Add new SentenceTransformer model

7b6614e verified 10 months ago

15.3 kB

	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:9712
	- loss:TripletLoss
	base_model: Syldehayem/all-MiniLM-L12-v2_embedder_train
	widget:
	- source_sentence: CGI 3D Animated Short "The Scarf" - by Team The Scarf
	sentences:
	- 'CGI 3D Short: "Lenovo Legion: Turning Point" - by Audis Huang & Moonshine Animation
	\| TheCGBros'
	- 'CGI Animated Trailers : "Dropzone" - by RealtimeUK'
	- 'CGI 3D Animated Short: "SOLVIVAL" - by Pixelhunters \| TheCGBros'
	- source_sentence: CGI Animated Short Film HD "Terazia's Zoo " by Alison Dulou & Estelle
	Lefebvre \| CGMeetup
	sentences:
	- A comedian puppet decides to branch out on his own / You're The Puppet
	- Horror Short Film Series “The Outer Darkness” Part 1 \| ALTER
	- ERNIE \| Omeleto
	- source_sentence: Kenneth Branagh in the thriller "Schneider's 2nd Stage" - Short
	film by Phil Stoole
	sentences:
	- 'CGI 3D Animated Short Film: "Fish in LOVE" by ISArt Digital \| @CGMeetup'
	- Cookies By The Fire Short Horror Film \| Screamfest \| Merry Christmas
	- 'CGI 3D Animated Spot: "Mantse Palm Wine" - by Arnold Bannerman \| TheCGBros'
	- source_sentence: The Portrait
	sentences:
	- A teenage girl must quickly adapt to a radically different urban environment \|
	Barrio Frontera
	- Queen of Meatloaf \| Short film tease
	- 'CGI 3D Tutorial : "Using Zapplink in Zbrush" - by 3dmotive'
	- source_sentence: Horror Short Film "Nice to Finally Meet You" \| ALTER \| Online Premiere
	sentences:
	- 'Mondays: The Spielberg Challenge Winner!'
	- 'The Curse of Pandora''s Box Returns to #UniversalHHN 2021'
	- SONS OF APRIL \| Omeleto
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	---

