Add new SentenceTransformer model

d942f56 verified 17 days ago

20.9 kB

	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:77201
	- loss:CosineSimilarityLoss
	base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
	widget:
	- source_sentence: '"17 тэрбумын хэрэгт холбогдсон хүмүүсийг шалгаж байна."'
	sentences:
	- Шинэ сайд томилогдлоо."
	- '"Авлига авсан хүмүүсийг шалгаж байна."'
	- Шүүхийг засварлах мөнгө байхгүй байна."
	- source_sentence: '"Гэмт хэрэг үйлдсэн. "'
	sentences:
	- LIKE дар.
	- Саусгоби сэндс компанийн хэргээр мөрдөн байцаалт явагдаж байна."
	- '"Гэмтэл учруулсан."'
	- source_sentence: '"Иргэдийн хүсэлтийг шинэчлэлийн Засгийн газар хэрэгжүүлнэ."'
	sentences:
	- '"Засгийн газар иргэдийн хүсэлтийг хэрэгжүүлэх бодолтой байна."'
	- '"Ц.Болд албан тушаалаа ашиглан төсвөөс мөнгө завшсан байна."'
	- Шүүх хараат бус байх ёстой."
	- source_sentence: '"Ам.долларын ханш суларснаас бэрхшээл үүсэж байна."'
	sentences:
	- '"тушаал"'
	- Шүүхийн шийдвэрийн талаарх судалгаа хийнэ."
	- '"Валютын ханшийн өөрчлөлтөөс болж бэрхшээл гарч байна."'
	- source_sentence: '"Сэтгүүлч анд маань хоёр дахь номоо хэвлэлтээс гаргажээ"'
	sentences:
	- БНХАУ-ын аж үйлдвэрлэлийн үйлдвэрлэлт буурсан.
	- Жастин Бибер, Кэти Перри нарын элэглэл хамгийн түрүүнд дүрслэгдэх аж.
	- '"Л.Болормаагийн хоёр дахь ном “Завгүй” хэмээн нэрийджээ."'
	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: dev t
	type: dev-t
	metrics:
	- type: pearson_cosine
	value: 0.9547459589724314
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.9538075641510714
	name: Spearman Cosine
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: test t
	type: test-t
	metrics:
	- type: pearson_cosine
	value: 0.956384303059334
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.9566981709702497
	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-mongolian-minilm-mn_v2")
	# Run inference
	sentences = [
	'"Сэтгүүлч анд маань хоёр дахь номоо хэвлэлтээс гаргажээ"',
	'"Л.Болормаагийн хоёр дахь ном “Завгүй” хэмээн нэрийджээ."',
	'БНХАУ-ын аж үйлдвэрлэлийн үйлдвэрлэлт буурсан.',
	]
	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

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

	\| Metric \| dev-t \| test-t \|
	\|:--------------------\|:-----------\|:-----------\|
	\| pearson_cosine \| 0.9547 \| 0.9564 \|
	\| spearman_cosine \| 0.9538 \| 0.9567 \|

