| ---
|
| tags:
|
| - sentence-transformers
|
| - sentence-similarity
|
| - feature-extraction
|
| - generated_from_trainer
|
| - dataset_size:45
|
| - loss:MultipleNegativesRankingLoss
|
| base_model: embedingHF/bilingual-roman-urdu-embedder
|
| widget:
|
| - source_sentence: map location
|
| sentences:
|
| - is garage available
|
| - how much is it
|
| - bedrooms kitnay hain
|
| - source_sentence: car stand hai
|
| sentences:
|
| - parking space
|
| - parking facility
|
| - precise location
|
| - source_sentence: rooms kitnay hain
|
| sentences:
|
| - kitna bara
|
| - number of rooms
|
| - property rate
|
| - source_sentence: yeh jaga kahan hai
|
| sentences:
|
| - where is this place
|
| - bedrooms
|
| - property size
|
| - source_sentence: total area batao
|
| sentences:
|
| - location on map
|
| - rate batao
|
| - total area
|
| pipeline_tag: sentence-similarity
|
| library_name: sentence-transformers
|
| ---
|
|
|
| # SentenceTransformer based on embedingHF/bilingual-roman-urdu-embedder
|
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [embedingHF/bilingual-roman-urdu-embedder](https://huggingface.co/embedingHF/bilingual-roman-urdu-embedder). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for retrieval.
|
|
|
| ## Model Details
|
|
|
| ### Model Description
|
| - **Model Type:** Sentence Transformer
|
| - **Base model:** [embedingHF/bilingual-roman-urdu-embedder](https://huggingface.co/embedingHF/bilingual-roman-urdu-embedder) <!-- at revision 733331b846073b4605769ad33ecf0931a3c35489 -->
|
| - **Maximum Sequence Length:** 256 tokens
|
| - **Output Dimensionality:** 384 dimensions
|
| - **Similarity Function:** Cosine Similarity
|
| - **Supported Modality:** Text
|
| <!-- - **Training Dataset:** Unknown -->
|
| <!-- - **Language:** Unknown -->
|
| <!-- - **License:** Unknown -->
|
|
|
| ### Model Sources
|
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
|
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
|
| - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
|
|
|
| ### Full Model Architecture
|
|
|
| ```
|
| SentenceTransformer(
|
| (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'})
|
| (1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'mean', '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("sentence_transformers_model_id")
|
| # Run inference
|
| sentences = [
|
| 'total area batao',
|
| 'total area',
|
| 'location on map',
|
| ]
|
| embeddings = model.encode(sentences)
|
| print(embeddings.shape)
|
| # [3, 384]
|
|
|
| # Get the similarity scores for the embeddings
|
| similarities = model.similarity(embeddings, embeddings)
|
| print(similarities)
|
| # tensor([[1.0000, 0.9458, 0.3963],
|
| # [0.9458, 1.0000, 0.3714],
|
| # [0.3963, 0.3714, 1.0000]])
|
| ```
|
| <!--
|
| ### Direct Usage (Transformers)
|
|
|
| <details><summary>Click to see the direct usage in Transformers</summary>
|
|
|
| </details>
|
| -->
|
|
|
| <!--
|
| ### Downstream Usage (Sentence Transformers)
|
|
|
| You can finetune this model on your own dataset.
|
|
|
| <details><summary>Click to expand</summary>
|
|
|
| </details>
|
| -->
|
|
|
| <!--
|
| ### Out-of-Scope Use
|
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
| -->
|
|
|
| <!--
|
| ## 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.*
|
| -->
|
|
|
| <!--
|
| ### Recommendations
|
|
|
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
| -->
|
|
|
| ## Training Details
|
|
|
| ### Training Dataset
|
|
|
| #### Unnamed Dataset
|
|
|
| * Size: 45 training samples
|
| * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
|
| * Approximate statistics based on the first 45 samples:
|
| | | sentence_0 | sentence_1 | label |
|
| |:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|:---------------------------------------------------------------|
|
| | type | string | string | float |
|
| | details | <ul><li>min: 3 tokens</li><li>mean: 5.69 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.4 tokens</li><li>max: 8 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.67</li><li>max: 1.0</li></ul> |
|
| * Samples:
|
| | sentence_0 | sentence_1 | label |
|
| |:------------------------------|:---------------------------------|:------------------|
|
| | <code>near kahan hai</code> | <code>where is it located</code> | <code>1.0</code> |
|
| | <code>total area batao</code> | <code>total area</code> | <code>1.0</code> |
|
| | <code>size</code> | <code>area</code> | <code>0.84</code> |
|
| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
|
| ```json
|
| {
|
| "scale": 20.0,
|
| "similarity_fct": "cos_sim",
|
| "gather_across_devices": false,
