Add new SentenceTransformer model.

23e9052 verified 3 months ago

19.7 kB

	---
	base_model: colorfulscoop/sbert-base-ja
	library_name: sentence-transformers
	metrics:
	- cosine_accuracy
	- cosine_accuracy_threshold
	- cosine_f1
	- cosine_f1_threshold
	- cosine_precision
	- cosine_recall
	- cosine_ap
	- dot_accuracy
	- dot_accuracy_threshold
	- dot_f1
	- dot_f1_threshold
	- dot_precision
	- dot_recall
	- dot_ap
	- manhattan_accuracy
	- manhattan_accuracy_threshold
	- manhattan_f1
	- manhattan_f1_threshold
	- manhattan_precision
	- manhattan_recall
	- manhattan_ap
	- euclidean_accuracy
	- euclidean_accuracy_threshold
	- euclidean_f1
	- euclidean_f1_threshold
	- euclidean_precision
	- euclidean_recall
	- euclidean_ap
	- max_accuracy
	- max_accuracy_threshold
	- max_f1
	- max_f1_threshold
	- max_precision
	- max_recall
	- max_ap
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:680
	- loss:ContrastiveLoss
	widget:
	- source_sentence: 両方はだめ？
	sentences:
	- 両方欲しい
	- あほ
	- キッチンを調べよう
	- source_sentence: どっちも欲しくない
	sentences:
	- 誰かが魔法の呪文で花をぬいぐるみに変えた
	- 呪文を試すため
	- 家の中を調べよう
	- source_sentence: この本は？
	sentences:
	- お鍋から匂いがしたから
	- なんでここに本が？
	- 両方行きたい
	- source_sentence: 他のは選べる？
	sentences:
	- 昨日夕飯にチキンヌードル食べた？
	- 別のは選べる？
	- チキンヌードル作った？
	- source_sentence: 猫のぬいぐるみ
	sentences:
	- 両方はだめ？
	- ぬいぐるみ
	- 夜ご飯を食べる前
	model-index:
	- name: SentenceTransformer based on colorfulscoop/sbert-base-ja
	results:
	- task:
	type: binary-classification
	name: Binary Classification
	dataset:
	name: custom arc semantics data jp
	type: custom-arc-semantics-data-jp
	metrics:
	- type: cosine_accuracy
	value: 0.8897058823529411
	name: Cosine Accuracy
	- type: cosine_accuracy_threshold
	value: 0.6581918001174927
	name: Cosine Accuracy Threshold
	- type: cosine_f1
	value: 0.9044585987261147
	name: Cosine F1
	- type: cosine_f1_threshold
	value: 0.6180122494697571
	name: Cosine F1 Threshold
	- type: cosine_precision
	value: 0.9466666666666667
	name: Cosine Precision
	- type: cosine_recall
	value: 0.8658536585365854
	name: Cosine Recall
	- type: cosine_ap
	value: 0.9692848872766847
	name: Cosine Ap
	- type: dot_accuracy
	value: 0.8897058823529411
	name: Dot Accuracy
	- type: dot_accuracy_threshold
	value: 374.541748046875
	name: Dot Accuracy Threshold
	- type: dot_f1
	value: 0.9019607843137255
	name: Dot F1
	- type: dot_f1_threshold
	value: 374.541748046875
	name: Dot F1 Threshold
	- type: dot_precision
	value: 0.971830985915493
	name: Dot Precision
	- type: dot_recall
	value: 0.8414634146341463
	name: Dot Recall
	- type: dot_ap
	value: 0.9691104975300342
	name: Dot Ap
	- type: manhattan_accuracy
	value: 0.8970588235294118
	name: Manhattan Accuracy
	- type: manhattan_accuracy_threshold
	value: 453.2839660644531
	name: Manhattan Accuracy Threshold
	- type: manhattan_f1
	value: 0.9102564102564101
	name: Manhattan F1
	- type: manhattan_f1_threshold
	value: 453.2839660644531
	name: Manhattan F1 Threshold
	- type: manhattan_precision
	value: 0.9594594594594594
	name: Manhattan Precision
	- type: manhattan_recall
	value: 0.8658536585365854
	name: Manhattan Recall
	- type: manhattan_ap
	value: 0.9687920395428105
	name: Manhattan Ap
	- type: euclidean_accuracy
	value: 0.8897058823529411
	name: Euclidean Accuracy
	- type: euclidean_accuracy_threshold
	value: 19.75204086303711
	name: Euclidean Accuracy Threshold
	- type: euclidean_f1
	value: 0.9047619047619047
	name: Euclidean F1
	- type: euclidean_f1_threshold
	value: 23.66771125793457
	name: Euclidean F1 Threshold
	- type: euclidean_precision
	value: 0.8837209302325582
	name: Euclidean Precision
	- type: euclidean_recall
	value: 0.926829268292683
	name: Euclidean Recall
	- type: euclidean_ap
	value: 0.9690811253492324
	name: Euclidean Ap
	- type: max_accuracy
	value: 0.8970588235294118
	name: Max Accuracy
	- type: max_accuracy_threshold
	value: 453.2839660644531
	name: Max Accuracy Threshold
	- type: max_f1
	value: 0.9102564102564101
	name: Max F1
	- type: max_f1_threshold
	value: 453.2839660644531
	name: Max F1 Threshold
	- type: max_precision
	value: 0.971830985915493
	name: Max Precision
	- type: max_recall
	value: 0.926829268292683
	name: Max Recall
	- type: max_ap
	value: 0.9692848872766847
	name: Max Ap
	---

