Add new SentenceTransformer model.

93683ef verified 6 months ago

23.5 kB

	---
	language:
	- en
	library_name: sentence-transformers
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- loss:MSELoss
	base_model: nreimers/TinyBERT_L-4_H-312_v2
	metrics:
	- pearson_cosine
	- spearman_cosine
	- pearson_manhattan
	- spearman_manhattan
	- pearson_euclidean
	- spearman_euclidean
	- pearson_dot
	- spearman_dot
	- pearson_max
	- spearman_max
	- negative_mse
	widget:
	- source_sentence: A woman at home.
	sentences:
	- The woman is inside.
	- The woman is performing for an audience.
	- The two men are freinds
	- source_sentence: boys play football
	sentences:
	- Rival college football players are playing a football game.
	- A man looks at his watch at a bus stop.
	- A woman walking on an old bridge near a mountain.
	- source_sentence: Nobody has a pot
	sentences:
	- Nobody has a suit
	- A woman riding a bicycle on the street.
	- The front is decorated with Ethiopian themes and motifs.
	- source_sentence: A dog plays ball.
	sentences:
	- A dog with a ball.
	- A man looking into a microscope in a lab
	- Children go past their parents.
	- source_sentence: A person standing
	sentences:
	- There is a person standing outside
	- A young man plays a racing video game.
	- Two children playing on the floor with toy trains.
	pipeline_tag: sentence-similarity
	co2_eq_emissions:
	emissions: 3.457859864142588
	energy_consumed: 0.00889591477312334
	source: codecarbon
	training_type: fine-tuning
	on_cloud: false
	cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
	ram_total_size: 31.777088165283203
	hours_used: 0.054
	hardware_used: 1 x NVIDIA GeForce RTX 3090
	model-index:
	- name: SentenceTransformer based on nreimers/TinyBERT_L-4_H-312_v2
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev
	type: sts-dev
	metrics:
	- type: pearson_cosine
	value: 0.8077673131159315
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8208863013753134
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8225516575982812
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8203236078973807
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8215663439432439
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8202318953605339
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7901487535994149
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7914362691291718
	name: Spearman Dot
	- type: pearson_max
	value: 0.8225516575982812
	name: Pearson Max
	- type: spearman_max
	value: 0.8208863013753134
	name: Spearman Max
	- task:
	type: knowledge-distillation
	name: Knowledge Distillation
	dataset:
	name: Unknown
	type: unknown
	metrics:
	- type: negative_mse
	value: -50.125449895858765
	name: Negative Mse
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test
	type: sts-test
	metrics:
	- type: pearson_cosine
	value: 0.7516961775809978
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.7558402072520215
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7762734499549059
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.75965556867712
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7705568379382428
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.7553604477247078
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7306801501272192
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7097993872384684
	name: Spearman Dot
	- type: pearson_max
	value: 0.7762734499549059
	name: Pearson Max
	- type: spearman_max
	value: 0.75965556867712
	name: Spearman Max
	---

	# SentenceTransformer based on nreimers/TinyBERT_L-4_H-312_v2

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nreimers/TinyBERT_L-4_H-312_v2](https://huggingface.co/nreimers/TinyBERT_L-4_H-312_v2) on the [sentence-transformers/wikipedia-en-sentences](https://huggingface.co/datasets/sentence-transformers/wikipedia-en-sentences) dataset. It maps sentences & paragraphs to a 312-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: [nreimers/TinyBERT_L-4_H-312_v2](https://huggingface.co/nreimers/TinyBERT_L-4_H-312_v2) <!-- at revision d782507ee95c6565fe5924fcd6090999055e8db6 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 312 tokens
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- [sentence-transformers/wikipedia-en-sentences](https://huggingface.co/datasets/sentence-transformers/wikipedia-en-sentences)
	- 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': 512, 'do_lower_case': False}) with Transformer model: BertModel
	(1): Pooling({'word_embedding_dimension': 312, '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("tomaarsen/TinyBERT_L-4_H-312_v2-distilled-from-stsb-roberta-base-v2")
	# Run inference
	sentences = [
	'A person standing',
	'There is a person standing outside',
	'A young man plays a racing video game.',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 312]

