---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:51368
- loss:MultipleNegativesRankingLoss
base_model: google/embeddinggemma-300m
widget:
- source_sentence: 'task: search result | query: Poetic rendition of a chariot being
made ready for a god.'
sentences:
- 'title: none | text: युञ्जन्ति हरी इषिरस्य गाथयोरौ रथ उरुयुगे
इन्द्रवाहा वचोयुजा'
- 'title: none | text: न यं रिपवो न रिषण्यवो गर्भे सन्तं रेषणा रेषयन्ति
अन्धा अपश्या न दभन्न् अभिख्या नित्यास ईम् प्रेतारो अरक्षन्'
- 'title: none | text: यथा पूर्वेभ्यो जरितृभ्य इन्द्र मय इवापो न तृष्यते बभूथ
ताम् अनु त्वा निविदं जोहवीमि विद्यामेषं वृजनं जीरदानुम्'
- source_sentence: 'task: search result | query: Verses mentioning journeys or migrations'
sentences:
- 'title: none | text: उद् व् एति सुभगो विश्वचक्षाः साधारणः सूर्यो मानुषाणाम्
चक्षुर् मित्रस्य वरुणस्य देवश् चर्मेव यः समविव्यक् तमांसि'
- 'title: none | text: नू मर्तो दयते सनिष्यन् यो विष्णव उरुगायाय दाशत्
प्र यः सत्राचा मनसा यजात एतावन्तं नर्यम् आविवासात्'
- 'title: none | text: इमे दिवो अनिमिषा पृथिव्याश् चिकित्वांसो अचेतसं नयन्ति
प्रव्राजे चिन् नद्यो गाधम् अस्ति पारं नो अस्य विष्पितस्य पर्षन्'
- source_sentence: 'task: search result | query: Verse about a deity associated with
cosmic order or friendship.'
sentences:
- 'title: none | text: विषूचो अश्वान् युयुजे वनेजा ऋजीतिभी रशनाभिर् गृभीतान्
चक्षदे मित्रो वसुभिः सुजातः सम् आनृधे पर्वभिर् वावृधानः'
- 'title: none | text: त्वाम् अग्ने हविष्मन्तो देवम् मर्तास ईﾱअते
मन्ये त्वा जातवेदसं स हव्या वक्ष्य् आनुषक्'
- 'title: none | text: अप्रतीतो जयति सं धनानि प्रतिजन्यान्य् उत या सजन्या
अवस्यवे यो वरिवः कृणोति ब्रह्मणे राजा तम् अवन्ति देवाः'
- source_sentence: 'task: search result | query: Indra''s participation in Soma rituals
involving dairy products'
sentences:
- 'title: none | text: रथं हिरण्यवन्धुरम् इन्द्रवायू स्वध्वरम्
आ हि स्थाथो दिविस्पृशम्'
- 'title: none | text: इमम् इन्द्र गवाशिरं यवाशिरं च नः पिब
आगत्या वृषभिः सुतम्'
- 'title: none | text: धेनुष् ट इन्द्र सूनृता यजमानाय सुन्वते
गाम् अश्वम् पिप्युषी दुहे'
- source_sentence: 'task: search result | query: वृष्टि-विद्युत्-सदृशं दैविकं आगमनम्'
sentences:
- 'title: none | text: उत द्वार उशतीर् वि श्रयन्ताम् उत देवाṁ उशत आ वहेह'
- 'title: none | text: प्राग्नये बृहते यज्ञियाय ऋतस्य वृष्णे असुराय मन्म
घृतं न यज्ञ आस्ये सुपूतं गिरम् भरे वृषभाय प्रतीचीम्'
- 'title: none | text: असामि हि प्रयज्यवः कण्वं दद प्रचेतसः
असामिभिर् मरुत आ न ऊतिभिर् गन्ता वृष्टिं न विद्युतः'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
model-index:
- name: SentenceTransformer based on google/embeddinggemma-300m
results:
- task:
type: triplet
name: Triplet
dataset:
name: test
type: test
metrics:
- type: cosine_accuracy
value: 0.9553258419036865
name: Cosine Accuracy
---
# SentenceTransformer based on google/embeddinggemma-300m
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m). It maps sentences & paragraphs to a 768-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:** [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m)
- **Maximum Sequence Length:** 2048 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
### 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({'max_seq_length': 2048, 'do_lower_case': False, 'architecture': 'Gemma3TextModel'})
(1): Pooling({'word_embedding_dimension': 768, '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): Dense({'in_features': 768, 'out_features': 3072, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
(3): Dense({'in_features': 3072, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
(4): 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
queries = [
"task: search result | query: \u0935\u0943\u0937\u094d\u091f\u093f-\u0935\u093f\u0926\u094d\u092f\u0941\u0924\u094d-\u0938\u0926\u0943\u0936\u0902 \u0926\u0948\u0935\u093f\u0915\u0902 \u0906\u0917\u092e\u0928\u092e\u094d",
]
documents = [
'title: none | text: असामि हि प्रयज्यवः कण्वं दद प्रचेतसः\nअसामिभिर् मरुत आ न ऊतिभिर् गन्ता वृष्टिं न विद्युतः',
'title: none | text: उत द्वार उशतीर् वि श्रयन्ताम् उत देवाṁ उशत आ वहेह',
'title: none | text: प्राग्नये बृहते यज्ञियाय ऋतस्य वृष्णे असुराय मन्म\nघृतं न यज्ञ आस्ये सुपूतं गिरम् भरे वृषभाय प्रतीचीम्',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 768] [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.5082, 0.0894, 0.0246]])
```
## Evaluation
### Metrics
#### Triplet
* Dataset: `test`
* Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.9553** |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 51,368 training samples
* Columns: sentence_0 and sentence_1
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 13 tokens
- mean: 20.48 tokens
- max: 41 tokens
| - min: 25 tokens
- mean: 48.95 tokens
- max: 100 tokens
|
* Samples:
| sentence_0 | sentence_1 |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------|
| task: search result \| query: Passage describing multiple entities undergoing purification or being purifying | title: none \| text: सम् उ प्रिया अनूषत गावो मदाय घृष्वयः
सोमासः कृण्वते पथः पवमानास इन्दवः |
| task: search result \| query: Which verse describes adversaries or those who attempt to inflict injury, but are ultimately rendered ineffective? | title: none \| text: न यं रिपवो न रिषण्यवो गर्भे सन्तं रेषणा रेषयन्ति
अन्धा अपश्या न दभन्न् अभिख्या नित्यास ईम् प्रेतारो अरक्षन् |
| task: search result \| query: A hymn requesting blessings for both human and animal residents of a dwelling. | title: none \| text: वास्तोष्पते प्रति जानीह्य् अस्मान् स्वावेशो अनमीवो भवा नः
यत् त्वेमहे प्रति तन् नो जुषस्व शं नो भव द्विपदे शं चतुष्पदे |
* Loss: [MultipleNegativesRankingLoss](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
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `fp16`: True
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `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`: 3
- `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
- `bf16`: False
- `fp16`: True
- `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}
- `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}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `project`: huggingface
- `trackio_space_id`: trackio
- `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
- `hub_revision`: None
- `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`: no
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: True
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
- `router_mapping`: {}
- `learning_rate_mapping`: {}