README.md

---
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: Live Action Animation Effect from Spider-Man Across The Spider-Verse
  sentences:
  - PANDEMONIUM - Animation Short Film 2023 - GOBELINS
  - Rakhal Raja | রাখাল রাজা | Bengali Movie – 6/14 | Chiranjeet
  - 'CGI Animated Short Film: "Song for a Wooden Heart" by The Inklings | CGMeetup'
- source_sentence: The Mannequin | Short Horror Film
  sentences:
  - Sci-Fi Digital Series "Nikola Tesla and the End of the World" Ep 1 | DUST
  - CGI Animated Short Film HD "Roommate Wanted - Dead or Alive " by Monkey Tennis
    Animation | CGMeetup
  - O Dharitri Maa | Lav Kush | Bengali Movie Devotional Song
- source_sentence: Short film on choosing between child and career | "Patision Avenue"
    - by Thanasis Neofotistos
  sentences:
  - Pratham Dekha | প্রথম দেখা | Bengali Movie – 1/15 | Prosenjit
  - 'CGI & VFX Breakdowns: "The Intruder" - by PenguineFx Academy | TheCGBros'
  - 'CGI 2D Photoshop Tutorial : "Creating Tileable Textures from Pictures" - by 3dmotive'
- source_sentence: The Meaning Behind Camera Movement!
  sentences:
  - PROSOPAGNOSIA | Omeleto
  - Horror Short Film "Fry Day" | ALTER
  - Rupban Kanya | রূপবান কন্যা | Bengali Movie – 2/13 | Biswajit
- source_sentence: 'CGI 3D Animated Trailers: "Killing Anabella" - by Aman Bhanot
    | TheCGBros'
  sentences:
  - 'CGI 3D Animated Trailers: "Play On" - by Sun Woo Kang | TheCGBros'
  - Kaise Katey Rajani | Khudito Pasan | Bengali Movie Video Song | Bengali Classic
    Song
  - Haenyo, the women of the sea (Trailer) - Animated short film by Eloïc Gimenez
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_train")
# Run inference
sentences = [
    'CGI 3D Animated Trailers: "Killing Anabella" - by Aman Bhanot | TheCGBros',
    'Kaise Katey Rajani | Khudito Pasan | Bengali Movie Video Song | Bengali Classic Song',
    'CGI 3D Animated Trailers: "Play On" - by Sun Woo Kang | TheCGBros',
]
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: 19.63 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 20.02 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 20.32 tokens</li><li>max: 62 tokens</li></ul> |
* Samples:
  | sentence_0                                                                         | sentence_1                                                                                        | sentence_2                                                                                   |
  |:-----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------|
  | <code>13 Films In 2 Years - A Filmmaker's Journey EPISODE 2 (Documentary)</code>   | <code>দেওয়া নেওয়া ইত্যাদি | Natok Korish Na Toh | Sketch Comedy Show | Episode 3 | Story 1</code> | <code>Poetic animation about polar myths | Inukshuk - Short Film by Camillelvis Théry</code> |
  | <code>CGI & VFX Showreels: "B-War" - by Jorge Baldeon | TheCGBros</code>           | <code>Hot Dog | Coworkers Try to Rescue Dog Locked in Car, Chaos Ensues, Comedy Short Film</code> | <code>CGI 3D Animated Short "Heart and Soul" - by Pierre Zah + Ringling | TheCGBros</code>   |
  | <code>Excuse Me - Comedy Scene | Mauchaak | Ranjit Mallick, Mithu Mukherjee</code> | <code>Cholo Jai Cholo Jai | Kony | Bengali Movie Rabindra Sangeet | Malabi Mukherjee</code>       | <code>AWAKEN THE INNER SELF | Horror Short Film</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   | 5.0003        |
| 1.6474  | 1000  | 4.9955        |
| 2.4712  | 1500  | 4.9898        |
| 3.2949  | 2000  | 4.9741        |
| 4.1186  | 2500  | 4.9602        |
| 4.9423  | 3000  | 4.9196        |
| 5.7661  | 3500  | 4.8714        |
| 6.5898  | 4000  | 4.8077        |
| 7.4135  | 4500  | 4.7834        |
| 8.2372  | 5000  | 4.7543        |
| 9.0610  | 5500  | 4.7321        |
| 9.8847  | 6000  | 4.7047        |
| 10.7084 | 6500  | 4.7031        |
| 11.5321 | 7000  | 4.6618        |
| 12.3558 | 7500  | 4.6335        |
| 13.1796 | 8000  | 4.6199        |
| 14.0033 | 8500  | 4.5678        |
| 14.8270 | 9000  | 4.585         |
| 15.6507 | 9500  | 4.5565        |
| 16.4745 | 10000 | 4.5897        |
| 17.2982 | 10500 | 4.532         |
| 18.1219 | 11000 | 4.5248        |
| 18.9456 | 11500 | 4.5226        |
| 19.7694 | 12000 | 4.4929        |
| 20.5931 | 12500 | 4.4835        |
| 21.4168 | 13000 | 4.468         |
| 22.2405 | 13500 | 4.4638        |
| 23.0643 | 14000 | 4.4377        |
| 23.8880 | 14500 | 4.4336        |
| 24.7117 | 15000 | 4.4322        |
| 25.5354 | 15500 | 4.4144        |
| 26.3591 | 16000 | 4.4041        |
| 27.1829 | 16500 | 4.4118        |
| 28.0066 | 17000 | 4.3932        |
| 28.8303 | 17500 | 4.3745        |
| 29.6540 | 18000 | 4.3673        |
| 30.4778 | 18500 | 4.3903        |
| 31.3015 | 19000 | 4.3573        |
| 32.1252 | 19500 | 4.3369        |
| 32.9489 | 20000 | 4.3424        |
| 33.7727 | 20500 | 4.3416        |
| 34.5964 | 21000 | 4.3402        |
| 35.4201 | 21500 | 4.3205        |
| 36.2438 | 22000 | 4.3288        |
| 37.0675 | 22500 | 4.3306        |
| 37.8913 | 23000 | 4.3067        |
| 38.7150 | 23500 | 4.3108        |
| 39.5387 | 24000 | 4.2793        |
| 40.3624 | 24500 | 4.3203        |
| 41.1862 | 25000 | 4.3012        |
| 42.0099 | 25500 | 4.288         |
| 42.8336 | 26000 | 4.2913        |
| 43.6573 | 26500 | 4.2956        |
| 44.4811 | 27000 | 4.2755        |
| 45.3048 | 27500 | 4.2914        |
| 46.1285 | 28000 | 4.2525        |
| 46.9522 | 28500 | 4.2877        |
| 47.7759 | 29000 | 4.2624        |
| 48.5997 | 29500 | 4.2649        |
| 49.4234 | 30000 | 4.2897        |


### 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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