	# SentenceTransformer based on colorfulscoop/sbert-base-ja

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [colorfulscoop/sbert-base-ja](https://huggingface.co/colorfulscoop/sbert-base-ja) on the csv 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: [colorfulscoop/sbert-base-ja](https://huggingface.co/colorfulscoop/sbert-base-ja) <!-- at revision ecb8a98cd5176719ff7ab0d770a27420118732cf -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 tokens
	- 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': 512, 'do_lower_case': False}) with Transformer model: BertModel
	(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("sentence_transformers_model_id")
	# Run inference
	sentences = [
	'猫のぬいぐるみ',
	'ぬいぐるみ',
	'両方はだめ？',
	]
	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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	### 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

	#### Binary Classification
	* Dataset: `custom-arc-semantics-data-jp`
	* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

	\| Metric \| Value \|
	\|:-----------------------------\|:-----------\|
	\| cosine_accuracy \| 0.8897 \|
	\| cosine_accuracy_threshold \| 0.6582 \|
	\| cosine_f1 \| 0.9045 \|
	\| cosine_f1_threshold \| 0.618 \|
	\| cosine_precision \| 0.9467 \|
	\| cosine_recall \| 0.8659 \|
	\| cosine_ap \| 0.9693 \|
	\| dot_accuracy \| 0.8897 \|
	\| dot_accuracy_threshold \| 374.5417 \|
	\| dot_f1 \| 0.902 \|
	\| dot_f1_threshold \| 374.5417 \|
	\| dot_precision \| 0.9718 \|
	\| dot_recall \| 0.8415 \|
	\| dot_ap \| 0.9691 \|
	\| manhattan_accuracy \| 0.8971 \|
	\| manhattan_accuracy_threshold \| 453.284 \|
	\| manhattan_f1 \| 0.9103 \|
	\| manhattan_f1_threshold \| 453.284 \|
	\| manhattan_precision \| 0.9595 \|
	\| manhattan_recall \| 0.8659 \|
	\| manhattan_ap \| 0.9688 \|
	\| euclidean_accuracy \| 0.8897 \|
	\| euclidean_accuracy_threshold \| 19.752 \|
	\| euclidean_f1 \| 0.9048 \|
	\| euclidean_f1_threshold \| 23.6677 \|
	\| euclidean_precision \| 0.8837 \|
	\| euclidean_recall \| 0.9268 \|
	\| euclidean_ap \| 0.9691 \|
	\| max_accuracy \| 0.8971 \|
	\| max_accuracy_threshold \| 453.284 \|
	\| max_f1 \| 0.9103 \|
	\| max_f1_threshold \| 453.284 \|
	\| max_precision \| 0.9718 \|
	\| max_recall \| 0.9268 \|
	\| max_ap \| 0.9693 \|

