 0.4157 \| 0.9156 \| - \|
	\| 1.0 \| 6250 \| - \| - \| - \| 0.9262 \|


	### Framework Versions
	- Python: 3.12.4
	- Sentence Transformers: 3.1.1
	- Transformers: 4.45.2
	- PyTorch: 2.2.2
	- Accelerate: 0.26.0
	- Datasets: 3.0.2
	- Tokenizers: 0.20.1

	## 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}
	}
	```

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