Add new SentenceTransformer model

9db0545 verified about 1 month ago

19.8 kB

	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:11113
	- loss:CosineSimilarityLoss
	base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
	widget:
	- source_sentence: Фэнүүддээ сайхан мэдээг дуулгажээ
	sentences:
	- Фэнүүддээ муу мэдээг дуулгажээ
	- Жүжгийг 14.00 болон 16.00 цагаас тоглоно.
	- Киноны дараа хэлэлцүүлэг болно.
	- source_sentence: Фрида 22 насандаа Диего Риверагийн эхнэр болжээ
	sentences:
	- Хүрэл металлын найрлагад олон төрлийн элементүүд ордог бөгөөд цэвэр хүрлийг гарган
	авдаг
	- “Жонон” хамтлаг олон улсын хэмжээнд тоглолт хийхээр төлөвлөж байна.
	- Тэдний гэр бүлийн амьдрал буцалж байв.
	- source_sentence: Тоглолтыг ССАЖЯ-ны дэмжлэгтэй зохион байгуулжээ
	sentences:
	- Тоглолт аравдугаар сарын 26-нд болно.
	- Цомогт мал аж ахуйн сэдэвтэй дуунууд багтсан
	- Тоглолт өвөрмөц тайз, онцгой хөтөлбөртэй
	- source_sentence: '"TJ" энтертайнменттэй хамтран ажиллаж байна'
	sentences:
	- Тодорхой хэмжээгээр урлагтайгаа л байна
	- “Алтан хуур” наадмын зохион байгуулагчид мэдээлэл хийлээ
	- Тэд хамтран podcast хийж байна
	- source_sentence: дөнгөж арван настайдаа олгойны хагалгаанд орж байсан
	sentences:
	- Түүнээс би монгол эрчүүд ийм, тийм гэж боддог учраас хань, нөхрөөрөө сонгохгүй
	байгаа юм биш
	- '"Домог" чуулгын тоглолт Монгол иргэдэд зориулагджээ'
	- Энэ мэтчилэн болсон болоогүй өвчин тусдаг нэг тийм л хүүхэд байсан юм шиг байгаа
	юм.
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	metrics:
	- pearson_cosine
	- spearman_cosine
	model-index:
	- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev
	type: sts-dev
	metrics:
	- type: pearson_cosine
	value: 0.9672191293060537
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.9652101071464687
	name: Spearman Cosine
	---

	# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) on the csv dataset. It maps sentences & paragraphs to a 384-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/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision 8d6b950845285729817bf8e1af1861502c2fed0c -->
	- Maximum Sequence Length: 128 tokens
	- Output Dimensionality: 384 dimensions
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- csv
	<!-- - 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': 128, 'do_lower_case': False}) with Transformer model: BertModel
	(1): Pooling({'word_embedding_dimension': 384, '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("gmunkhtur/paraphrase-multilingual-minilm-l12-v2-mn")
	# Run inference
	sentences = [
	'дөнгөж арван настайдаа олгойны хагалгаанд орж байсан',
	'Энэ мэтчилэн болсон болоогүй өвчин тусдаг нэг тийм л хүүхэд байсан юм шиг байгаа юм.',
	'"Домог" чуулгын тоглолт Монгол иргэдэд зориулагджээ',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 384]

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

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	## Evaluation

	### Metrics

	#### Semantic Similarity

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

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.9672 \|
	\| spearman_cosine \| 0.9652 \|

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	## Training Details

	### Training Dataset

	#### csv

	* Dataset: csv
	* Size: 11,113 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: 5 tokens</li><li>mean: 19.59 tokens</li><li>max: 116 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 19.86 tokens</li><li>max: 104 tokens</li></ul> \| <ul><li>min: -0.07</li><li>mean: 0.49</li><li>max: 0.98</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:-----------------------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:--------------------------------\|
	\| <code>"Гамлет" жүжиг УДЭТ-д тоглогдоно</code> \| <code>"Скапений дамшиглал" жүжиг УДЭТ-д тоглогдоно.</code> \| <code>0.7848628163337708</code> \|
	\| <code>Киноны эхэнд нөхөртэйгээ дөнгөж танилцаж байх үедээ М.Тетчэр “Би нөхрийнхөө сүүдэр дор амьдарч, аяга угаахын төлөө төрсөн хүн биш</code> \| <code>Харин киноны төгсгөлд нас барсан нөхрийгөө амьд мэтээр төсөөлж, түүнтэй үргэлж ярилцан ганцаардмал байдлаасаа ангижрахыг оролддог настай эмэгтэй цайны аягаа өөрөө угаачихаад цааш явж байгааг харуулсан юм</code> \| <code>0.5108565092086792</code> \|
	\| <code>Арга хэмжээний нээлтээр тоглолт болно</code> \| <code>Нээлтийн арга хэмжээ нь тоглолт юм</code> \| <code>0.8344829082489014</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"
	}
	```

	### Evaluation Dataset

	#### csv

	* Dataset: csv
	* Size: 11,113 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: 20.22 tokens</li><li>max: 128 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 20.11 tokens</li><li>max: 128 tokens</li></ul> \| <ul><li>min: -0.11</li><li>mean: 0.49</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:-----------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:--------------------------------\|
	\| <code>Гиннессийн амжилтад бүртгүүлсэн байна</code> \| <code>Швед улсад очиж тоглох гэнэ.</code> \| <code>0.3108136057853699</code> \|
	\| <code>PLAYTIME 2014 наадам нь Улаанбаатар хотын орчин үеийн хөгжмийн соёлыг хөгжүүлэхэд чиглэгдэнэ.</code> \| <code>PLAYTIME 2014 наадам нь залууст амралт чөлөөт цагаа цэвэр агаарт өнгөрүүлэх боломжийг олгоно.</code> \| <code>0.577198326587677</code> \|
	\| <code>Альфа артист-аар тодорсон дуучин олон шагналын эзэн болно</code> \| <code>Альфа артист-аар тодорсон нэг дуучин ирэх гуравдугаар сард Хонконгод болох Бруно Марсын тоглолтыг үзэх клип хийлгэх гэх зэрэг олон шагналын эзэн болох юм байна.</code> \| <code>0.6577209830284119</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`: 16
	- `per_device_eval_batch_size`: 16
	- `num_train_epochs`: 1
	- `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`: 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`: 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 \|
	\|:------:\|:----:\|:-------------:\|:---------------:\|:-----------------------:\|
	\| 0 \| 0 \| - \| - \| 1.0000 \|
	\| 0.1799 \| 100 \| 0.0045 \| - \| - \|
	\| 0.3597 \| 200 \| 0.006 \| - \| - \|
	\| 0.5396 \| 300 \| 0.006 \| - \| - \|
	\| 0.7194 \| 400 \| 0.005 \| - \| - \|
	\| 0.8993 \| 500 \| 0.0047 \| 0.0030 \| 0.9652 \|


	### 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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