---
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:1K<n<10K
- loss:CosineSimilarityLoss
base_model: Rajan/NepaliBERT
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: अघिल्लो वर्ष देखि।
  sentences:
  - अघिल्लो वर्ष देखि .।
  - एउटी महिला बन्दुक हान्दै छिन्।
  - हिउँमा हिंडिरहेको सेतो कुकुर।
- source_sentence: यो मोलोच दृश्य हो।
  sentences:
  - वास्तवमा, यो केवल डच हो।
  - एउटा मानिस डोरीमा झुलिरहेको छ।
  - रातो झोला लिएर सडकमा उभिएकी केटी।
- source_sentence: दमास्कसमा रुसीहरू!
  sentences:
  - रुसीहरू दमस्कसमा किन छन्?
  - कसैले मिर्चको बीउ निकाल्दै छ।
  - एकजना मानिस साइकल चलाउँदै छन्।
- source_sentence: रेल ट्र्याकमा रेल।
  sentences:
  - लामो रेल रेल ट्र्याकमा छ।
  - एउटी महिला सिडु चढिरहेकी छिन्।
  - एक व्यक्ति सडकमा हिर्किरहेको छ।
- source_sentence: रातो, डबल डेकर बस।
  sentences:
  - रातो डबल डेकर बस।
  - दुई कालो कुकुर हिउँमा हिंड्दै।
  - एउटी महिला मासु फ्राइरहेकी छिन्।
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on Rajan/NepaliBERT
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: stsb dev nepali
      type: stsb-dev-nepali
    metrics:
    - type: pearson_cosine
      value: 0.6971387543395983
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6623150295431888
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.6332077130918778
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.6078651194262178
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.6339817618698202
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6090065238762821
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.4848273995348276
      name: Pearson Dot
    - type: spearman_dot
      value: 0.5306425402414711
      name: Spearman Dot
    - type: pearson_max
      value: 0.6971387543395983
      name: Pearson Max
    - type: spearman_max
      value: 0.6623150295431888
      name: Spearman Max
datasets:
- syubraj/stsb_nepali
---

# SentenceTransformer based on Rajan/NepaliBERT

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Rajan/NepaliBERT](https://huggingface.co/Rajan/NepaliBERT). 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:** [Rajan/NepaliBERT](https://huggingface.co/Rajan/NepaliBERT) <!-- at revision 996c3b86b779a63225b473221678447c9d9185d0 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (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})
)
```

## 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("syubraj/sentence_similarity_nepali_v2")
# Run inference
sentences = [
    'रातो, डबल डेकर बस।',
    'रातो डबल डेकर बस।',
    'दुई कालो कुकुर हिउँमा हिंड्दै।',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

<!--
### 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.*
-->

## Evaluation

### Metrics

#### Semantic Similarity
* Dataset: `stsb-dev-nepali`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric             | Value      |
|:-------------------|:-----------|
| pearson_cosine     | 0.6971     |
| spearman_cosine    | 0.6623     |
| pearson_manhattan  | 0.6332     |
| spearman_manhattan | 0.6079     |
| pearson_euclidean  | 0.634      |
| spearman_euclidean | 0.609      |
| pearson_dot        | 0.4848     |
| spearman_dot       | 0.5306     |
| pearson_max        | 0.6971     |
| **spearman_max**   | **0.6623** |

<!--
## 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: 4,599 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                       | sentence_1                                                                        | label                                                          |
  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                           | string                                                                            | float                                                          |
  | details | <ul><li>min: 6 tokens</li><li>mean: 19.5 tokens</li><li>max: 81 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 19.43 tokens</li><li>max: 75 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence_0                                                               | sentence_1                                                     | label                           |
  |:-------------------------------------------------------------------------|:---------------------------------------------------------------|:--------------------------------|
  | <code>एक व्यक्ति प्याज काट्दै छ।</code>                                  | <code>एउटा बिरालो शौचालयमा पपिङ गर्दैछ।</code>                 | <code>0.0</code>                |