	# Get the similarity scores for the embeddings
	similarities = model.similarity(embeddings)
	print(similarities.shape)
	# [3, 3]
	```

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	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

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

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	## Evaluation

	### Metrics

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

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.8078 \|
	\| spearman_cosine \| 0.8209 \|
	\| pearson_manhattan \| 0.8226 \|
	\| spearman_manhattan \| 0.8203 \|
	\| pearson_euclidean \| 0.8216 \|
	\| spearman_euclidean \| 0.8202 \|
	\| pearson_dot \| 0.7901 \|
	\| spearman_dot \| 0.7914 \|
	\| pearson_max \| 0.8226 \|
	\| spearman_max \| 0.8209 \|

	#### Knowledge Distillation

	* Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)

	\| Metric \| Value \|
	\|:-----------------\|:-------------\|
	\| negative_mse \| -50.1254 \|

	#### Semantic Similarity
	* Dataset: `sts-test`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.7517 \|
	\| spearman_cosine \| 0.7558 \|
	\| pearson_manhattan \| 0.7763 \|
	\| spearman_manhattan \| 0.7597 \|
	\| pearson_euclidean \| 0.7706 \|
	\| spearman_euclidean \| 0.7554 \|
	\| pearson_dot \| 0.7307 \|
	\| spearman_dot \| 0.7098 \|
	\| pearson_max \| 0.7763 \|
	\| spearman_max \| 0.7597 \|

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	## Training Details

	### Training Dataset

	#### sentence-transformers/wikipedia-en-sentences

	* Dataset: [sentence-transformers/wikipedia-en-sentences](https://huggingface.co/datasets/sentence-transformers/wikipedia-en-sentences) at [4a0972d](https://huggingface.co/datasets/sentence-transformers/wikipedia-en-sentences/tree/4a0972dcb781b5b5d27799798f032606421dd422)
	* Size: 200,000 training samples
	* Columns: <code>sentence</code> and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence \| label \|
	\|:--------\|:----------------------------------------------------------------------------------\|:-------------------------------------\|
	\| type \| string \| list \|
	\| details \| <ul><li>min: 4 tokens</li><li>mean: 12.24 tokens</li><li>max: 52 tokens</li></ul> \| <ul><li>size: 312 elements</li></ul> \|
	* Samples:
	\| sentence \| label \|
	\|:---------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------\|
	\| <code>A person on a horse jumps over a broken down airplane.</code> \| <code>[-0.09614687412977219, 0.6815224885940552, 2.702199935913086, 1.8371250629425049, -1.2949433326721191, ...]</code> \|
	\| <code>Children smiling and waving at camera</code> \| <code>[2.769360303878784, 3.074428081512451, -7.291755676269531, 5.248741149902344, 2.85081148147583, ...]</code> \|
	\| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> \| <code>[-3.0669667720794678, 2.9899890422821045, -1.253997802734375, 6.15218448638916, 0.5838223099708557, ...]</code> \|
	* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/losses.html#mseloss)

	### Evaluation Dataset

	#### sentence-transformers/wikipedia-en-sentences

	* Dataset: [sentence-transformers/wikipedia-en-sentences](https://huggingface.co/datasets/sentence-transformers/wikipedia-en-sentences) at [4a0972d](https://huggingface.co/datasets/sentence-transformers/wikipedia-en-sentences/tree/4a0972dcb781b5b5d27799798f032606421dd422)
	* Size: 10,000 evaluation samples
	* Columns: <code>sentence</code> and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence \| label \|
	\|:--------\|:----------------------------------------------------------------------------------\|:-------------------------------------\|
	\| type \| string \| list \|
	\| details \| <ul><li>min: 5 tokens</li><li>mean: 13.23 tokens</li><li>max: 57 tokens</li></ul> \| <ul><li>size: 312 elements</li></ul> \|
	* Samples:
	\| sentence \| label \|
	\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------\|
	\| <code>Two women are embracing while holding to go packages.</code> \| <code>[6.200135707855225, -2.0865142345428467, -2.1313390731811523, -1.9593913555145264, -1.081985592842102, ...]</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>[1.7725015878677368, 0.6873414516448975, -2.5191268920898438, 3.866339683532715, 2.853647470474243, ...]</code> \|