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	## Bias, Risks and Limitations

	What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.
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	## Training Details

	### Training Dataset

	#### csv

	* Dataset: csv
	* Size: 680 training samples
	* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
	* Approximate statistics based on the first 680 samples:
	\| \| text1 \| text2 \| label \|
	\|:--------\|:---------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------\|:------------------------------------------------\|
	\| type \| string \| string \| int \|
	\| details \| <ul><li>min: 4 tokens</li><li>mean: 8.32 tokens</li><li>max: 15 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 8.0 tokens</li><li>max: 14 tokens</li></ul> \| <ul><li>0: ~41.73%</li><li>1: ~58.27%</li></ul> \|
	* Samples:
	\| text1 \| text2 \| label \|
	\|:----------------------\|:---------------------------\|:---------------\|
	\| <code>試すため</code> \| <code>ためすため</code> \| <code>1</code> \|
	\| <code>お鍋からの香り</code> \| <code>お鍋から辛い匂いがしたから</code> \| <code>1</code> \|
	\| <code>なんで話せるの？</code> \| <code>なんでしゃべれるの？</code> \| <code>1</code> \|
	* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
	```json
	{
	"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
	"margin": 0.8,
	"size_average": true
	}
	```

	### Evaluation Dataset

	#### csv

	* Dataset: csv
	* Size: 680 evaluation samples
	* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
	* Approximate statistics based on the first 680 samples:
	\| \| text1 \| text2 \| label \|
	\|:--------\|:---------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:------------------------------------------------\|
	\| type \| string \| string \| int \|
	\| details \| <ul><li>min: 4 tokens</li><li>mean: 8.21 tokens</li><li>max: 13 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 8.04 tokens</li><li>max: 14 tokens</li></ul> \| <ul><li>0: ~39.71%</li><li>1: ~60.29%</li></ul> \|
	* Samples:
	\| text1 \| text2 \| label \|
	\|:-----------------------\|:-----------------------\|:---------------\|
	\| <code>村人について教えて</code> \| <code>猫のぬいぐるみ</code> \| <code>0</code> \|
	\| <code>ハロー</code> \| <code>やあ</code> \| <code>1</code> \|
	\| <code>窓から出て行った</code> \| <code>オブリビオンの魔法</code> \| <code>0</code> \|
	* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
	```json
	{
	"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
	"margin": 0.8,
	"size_average": true
	}
	```

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

	- `eval_strategy`: epoch
	- `learning_rate`: 2e-05
	- `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`: epoch
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 8
	- `per_device_eval_batch_size`: 8
	- `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`: 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`: 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
	- `eval_use_gather_object`: False
	- `batch_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| loss \| custom-arc-semantics-data-jp_max_ap \|
	\|:-----:\|:----:\|:-------------:\|:------:\|:-----------------------------------:\|
	\| None \| 0 \| - \| - \| 0.9118 \|
	\| 1.0 \| 68 \| 0.0481 \| 0.0342 \| 0.9611 \|
	\| 2.0 \| 136 \| 0.0307 \| 0.0318 \| 0.9656 \|
	\| 3.0 \| 204 \| 0.0218 \| 0.0282 \| 0.9728 \|
	\| 4.0 \| 272 \| 0.0169 \| 0.0285 \| 0.9706 \|
	\| 5.0 \| 340 \| 0.0144 \| 0.0289 \| 0.9693 \|


	### Framework Versions
	- Python: 3.10.14
	- Sentence Transformers: 3.1.0
	- Transformers: 4.44.2
	- PyTorch: 2.4.1+cu121
	- Accelerate: 0.34.2
	- Datasets: 2.20.0
	- 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",
	}
	```

	#### ContrastiveLoss
	```bibtex
	@inproceedings{hadsell2006dimensionality,
	author={Hadsell, R. and Chopra, S. and LeCun, Y.},
	booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
	title={Dimensionality Reduction by Learning an Invariant Mapping},
	year={2006},
	volume={2},
	number={},
	pages={1735-1742},
	doi={10.1109/CVPR.2006.100}
	}
	```

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