  | <code>क्यानडाको तेल रेल विस्फोटमा थप मृत्यु हुने अपेक्षा गरिएको छ</code> | <code>क्यानडामा रेल दुर्घटनामा पाँच जनाको मृत्यु भएको छ</code> | <code>0.5599999904632569</code> |
  | <code>एउटी महिला झिंगा माझ्दै छिन्।</code>                               | <code>एउटी महिला केही झिंगा माझ्दै।</code>                     | <code>1.0</code>                |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `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`: steps
- `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
- `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}
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
<details><summary>Click to expand</summary>

| Epoch   | Step  | Training Loss | stsb-dev-nepali_spearman_max |
|:-------:|:-----:|:-------------:|:----------------------------:|
| 1.0     | 288   | -             | 0.5355                       |
| 1.7361  | 500   | 0.0723        | -                            |
| 2.0     | 576   | -             | 0.5794                       |
| 3.0     | 864   | -             | 0.6108                       |
| 3.4722  | 1000  | 0.047         | 0.6147                       |
| 4.0     | 1152  | -             | 0.6259                       |
| 5.0     | 1440  | -             | 0.6356                       |
| 5.2083  | 1500  | 0.034         | -                            |
| 6.0     | 1728  | -             | 0.6329                       |
| 6.9444  | 2000  | 0.0217        | 0.6375                       |
| 7.0     | 2016  | -             | 0.6382                       |
| 8.0     | 2304  | -             | 0.6468                       |
| 8.6806  | 2500  | 0.0137        | -                            |
| 9.0     | 2592  | -             | 0.6348                       |
| 10.0    | 2880  | -             | 0.6332                       |
| 10.4167 | 3000  | 0.0102        | 0.6427                       |
| 11.0    | 3168  | -             | 0.6370                       |
| 12.0    | 3456  | -             | 0.6515                       |
| 12.1528 | 3500  | 0.0084        | -                            |
| 13.0    | 3744  | -             | 0.6546                       |
| 13.8889 | 4000  | 0.0069        | 0.6400                       |
| 14.0    | 4032  | -             | 0.6610                       |
| 15.0    | 4320  | -             | 0.6495                       |
| 15.625  | 4500  | 0.006         | -                            |
| 16.0    | 4608  | -             | 0.6574                       |
| 17.0    | 4896  | -             | 0.6486                       |
| 17.3611 | 5000  | 0.0053        | 0.6589                       |
| 18.0    | 5184  | -             | 0.6592                       |
| 19.0    | 5472  | -             | 0.6488                       |
| 19.0972 | 5500  | 0.0047        | -                            |
| 20.0    | 5760  | -             | 0.6436                       |
| 20.8333 | 6000  | 0.0044        | 0.6576                       |
| 21.0    | 6048  | -             | 0.6515                       |
| 22.0    | 6336  | -             | 0.6541                       |
| 22.5694 | 6500  | 0.0041        | -                            |
| 23.0    | 6624  | -             | 0.6549                       |
| 24.0    | 6912  | -             | 0.6571                       |
| 24.3056 | 7000  | 0.0037        | 0.6603                       |
| 25.0    | 7200  | -             | 0.6699                       |
| 26.0    | 7488  | -             | 0.6653                       |
| 26.0417 | 7500  | 0.0037        | -                            |
| 27.0    | 7776  | -             | 0.6609                       |
| 27.7778 | 8000  | 0.0033        | 0.6578                       |
| 28.0    | 8064  | -             | 0.6606                       |
| 29.0    | 8352  | -             | 0.6614                       |
| 29.5139 | 8500  | 0.0031        | -                            |
| 30.0    | 8640  | -             | 0.6579                       |
| 31.0    | 8928  | -             | 0.6688                       |
| 31.25   | 9000  | 0.0028        | 0.6650                       |
| 32.0    | 9216  | -             | 0.6639                       |
| 32.9861 | 9500  | 0.0027        | -                            |
| 33.0    | 9504  | -             | 0.6624                       |
| 34.0    | 9792  | -             | 0.6646                       |