	\| <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> \| <code>[-3.317653179168701, 3.0908589363098145, 0.1683920919895172, -2.4405274391174316, -3.1366524696350098, ...]</code> \|
	* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/losses.html#mseloss)

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

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 64
	- `per_device_eval_batch_size`: 64
	- `learning_rate`: 0.0001
	- `num_train_epochs`: 1
	- `warmup_ratio`: 0.1
	- `fp16`: True
	- `load_best_model_at_end`: True

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

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: steps
	- `prediction_loss_only`: False
	- `per_device_train_batch_size`: 64
	- `per_device_eval_batch_size`: 64
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 1
	- `eval_accumulation_steps`: None
	- `learning_rate`: 0.0001
	- `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
	- `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`: 0
	- `dataloader_prefetch_factor`: None
	- `past_index`: -1
	- `disable_tqdm`: False
	- `remove_unused_columns`: True
	- `label_names`: None
	- `load_best_model_at_end`: True
	- `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`: None
	- `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_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| loss \| negative_mse \| sts-dev_spearman_cosine \| sts-test_spearman_cosine \|
	\|:--------:\|:--------:\|:-------------:\|:----------:\|:------------:\|:-----------------------:\|:------------------------:\|
	\| 0.032 \| 100 \| 0.8847 \| - \| - \| - \| - \|
	\| 0.064 \| 200 \| 0.8136 \| - \| - \| - \| - \|
	\| 0.096 \| 300 \| 0.697 \| - \| - \| - \| - \|
	\| 0.128 \| 400 \| 0.6128 \| - \| - \| - \| - \|
	\| 0.16 \| 500 \| 0.5634 \| 0.6324 \| -63.2356 \| 0.7564 \| - \|
	\| 0.192 \| 600 \| 0.5294 \| - \| - \| - \| - \|
	\| 0.224 \| 700 \| 0.5035 \| - \| - \| - \| - \|
	\| 0.256 \| 800 \| 0.4861 \| - \| - \| - \| - \|
	\| 0.288 \| 900 \| 0.4668 \| - \| - \| - \| - \|
	\| 0.32 \| 1000 \| 0.4515 \| 0.5673 \| -56.7263 \| 0.7965 \| - \|
	\| 0.352 \| 1100 \| 0.4376 \| - \| - \| - \| - \|
	\| 0.384 \| 1200 \| 0.4274 \| - \| - \| - \| - \|
	\| 0.416 \| 1300 \| 0.4178 \| - \| - \| - \| - \|
	\| 0.448 \| 1400 \| 0.4098 \| - \| - \| - \| - \|
	\| 0.48 \| 1500 \| 0.4053 \| 0.5354 \| -53.5381 \| 0.8091 \| - \|
	\| 0.512 \| 1600 \| 0.3934 \| - \| - \| - \| - \|
	\| 0.544 \| 1700 \| 0.391 \| - \| - \| - \| - \|
	\| 0.576 \| 1800 \| 0.3848 \| - \| - \| - \| - \|
	\| 0.608 \| 1900 \| 0.3785 \| - \| - \| - \| - \|
	\| 0.64 \| 2000 \| 0.3737 \| 0.5168 \| -51.6829 \| 0.8159 \| - \|
	\| 0.672 \| 2100 \| 0.3716 \| - \| - \| - \| - \|
	\| 0.704 \| 2200 \| 0.3695 \| - \| - \| - \| - \|
	\| 0.736 \| 2300 \| 0.3666 \| - \| - \| - \| - \|
	\| 0.768 \| 2400 \| 0.3616 \| - \| - \| - \| - \|
	\| 0.8 \| 2500 \| 0.358 \| 0.5067 \| -50.6687 \| 0.8189 \| - \|
	\| 0.832 \| 2600 \| 0.3551 \| - \| - \| - \| - \|
	\| 0.864 \| 2700 \| 0.3544 \| - \| - \| - \| - \|
	\| 0.896 \| 2800 \| 0.3524 \| - \| - \| - \| - \|
	\| 0.928 \| 2900 \| 0.3524 \| - \| - \| - \| - \|
	\| 0.96 \| 3000 \| 0.3529 \| 0.5013 \| -50.1254 \| 0.8209 \| - \|
	\| 0.992 \| 3100 \| 0.3496 \| - \| - \| - \| - \|
	\| 1.0 \| 3125 \| - \| - \| - \| - \| 0.7558 \|

	* The bold row denotes the saved checkpoint.

	### Environmental Impact
	Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
	- Energy Consumed: 0.009 kWh
	- Carbon Emitted: 0.003 kg of CO2
	- Hours Used: 0.054 hours

	### Training Hardware
	- On Cloud: No
	- GPU Model: 1 x NVIDIA GeForce RTX 3090
	- CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
	- RAM Size: 31.78 GB

	### Framework Versions
	- Python: 3.11.6
	- Sentence Transformers: 3.0.0.dev0
	- Transformers: 4.41.0.dev0
	- PyTorch: 2.3.0+cu121
	- Accelerate: 0.26.1
	- Datasets: 2.18.0
	- Tokenizers: 0.19.1

	## 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",
	}
	```

	#### MSELoss
	```bibtex
	@inproceedings{reimers-2020-multilingual-sentence-bert,
	title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
	author = "Reimers, Nils and Gurevych, Iryna",
	booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
	month = "11",
	year = "2020",
	publisher = "Association for Computational Linguistics",
	url = "https://arxiv.org/abs/2004.09813",
	}
	```

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