|
| "directions": [
|
| "query_to_doc"
|
| ],
|
| "partition_mode": "joint",
|
| "hardness_mode": null,
|
| "hardness_strength": 0.0
|
| }
|
| ```
|
|
|
| ### Training Hyperparameters
|
| #### Non-Default Hyperparameters
|
|
|
| - `num_train_epochs`: 20
|
| - `multi_dataset_batch_sampler`: round_robin
|
|
|
| #### All Hyperparameters
|
| <details><summary>Click to expand</summary>
|
|
|
| - `per_device_train_batch_size`: 8
|
| - `num_train_epochs`: 20
|
| - `max_steps`: -1
|
| - `learning_rate`: 5e-05
|
| - `lr_scheduler_type`: linear
|
| - `lr_scheduler_kwargs`: None
|
| - `warmup_steps`: 0
|
| - `optim`: adamw_torch_fused
|
| - `optim_args`: None
|
| - `weight_decay`: 0.0
|
| - `adam_beta1`: 0.9
|
| - `adam_beta2`: 0.999
|
| - `adam_epsilon`: 1e-08
|
| - `optim_target_modules`: None
|
| - `gradient_accumulation_steps`: 1
|
| - `average_tokens_across_devices`: True
|
| - `max_grad_norm`: 1
|
| - `label_smoothing_factor`: 0.0
|
| - `bf16`: False
|
| - `fp16`: False
|
| - `bf16_full_eval`: False
|
| - `fp16_full_eval`: False
|
| - `tf32`: None
|
| - `gradient_checkpointing`: False
|
| - `gradient_checkpointing_kwargs`: None
|
| - `torch_compile`: False
|
| - `torch_compile_backend`: None
|
| - `torch_compile_mode`: None
|
| - `use_liger_kernel`: False
|
| - `liger_kernel_config`: None
|
| - `use_cache`: False
|
| - `neftune_noise_alpha`: None
|
| - `torch_empty_cache_steps`: None
|
| - `auto_find_batch_size`: False
|
| - `log_on_each_node`: True
|
| - `logging_nan_inf_filter`: True
|
| - `include_num_input_tokens_seen`: no
|
| - `log_level`: passive
|
| - `log_level_replica`: warning
|
| - `disable_tqdm`: False
|
| - `project`: huggingface
|
| - `trackio_space_id`: trackio
|
| - `per_device_eval_batch_size`: 8
|
| - `prediction_loss_only`: True
|
| - `eval_on_start`: False
|
| - `eval_do_concat_batches`: True
|
| - `eval_use_gather_object`: False
|
| - `eval_accumulation_steps`: None
|
| - `include_for_metrics`: []
|
| - `batch_eval_metrics`: False
|
| - `save_only_model`: False
|
| - `save_on_each_node`: False
|
| - `enable_jit_checkpoint`: False
|
| - `push_to_hub`: False
|
| - `hub_private_repo`: None
|
| - `hub_model_id`: None
|
| - `hub_strategy`: every_save
|
| - `hub_always_push`: False
|
| - `hub_revision`: None
|
| - `load_best_model_at_end`: False
|
| - `ignore_data_skip`: False
|
| - `restore_callback_states_from_checkpoint`: False
|
| - `full_determinism`: False
|
| - `seed`: 42
|
| - `data_seed`: None
|
| - `use_cpu`: False
|
| - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
|
| - `parallelism_config`: None
|
| - `dataloader_drop_last`: False
|
| - `dataloader_num_workers`: 0
|
| - `dataloader_pin_memory`: True
|
| - `dataloader_persistent_workers`: False
|
| - `dataloader_prefetch_factor`: None
|
| - `remove_unused_columns`: True
|
| - `label_names`: None
|
| - `train_sampling_strategy`: random
|
| - `length_column_name`: length
|
| - `ddp_find_unused_parameters`: None
|
| - `ddp_bucket_cap_mb`: None
|
| - `ddp_broadcast_buffers`: False
|
| - `ddp_backend`: None
|
| - `ddp_timeout`: 1800
|
| - `fsdp`: []
|
| - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
|
| - `deepspeed`: None
|
| - `debug`: []
|
| - `skip_memory_metrics`: True
|
| - `do_predict`: False
|
| - `resume_from_checkpoint`: None
|
| - `warmup_ratio`: None
|
| - `local_rank`: -1
|
| - `prompts`: None
|
| - `batch_sampler`: batch_sampler
|
| - `multi_dataset_batch_sampler`: round_robin
|
| - `router_mapping`: {}
|
| - `learning_rate_mapping`: {}
|
|
|
| </details>
|
|
|
| ### Training Time
|
| - **Training**: 39.7 seconds
|
|
|
| ### Framework Versions
|
| - Python: 3.12.3
|
| - Sentence Transformers: 5.4.1
|
| - Transformers: 5.5.4
|
| - PyTorch: 2.11.0+cpu
|
| - Accelerate: 1.13.0
|
| - Datasets: 4.8.4
|
| - Tokenizers: 0.22.2
|
|
|
| ## 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",
|
| }
|
| ```
|
|
|
| #### MultipleNegativesRankingLoss
|
| ```bibtex
|
| @misc{oord2019representationlearningcontrastivepredictive,
|
| title={Representation Learning with Contrastive Predictive Coding},
|
| author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
|
| year={2019},
|
| eprint={1807.03748},
|
| archivePrefix={arXiv},
|
| primaryClass={cs.LG},
|
| url={https://arxiv.org/abs/1807.03748},
|
| }
|
| ```
|
|
|
| <!--
|
| ## Glossary
|
|
|
| *Clearly define terms in order to be accessible across audiences.*
|
| -->
|
|
|
| <!--
|
| ## Model Card Authors
|
|
|
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
| -->
|
|
|
| <!--
|
| ## Model Card Contact
|
|
|
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
| --> |
