--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:9712 - loss:TripletLoss base_model: sentence-transformers/all-MiniLM-L6-v2 widget: - source_sentence: CGI VFX Breakdowns "El Principe Season 1" - by Stargate Studios Malta sentences: - Best of 2013! - 'কাজকর্ম ফেলে ছেলে নিয়ে পড়ে থাকলে হবে | Baro Bou | #shorts | #banglacinema' - CG animation on social anxiety | "Subconcious Password" - by Chris Landreth (Oscar-winner) - source_sentence: Award-Winning Stop-Motion Animation Short Film | HEATWAVE sentences: - Natun Diner Alo - Bengali Full Movie | Soumitra Chatterjee | Sabitri Chatterjee - Funny CG short film on Martin Luther and the Reformation | "Luther" - by Tumblehead - 'Serbian Dancing Lady made into a film #horrorstory #shorts #horrorstories' - source_sentence: 'MotionBuilder Speed Tutorial: How to add Alpha Maps to objects and see it your viewport.(Basic)' sentences: - Animated short film about anonymity and small encounters | "Through You" - by Lucette Braune - Animated short film on parental pressure | "Matilda and the Spare Head" - by Ignas Meilūnas - '📽️ Vertical Short: "Course of Nature" - by Lucy Xue and Paisley Manga | #TheCGBros' - source_sentence: Mriter Marte Agaman - Bengali Full Movie | Bhanu Bandopadhyay | Jahor Roy sentences: - CGI VFX Breakdowns HD "Labanita 3D Breakdown" by Monkeys | CGMeetup - 'CGI VFX Spot : "Network of the Future" by - MPC' - Writing a Story Around a Shot Idea & The Best Part About Filmmaking - source_sentence: '**Award Winning** CGI 3D Animated Short: "Monsters In The Dark" - by Apollonia Thomaier | TheCGBros' sentences: - Nayantara | নয়নতারা | Family Movie | Full HD | Saswata Chatterjee, Soumitra, Mamata Shankar - Gajamukta - Bengali Full Movie | Moon Moon Sen | Abhishek Chatterjee | Soumitra Chatterjee - Sci-Fi Short Film "In Sight Sci-Fi Short Film" by ArtFx | CGMeetup pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). 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/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 384 dimensions - **Similarity Function:** Cosine Similarity ### 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': 256, '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-L6-v2_embedder") # Run inference sentences = [ '**Award Winning** CGI 3D Animated Short: "Monsters In The Dark" - by Apollonia Thomaier | TheCGBros', 'Gajamukta - Bengali Full Movie | Moon Moon Sen | Abhishek Chatterjee | Soumitra Chatterjee', 'Nayantara | নয়নতারা | Family Movie | Full HD | Saswata Chatterjee, Soumitra, Mamata Shankar', ] 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] ``` ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 9,712 training samples * Columns: sentence_0, sentence_1, and sentence_2 * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | sentence_2 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | sentence_0 | sentence_1 | sentence_2 | |:-------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------| | D.A.D. (Sci-Fi Short Film) | Dad just got an upgrade | Preservation Clip | A man's life is ruined by his sexist auto-correct text messages. | Short Film "Auto-Cowrecked" | | WATCH Unknown Caller Short Film | LINK BELOW #shorts | CGI VFX Short Spot : "Chalet" by - Counterfeit FX | CGI 3D VFX Short : "Zumtobel" by - Trizz | | Pratibha | প্রতিভা | Bengali Romantic Movie | Full HD | Ranjit Mallick, Satabdi Roy | Sci-Fi Series "ATROPA" Episode 5 | DUST | CGI 3D Animated Short: "Glitch" - by ESMA | TheCGBros | * Loss: [TripletLoss](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`: 100 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters
Click to expand - `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`: 100 - `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