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	## Training Details

	### Training Dataset

	#### csv

	* Dataset: csv
	* Size: 77,201 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: 3 tokens</li><li>mean: 16.02 tokens</li><li>max: 96 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 14.66 tokens</li><li>max: 87 tokens</li></ul> \| <ul><li>min: -0.14</li><li>mean: 0.63</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:---------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------\|:--------------------------------\|
	\| <code>Маргааш мэдээлэл өгнө</code> \| <code>Хэвлэлийн хурал болно.</code> \| <code>0.5448001623153687</code> \|
	\| <code>Дотоод аудитын шалгалтаар 2012-2013 оны үйл ажиллагаанд 16 зөрчил илэрлээ</code> \| <code>“Монголын Хөрөнгийн Бирж” ТӨХК-ийн Төлөөлөн удирдах зөвлөл болон Гүйцэтгэх удирдлагад 13 зөвлөмж өгөгдсөн байна.</code> \| <code>0.4059729874134063</code> \|
	\| <code>"хохирогчид ажлын байраар хангагдана"</code> \| <code>"ажил олддог болно."</code> \| <code>0.6021140813827515</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: 77,201 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: 3 tokens</li><li>mean: 16.53 tokens</li><li>max: 85 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 14.68 tokens</li><li>max: 83 tokens</li></ul> \| <ul><li>min: -0.04</li><li>mean: 0.62</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:---------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------\|:--------------------------------\|
	\| <code>Ченжүүд мэдээллийн сүлжээтэй лут холбогдсон байх юм</code> \| <code>"Энд ноолуурын үнэ асуусан хэдэн нөхөд яваад байна" гээд хэлчихсэн бололтой юм</code> \| <code>0.3234536349773407</code> \|
	\| <code>Хий дэлбэрэлт гарсан тухай мэдээлэл байна уу?</code> \| <code>Мэдээлэл цуглуулж байна.</code> \| <code>0.3009476661682129</code> \|
	\| <code>"Энэ нь хэн нэгнээр дамжуулж биш өөрөө сонгоно гэсэн утгатай.</code> \| <code>Өөрөө сонгоно гэсэн утгатай."</code> \| <code>0.770484447479248</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`: 5
	- `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`: 5
	- `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 \| dev-t_spearman_cosine \| test-t_spearman_cosine \|
	\|:------:\|:-----:\|:-------------:\|:---------------:\|:---------------------:\|:----------------------:\|
	\| 0 \| 0 \| - \| - \| 1.0000 \| - \|
	\| 0.1727 \| 500 \| 0.0046 \| - \| - \| - \|
	\| 0.3454 \| 1000 \| 0.0054 \| 0.0042 \| 0.9549 \| - \|
	\| 0.5181 \| 1500 \| 0.0069 \| - \| - \| - \|
	\| 0.6908 \| 2000 \| 0.008 \| 0.0067 \| 0.9298 \| - \|
	\| 0.8636 \| 2500 \| 0.0076 \| - \| - \| - \|
	\| 1.0363 \| 3000 \| 0.0075 \| 0.0065 \| 0.9317 \| - \|
	\| 1.2090 \| 3500 \| 0.0069 \| - \| - \| - \|
	\| 1.3817 \| 4000 \| 0.0063 \| 0.0063 \| 0.9366 \| - \|
	\| 1.5544 \| 4500 \| 0.0055 \| - \| - \| - \|
	\| 1.7271 \| 5000 \| 0.0049 \| 0.0057 \| 0.9411 \| - \|
	\| 1.8998 \| 5500 \| 0.0045 \| - \| - \| - \|
	\| 2.0725 \| 6000 \| 0.0045 \| 0.0056 \| 0.9405 \| - \|
	\| 2.2453 \| 6500 \| 0.004 \| - \| - \| - \|
	\| 2.4180 \| 7000 \| 0.0038 \| 0.0053 \| 0.9432 \| - \|
	\| 2.5907 \| 7500 \| 0.0034 \| - \| - \| - \|
	\| 2.7634 \| 8000 \| 0.0032 \| 0.0053 \| 0.9448 \| - \|
	\| 2.9361 \| 8500 \| 0.0029 \| - \| - \| - \|
	\| 3.1088 \| 9000 \| 0.0028 \| 0.0051 \| 0.9459 \| - \|
	\| 3.2815 \| 9500 \| 0.0025 \| - \| - \| - \|
	\| 3.4542 \| 10000 \| 0.0023 \| 0.0047 \| 0.9498 \| - \|
	\| 3.6269 \| 10500 \| 0.0022 \| - \| - \| - \|
	\| 3.7997 \| 11000 \| 0.0021 \| 0.0046 \| 0.9510 \| - \|
	\| 3.9724 \| 11500 \| 0.0019 \| - \| - \| - \|
	\| 4.1451 \| 12000 \| 0.0019 \| 0.0046 \| 0.9525 \| - \|
	\| 4.3178 \| 12500 \| 0.0016 \| - \| - \| - \|
	\| 4.4905 \| 13000 \| 0.0016 \| 0.0045 \| 0.9528 \| - \|
	\| 4.6632 \| 13500 \| 0.0014 \| - \| - \| - \|
	\| 4.8359 \| 14000 \| 0.0013 \| 0.0044 \| 0.9538 \| - \|
	\| 5.0 \| 14475 \| - \| - \| - \| 0.9567 \|


	### 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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