Add new SentenceTransformer model

3cf8e2b verified 3 months ago

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	---
	base_model: answerdotai/ModernBERT-large
	datasets:
	- sentence-transformers/stsb
	language:
	- en
	library_name: sentence-transformers
	metrics:
	- pearson_cosine
	- spearman_cosine
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:5749
	- loss:MatryoshkaLoss
	- loss:CoSENTLoss
	widget:
	- source_sentence: The man talked to a girl over the internet camera.
	sentences:
	- A group of elderly people pose around a dining table.
	- A teenager talks to a girl over a webcam.
	- There is no 'still' that is not relative to some other object.
	- source_sentence: A woman is writing something.
	sentences:
	- Two eagles are perched on a branch.
	- It refers to the maximum f-stop (which is defined as the ratio of focal length
	to effective aperture diameter).
	- A woman is chopping green onions.
	- source_sentence: The player shoots the winning points.
	sentences:
	- Minimum wage laws hurt the least skilled, least productive the most.
	- The basketball player is about to score points for his team.
	- Sheep are grazing in the field in front of a line of trees.
	- source_sentence: Stars form in star-formation regions, which itself develop from
	molecular clouds.
	sentences:
	- Although I believe Searle is mistaken, I don't think you have found the problem.
	- It may be possible for a solar system like ours to exist outside of a galaxy.
	- A blond-haired child performing on the trumpet in front of a house while his younger
	brother watches.
	- source_sentence: While Queen may refer to both Queen regent (sovereign) or Queen
	consort, the King has always been the sovereign.
	sentences:
	- At first, I thought this is a bit of a tricky question.
	- A man sitting on the floor in a room is strumming a guitar.
	- There is a very good reason not to refer to the Queen's spouse as "King" - because
	they aren't the King.
	model-index:
	- name: SentenceTransformer based on answerdotai/ModernBERT-large
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev
	type: sts-dev
	metrics:
	- type: pearson_cosine
	value: 0.8806182367761234
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8877448358326038
	name: Spearman Cosine
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test
	type: sts-test
	metrics:
	- type: pearson_cosine
	value: 0.8505275385588008
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8678439086871484
	name: Spearman Cosine
	---

	# SentenceTransformer based on answerdotai/ModernBERT-large

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) on the [stsb](https://huggingface.co/datasets/sentence-transformers/stsb) dataset. It maps sentences & paragraphs to a 1024-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: [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) <!-- at revision 4bbcbf40bed02ce487125bcb3c897ea9bdc88340 -->
	- Maximum Sequence Length: 8192 tokens
	- Output Dimensionality: 1024 dimensions
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- [stsb](https://huggingface.co/datasets/sentence-transformers/stsb)
	- 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': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel
	(1): Pooling({'word_embedding_dimension': 1024, '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("nickprock/ModernBERT-large-sts")
	# Run inference
	sentences = [
	'While Queen may refer to both Queen regent (sovereign) or Queen consort, the King has always been the sovereign.',
	'There is a very good reason not to refer to the Queen\'s spouse as "King" - because they aren\'t the King.',
	'A man sitting on the floor in a room is strumming a guitar.',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 1024]

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

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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>

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	## Evaluation

	### Metrics

	#### Semantic Similarity

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

	\| Metric \| sts-dev \| sts-test \|
	\|:--------------------\|:-----------\|:-----------\|
	\| pearson_cosine \| 0.8806 \| 0.8505 \|
	\| spearman_cosine \| 0.8877 \| 0.8678 \|

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	## Training Details

	### Training Dataset

	#### stsb

	* Dataset: [stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
	* Size: 5,749 training samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| score \|
	\|:--------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 6 tokens</li><li>mean: 10.16 tokens</li><li>max: 28 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 10.12 tokens</li><li>max: 25 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:-----------------------------------------------------------\|:----------------------------------------------------------------------\|:------------------\|
	\| <code>A plane is taking off.</code> \| <code>An air plane is taking off.</code> \| <code>1.0</code> \|
	\| <code>A man is playing a large flute.</code> \| <code>A man is playing a flute.</code> \| <code>0.76</code> \|
	\| <code>A man is spreading shreded cheese on a pizza.</code> \| <code>A man is spreading shredded cheese on an uncooked pizza.</code> \| <code>0.76</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
	```json
	{
	"loss": "CoSENTLoss",
	"matryoshka_dims": [
	768,
	512,
	256,
	128,
	64
	],
	"matryoshka_weights": [
	1,
	1,
	1,
	1,
	1
	],
	"n_dims_per_step": -1
	}
	```

	### Evaluation Dataset

	#### stsb

	* Dataset: [stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
	* Size: 1,500 evaluation samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| score \|
	\|:--------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:---------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 5 tokens</li><li>mean: 15.11 tokens</li><li>max: 44 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 15.1 tokens</li><li>max: 50 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:--------------------------------------------------\|:------------------------------------------------------\|:------------------\|
	\| <code>A man with a hard hat is dancing.</code> \| <code>A man wearing a hard hat is dancing.</code> \| <code>1.0</code> \|
	\| <code>A young child is riding a horse.</code> \| <code>A child is riding a horse.</code> \| <code>0.95</code> \|
	\| <code>A man is feeding a mouse to a snake.</code> \| <code>The man is feeding a mouse to the snake.</code> \| <code>1.0</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
	```json
	{
	"loss": "CoSENTLoss",
	"matryoshka_dims": [
	768,
	512,
	256,
	128,
	64
	],
	"matryoshka_weights": [
	1,
	1,
	1,
	1,
	1
	],
	"n_dims_per_step": -1
	}
	```

