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all-MiniLM-L6-v2_embedder

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:9712
loss:TripletLoss
text-embeddings-inference
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 ---
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) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
- **Maximum Sequence Length:** 256 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': 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]
```
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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: 4 tokens</li><li>mean: 19.73 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 20.14 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 20.23 tokens</li><li>max: 66 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 | sentence_2 |
|:-------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------|
| <code>D.A.D. (Sci-Fi Short Film) | Dad just got an upgrade</code> | <code>Preservation Clip</code> | <code>A man's life is ruined by his sexist auto-correct text messages. | Short Film "Auto-Cowrecked"</code> |
| <code>WATCH Unknown Caller Short Film | LINK BELOW #shorts</code> | <code>CGI VFX Short Spot : "Chalet" by - Counterfeit FX</code> | <code>CGI 3D VFX Short : "Zumtobel" by - Trizz</code> |
| <code>Pratibha | প্রতিভা | Bengali Romantic Movie | Full HD | Ranjit Mallick, Satabdi Roy</code> | <code>Sci-Fi Series "ATROPA" Episode 5 | DUST</code> | <code>CGI 3D Animated Short: "Glitch" - by ESMA | TheCGBros</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`: 100
- `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`: 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
</details>
### Training Logs
<details><summary>Click to expand</summary>
| 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 |
</details>
### 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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