	# SentenceTransformer based on Syldehayem/all-MiniLM-L12-v2_embedder_train

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Syldehayem/all-MiniLM-L12-v2_embedder_train](https://huggingface.co/Syldehayem/all-MiniLM-L12-v2_embedder_train). 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: [Syldehayem/all-MiniLM-L12-v2_embedder_train](https://huggingface.co/Syldehayem/all-MiniLM-L12-v2_embedder_train) <!-- at revision 58956428f2d485efdf2697a1a2cc793795e25057 -->
	- Maximum Sequence Length: 128 tokens
	- Output Dimensionality: 384 dimensions
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	<!-- - 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})
	(2): Normalize()
	)
	```

	## 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("Syldehayem/all-MiniLM-L12-v2_embedder_train")
	# Run inference
	sentences = [
	'Horror Short Film "Nice to Finally Meet You" \| ALTER \| Online Premiere',
	"The Curse of Pandora's Box Returns to #UniversalHHN 2021",
	'Mondays: The Spielberg Challenge Winner!',
	]
	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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	## Training Details

	### Training Dataset

	#### Unnamed Dataset

	* Size: 9,712 training samples
	* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence_0 \| sentence_1 \| sentence_2 \|
	\|:--------\|:---------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 3 tokens</li><li>mean: 19.7 tokens</li><li>max: 49 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 19.91 tokens</li><li>max: 49 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 20.27 tokens</li><li>max: 50 tokens</li></ul> \|
	* Samples:
	\| sentence_0 \| sentence_1 \| sentence_2 \|
	\|:---------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------\|:---------------------------------------------------------------------\|
	\| <code>মেয়ে যখন মায়ের মতন \| Bidhilipi \| #Shorts \| Bengali Family Drama</code> \| <code>CGI 3D Animated Shorts: "Rust" - by Matthieu Druaud</code> \| <code>Mukhyamantri \| মুখ্যমন্ত্রী \| Bengali Movie Part – 3/12</code> \|
	\| <code>A Sci-Fi Short Film: "Voltok" - by Jonathan Vleeschower \| TheCGBros</code> \| <code>CGI MoCap Demo : "Finger Mocap Without Any Post Animation" by the MocapLab</code> \| <code>A MAN DEPARTED \| Omeleto Drama</code> \|
	\| <code>LEAKY PIPES</code> \| <code>Taking care of a baby at 15 \| "Fifteen" - Short film by Sameh Alaa</code> \| <code>CGI VFX Spot : "Black Beetle" by - The MILL</code> \|
	* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
	```json
	{
	"distance_metric": "TripletDistanceMetric.EUCLIDEAN",
	"triplet_margin": 5
	}
	```

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

	- `per_device_train_batch_size`: 16
	- `per_device_eval_batch_size`: 16
	- `num_train_epochs`: 50
	- `multi_dataset_batch_sampler`: round_robin

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

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: no
	- `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
	- `num_train_epochs`: 50
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.0
	- `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`: False
	- `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}
	- `tp_size`: 0
	- `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
	- `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`: batch_sampler
	- `multi_dataset_batch_sampler`: round_robin

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \|
	\|:-------:\|:-----:\|:-------------:\|
	\| 0.8237 \| 500 \| 5.0075 \|
	\| 1.6474 \| 1000 \| 4.9816 \|
	\| 2.4712 \| 1500 \| 5.013 \|
	\| 3.2949 \| 2000 \| 4.981 \|
	\| 4.1186 \| 2500 \| 4.9981 \|
	\| 4.9423 \| 3000 \| 4.9727 \|
	\| 5.7661 \| 3500 \| 4.9698 \|
	\| 6.5898 \| 4000 \| 4.9839 \|
	\| 7.4135 \| 4500 \| 5.0001 \|
	\| 8.2372 \| 5000 \| 4.9996 \|
	\| 9.0610 \| 5500 \| 4.9993 \|
	\| 9.8847 \| 6000 \| 4.9999 \|
	\| 10.7084 \| 6500 \| 5.0015 \|
	\| 11.5321 \| 7000 \| 4.9934 \|
	\| 12.3558 \| 7500 \| 4.9903 \|
	\| 13.1796 \| 8000 \| 4.9875 \|
	\| 14.0033 \| 8500 \| 5.0018 \|
	\| 14.8270 \| 9000 \| 5.0088 \|
	\| 15.6507 \| 9500 \| 4.9643 \|
	\| 16.4745 \| 10000 \| 4.9447 \|
	\| 17.2982 \| 10500 \| 4.8911 \|
	\| 18.1219 \| 11000 \| 4.8719 \|
	\| 18.9456 \| 11500 \| 4.8671 \|
	\| 19.7694 \| 12000 \| 4.8268 \|
	\| 20.5931 \| 12500 \| 4.8195 \|
	\| 21.4168 \| 13000 \| 4.7726 \|
	\| 22.2405 \| 13500 \| 4.7479 \|
	\| 23.0643 \| 14000 \| 4.7465 \|
	\| 23.8880 \| 14500 \| 4.7776 \|
	\| 24.7117 \| 15000 \| 4.7366 \|
	\| 25.5354 \| 15500 \| 4.7076 \|
	\| 26.3591 \| 16000 \| 4.74 \|
	\| 27.1829 \| 16500 \| 4.7118 \|
	\| 28.0066 \| 17000 \| 4.6797 \|
	\| 28.8303 \| 17500 \| 4.7144 \|
	\| 29.6540 \| 18000 \| 4.662 \|
	\| 30.4778 \| 18500 \| 4.6849 \|
	\| 31.3015 \| 19000 \| 4.6608 \|
	\| 32.1252 \| 19500 \| 4.6844 \|
	\| 32.9489 \| 20000 \| 4.6561 \|
	\| 33.7727 \| 20500 \| 4.6513 \|
	\| 34.5964 \| 21000 \| 4.6418 \|
	\| 35.4201 \| 21500 \| 4.635 \|
	\| 36.2438 \| 22000 \| 4.6418 \|
	\| 37.0675 \| 22500 \| 4.62 \|
	\| 37.8913 \| 23000 \| 4.615 \|
	\| 38.7150 \| 23500 \| 4.6189 \|
	\| 39.5387 \| 24000 \| 4.6113 \|
	\| 40.3624 \| 24500 \| 4.6054 \|
	\| 41.1862 \| 25000 \| 4.5824 \|
	\| 42.0099 \| 25500 \| 4.5907 \|
	\| 42.8336 \| 26000 \| 4.5949 \|
	\| 43.6573 \| 26500 \| 4.5769 \|
	\| 44.4811 \| 27000 \| 4.5758 \|
	\| 45.3048 \| 27500 \| 4.5613 \|
	\| 46.1285 \| 28000 \| 4.5816 \|
	\| 46.9522 \| 28500 \| 4.5538 \|
	\| 47.7759 \| 29000 \| 4.5645 \|
	\| 48.5997 \| 29500 \| 4.5653 \|
	\| 49.4234 \| 30000 \| 4.5494 \|


	### Framework Versions
	- Python: 3.12.9
	- Sentence Transformers: 4.1.0
	- Transformers: 4.51.3
	- PyTorch: 2.7.0+cu126
	- Accelerate: 1.6.0
	- Datasets: 3.5.1
	- Tokenizers: 0.21.1

	## 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",
	}
	```

	#### TripletLoss
	```bibtex
	@misc{hermans2017defense,
	title={In Defense of the Triplet Loss for Person Re-Identification},
	author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
	year={2017},
	eprint={1703.07737},
	archivePrefix={arXiv},
	primaryClass={cs.CV}
	}
	```

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