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

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 16
	- `per_device_eval_batch_size`: 16
	- `num_train_epochs`: 10
	- `warmup_ratio`: 0.1
	- `fp16`: True
	- `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`: 5e-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`: 10
	- `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`: 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`: 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 \| sts-dev_spearman_cosine \| sts-test_spearman_cosine \|
	\|:------:\|:----:\|:-------------:\|:---------------:\|:-----------------------:\|:------------------------:\|
	\| 0.2778 \| 100 \| 25.6058 \| 22.1112 \| 0.7926 \| - \|
	\| 0.5556 \| 200 \| 21.8238 \| 21.6575 \| 0.8499 \| - \|
	\| 0.8333 \| 300 \| 21.633 \| 21.2353 \| 0.8684 \| - \|
	\| 1.1111 \| 400 \| 22.3829 \| 21.8035 \| 0.8373 \| - \|
	\| 1.3889 \| 500 \| 22.0584 \| 23.0027 \| 0.8228 \| - \|
	\| 1.6667 \| 600 \| 21.6662 \| 22.3269 \| 0.8545 \| - \|
	\| 1.9444 \| 700 \| 21.2545 \| 21.3335 \| 0.8592 \| - \|
	\| 2.2222 \| 800 \| 20.5104 \| 21.8647 \| 0.8580 \| - \|
	\| 2.5 \| 900 \| 20.8763 \| 21.8435 \| 0.8631 \| - \|
	\| 2.7778 \| 1000 \| 20.3502 \| 21.9781 \| 0.8682 \| - \|
	\| 3.0556 \| 1100 \| 20.1262 \| 22.3008 \| 0.8662 \| - \|
	\| 3.3333 \| 1200 \| 20.0832 \| 21.4932 \| 0.8733 \| - \|
	\| 3.6111 \| 1300 \| 19.8407 \| 22.9816 \| 0.8661 \| - \|
	\| 3.8889 \| 1400 \| 20.027 \| 22.3290 \| 0.8729 \| - \|
	\| 4.1667 \| 1500 \| 19.2652 \| 23.7340 \| 0.8718 \| - \|
	\| 4.4444 \| 1600 \| 19.5304 \| 23.4634 \| 0.8766 \| - \|
	\| 4.7222 \| 1700 \| 19.6657 \| 23.3991 \| 0.8764 \| - \|
	\| 5.0 \| 1800 \| 18.8885 \| 24.1863 \| 0.8825 \| - \|
	\| 5.2778 \| 1900 \| 19.1028 \| 23.9508 \| 0.8781 \| - \|
	\| 5.5556 \| 2000 \| 19.0076 \| 23.6006 \| 0.8814 \| - \|
	\| 5.8333 \| 2100 \| 18.472 \| 24.0162 \| 0.8786 \| - \|
	\| 6.1111 \| 2200 \| 18.3949 \| 24.2914 \| 0.8839 \| - \|
	\| 6.3889 \| 2300 \| 17.6192 \| 26.2586 \| 0.8785 \| - \|
	\| 6.6667 \| 2400 \| 18.0109 \| 25.8655 \| 0.8820 \| - \|
	\| 6.9444 \| 2500 \| 17.8948 \| 24.8124 \| 0.8830 \| - \|
	\| 7.2222 \| 2600 \| 17.6087 \| 26.6571 \| 0.8837 \| - \|
	\| 7.5 \| 2700 \| 17.1578 \| 26.9229 \| 0.8838 \| - \|
	\| 7.7778 \| 2800 \| 17.0154 \| 27.1973 \| 0.8850 \| - \|
	\| 8.0556 \| 2900 \| 16.5323 \| 28.2881 \| 0.8836 \| - \|
	\| 8.3333 \| 3000 \| 16.0817 \| 28.4812 \| 0.8874 \| - \|
	\| 8.6111 \| 3100 \| 16.1146 \| 29.0393 \| 0.8869 \| - \|
	\| 8.8889 \| 3200 \| 16.0888 \| 29.6142 \| 0.8872 \| - \|
	\| 9.1667 \| 3300 \| 15.7132 \| 30.1223 \| 0.8873 \| - \|
	\| 9.4444 \| 3400 \| 15.2933 \| 30.4500 \| 0.8870 \| - \|
	\| 9.7222 \| 3500 \| 14.7292 \| 30.8898 \| 0.8876 \| - \|
	\| 10.0 \| 3600 \| 15.1894 \| 30.9508 \| 0.8877 \| - \|
	\| -1 \| -1 \| - \| - \| - \| 0.8678 \|


	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.4.0.dev0
	- Transformers: 4.49.0.dev0
	- PyTorch: 2.4.1+cu121
	- Accelerate: 0.34.2
	- 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",
	}
	```

	#### MatryoshkaLoss
	```bibtex
	@misc{kusupati2024matryoshka,
	title={Matryoshka Representation Learning},
	author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
	year={2024},
	eprint={2205.13147},
	archivePrefix={arXiv},
	primaryClass={cs.LG}
	}
	```

	#### CoSENTLoss
	```bibtex
	@online{kexuefm-8847,
	title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
	author={Su Jianlin},
	year={2022},
	month={Jan},
	url={https://kexue.fm/archives/8847},
	}
	```

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