### Training Logs
Click to expand | Epoch | Step | Training Loss | |:-------:|:-----:|:-------------:| | 0.8237 | 500 | 5.0006 | | 1.6474 | 1000 | 4.9915 | | 2.4712 | 1500 | 4.96 | | 3.2949 | 2000 | 4.9266 | | 4.1186 | 2500 | 4.8689 | | 4.9423 | 3000 | 4.8158 | | 5.7661 | 3500 | 4.7408 | | 6.5898 | 4000 | 4.702 | | 7.4135 | 4500 | 4.6564 | | 8.2372 | 5000 | 4.63 | | 9.0610 | 5500 | 4.6119 | | 9.8847 | 6000 | 4.5983 | | 0.8237 | 500 | 4.6071 | | 1.6474 | 1000 | 4.6401 | | 2.4712 | 1500 | 4.6525 | | 3.2949 | 2000 | 4.6101 | | 4.1186 | 2500 | 4.5926 | | 4.9423 | 3000 | 4.5827 | | 5.7661 | 3500 | 4.5096 | | 6.5898 | 4000 | 4.5171 | | 7.4135 | 4500 | 4.507 | | 8.2372 | 5000 | 4.4738 | | 9.0610 | 5500 | 4.4973 | | 9.8847 | 6000 | 4.4485 | | 0.8237 | 500 | 4.4222 | | 1.6474 | 1000 | 4.3984 | | 2.4712 | 1500 | 4.4144 | | 3.2949 | 2000 | 4.4117 | | 4.1186 | 2500 | 4.4042 | | 4.9423 | 3000 | 4.4136 | | 5.7661 | 3500 | 4.4055 | | 6.5898 | 4000 | 4.4267 | | 7.4135 | 4500 | 4.4548 | | 8.2372 | 5000 | 4.4443 | | 9.0610 | 5500 | 4.4649 | | 9.8847 | 6000 | 4.4463 | | 10.7084 | 6500 | 4.4771 | | 11.5321 | 7000 | 4.4691 | | 12.3558 | 7500 | 4.4817 | | 13.1796 | 8000 | 4.4505 | | 14.0033 | 8500 | 4.4355 | | 14.8270 | 9000 | 4.4145 | | 15.6507 | 9500 | 4.4128 | | 16.4745 | 10000 | 4.3874 | | 17.2982 | 10500 | 4.4057 | | 18.1219 | 11000 | 4.3841 | | 18.9456 | 11500 | 4.3836 | | 19.7694 | 12000 | 4.3554 | | 20.5931 | 12500 | 4.3445 | | 21.4168 | 13000 | 4.3351 | | 22.2405 | 13500 | 4.3602 | | 23.0643 | 14000 | 4.3366 | | 23.8880 | 14500 | 4.3302 | | 24.7117 | 15000 | 4.3531 | | 25.5354 | 15500 | 4.3002 | | 26.3591 | 16000 | 4.3499 | | 27.1829 | 16500 | 4.3049 | | 28.0066 | 17000 | 4.3039 | | 28.8303 | 17500 | 4.3045 | | 29.6540 | 18000 | 4.2969 | | 30.4778 | 18500 | 4.2831 | | 31.3015 | 19000 | 4.2999 | | 32.1252 | 19500 | 4.3037 | | 32.9489 | 20000 | 4.2768 | | 33.7727 | 20500 | 4.2928 | | 34.5964 | 21000 | 4.2697 | | 35.4201 | 21500 | 4.2985 | | 36.2438 | 22000 | 4.2799 | | 37.0675 | 22500 | 4.286 | | 37.8913 | 23000 | 4.2671 | | 38.7150 | 23500 | 4.2775 | | 39.5387 | 24000 | 4.2872 | | 40.3624 | 24500 | 4.2687 | | 41.1862 | 25000 | 4.2555 | | 42.0099 | 25500 | 4.2661 | | 42.8336 | 26000 | 4.2737 | | 43.6573 | 26500 | 4.2476 | | 44.4811 | 27000 | 4.2347 | | 45.3048 | 27500 | 4.2381 | | 46.1285 | 28000 | 4.2533 | | 46.9522 | 28500 | 4.2295 | | 47.7759 | 29000 | 4.2346 | | 48.5997 | 29500 | 4.2411 | | 49.4234 | 30000 | 4.2347 | | 50.2471 | 30500 | 4.232 | | 51.0708 | 31000 | 4.2409 | | 51.8946 | 31500 | 4.2219 | | 52.7183 | 32000 | 4.2284 | | 53.5420 | 32500 | 4.2396 | | 54.3657 | 33000 | 4.2199 | | 55.1895 | 33500 | 4.2198 | | 56.0132 | 34000 | 4.1958 | | 56.8369 | 34500 | 4.2034 | | 57.6606 | 35000 | 4.1931 | | 58.4843 | 35500 | 4.2292 | | 59.3081 | 36000 | 4.197 | | 60.1318 | 36500 | 4.2365 | | 60.9555 | 37000 | 4.1939 | | 61.7792 | 37500 | 4.2045 | | 62.6030 | 38000 | 4.2037 | | 63.4267 | 38500 | 4.2007 | | 64.2504 | 39000 | 4.2025 | | 65.0741 | 39500 | 4.1846 | | 65.8979 | 40000 | 4.1812 | | 66.7216 | 40500 | 4.2022 | | 67.5453 | 41000 | 4.1955 | | 68.3690 | 41500 | 4.1834 | | 69.1928 | 42000 | 4.1838 | | 70.0165 | 42500 | 4.1908 | | 70.8402 | 43000 | 4.1821 | | 71.6639 | 43500 | 4.1636 | | 72.4876 | 44000 | 4.1868 | | 73.3114 | 44500 | 4.1737 | | 74.1351 | 45000 | 4.1802 | | 74.9588 | 45500 | 4.1744 | | 75.7825 | 46000 | 4.1688 | | 76.6063 | 46500 | 4.1664 | | 77.4300 | 47000 | 4.1627 | | 78.2537 | 47500 | 4.1561 | | 79.0774 | 48000 | 4.1699 | | 79.9012 | 48500 | 4.1679 | | 80.7249 | 49000 | 4.1579 | | 81.5486 | 49500 | 4.1502 | | 82.3723 | 50000 | 4.1613 | | 83.1960 | 50500 | 4.1342 | | 84.0198 | 51000 | 4.1659 | | 84.8435 | 51500 | 4.1484 | | 85.6672 | 52000 | 4.1563 | | 86.4909 | 52500 | 4.1551 | | 87.3147 | 53000 | 4.1519 | | 88.1384 | 53500 | 4.1486 | | 88.9621 | 54000 | 4.1532 | | 89.7858 | 54500 | 4.1506 | | 90.6096 | 55000 | 4.1397 | | 91.4333 | 55500 | 4.1589 | | 92.2570 | 56000 | 4.1213 | | 93.0807 | 56500 | 4.1466 | | 93.9044 | 57000 | 4.1496 | | 94.7282 | 57500 | 4.1416 | | 95.5519 | 58000 | 4.1427 | | 96.3756 | 58500 | 4.133 | | 97.1993 | 59000 | 4.1505 | | 98.0231 | 59500 | 4.1342 | | 98.8468 | 60000 | 4.133 | | 99.6705 | 60500 | 4.151 |
### 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} } ```