| 34.7222 | 10000 | 0.0025        | 0.6530                       |
| 35.0    | 10080 | -             | 0.6587                       |
| 36.0    | 10368 | -             | 0.6671                       |
| 36.4583 | 10500 | 0.0025        | -                            |
| 37.0    | 10656 | -             | 0.6614                       |
| 38.0    | 10944 | -             | 0.6602                       |
| 38.1944 | 11000 | 0.0024        | 0.6576                       |
| 39.0    | 11232 | -             | 0.6665                       |
| 39.9306 | 11500 | 0.0023        | -                            |
| 40.0    | 11520 | -             | 0.6663                       |
| 41.0    | 11808 | -             | 0.6734                       |
| 41.6667 | 12000 | 0.0021        | 0.6633                       |
| 42.0    | 12096 | -             | 0.6667                       |
| 43.0    | 12384 | -             | 0.6679                       |
| 43.4028 | 12500 | 0.002         | -                            |
| 44.0    | 12672 | -             | 0.6701                       |
| 45.0    | 12960 | -             | 0.6650                       |
| 45.1389 | 13000 | 0.0019        | 0.6680                       |
| 46.0    | 13248 | -             | 0.6631                       |
| 46.875  | 13500 | 0.0018        | -                            |
| 47.0    | 13536 | -             | 0.6643                       |
| 48.0    | 13824 | -             | 0.6631                       |
| 48.6111 | 14000 | 0.0017        | 0.6648                       |
| 49.0    | 14112 | -             | 0.6648                       |
| 50.0    | 14400 | -             | 0.6619                       |
| 50.3472 | 14500 | 0.0017        | -                            |
| 51.0    | 14688 | -             | 0.6633                       |
| 52.0    | 14976 | -             | 0.6622                       |
| 52.0833 | 15000 | 0.0016        | 0.6612                       |
| 53.0    | 15264 | -             | 0.6670                       |
| 53.8194 | 15500 | 0.0015        | -                            |
| 54.0    | 15552 | -             | 0.6618                       |
| 55.0    | 15840 | -             | 0.6641                       |
| 55.5556 | 16000 | 0.0015        | 0.6617                       |
| 56.0    | 16128 | -             | 0.6669                       |
| 57.0    | 16416 | -             | 0.6645                       |
| 57.2917 | 16500 | 0.0014        | -                            |
| 58.0    | 16704 | -             | 0.6642                       |
| 59.0    | 16992 | -             | 0.6579                       |
| 59.0278 | 17000 | 0.0013        | 0.6592                       |
| 60.0    | 17280 | -             | 0.6589                       |
| 60.7639 | 17500 | 0.0014        | -                            |
| 61.0    | 17568 | -             | 0.6685                       |
| 62.0    | 17856 | -             | 0.6673                       |
| 62.5    | 18000 | 0.0012        | 0.6669                       |
| 63.0    | 18144 | -             | 0.6665                       |
| 64.0    | 18432 | -             | 0.6626                       |
| 64.2361 | 18500 | 0.0012        | -                            |
| 65.0    | 18720 | -             | 0.6619                       |
| 65.9722 | 19000 | 0.0012        | 0.6643                       |
| 66.0    | 19008 | -             | 0.6651                       |
| 67.0    | 19296 | -             | 0.6628                       |
| 67.7083 | 19500 | 0.0011        | -                            |
| 68.0    | 19584 | -             | 0.6658                       |
| 69.0    | 19872 | -             | 0.6615                       |
| 69.4444 | 20000 | 0.0011        | 0.6627                       |
| 70.0    | 20160 | -             | 0.6657                       |
| 71.0    | 20448 | -             | 0.6663                       |
| 71.1806 | 20500 | 0.0011        | -                            |
| 72.0    | 20736 | -             | 0.6634                       |
| 72.9167 | 21000 | 0.001         | 0.6649                       |
| 73.0    | 21024 | -             | 0.6632                       |
| 74.0    | 21312 | -             | 0.6658                       |
| 74.6528 | 21500 | 0.001         | -                            |
| 75.0    | 21600 | -             | 0.6639                       |
| 76.0    | 21888 | -             | 0.6601                       |
| 76.3889 | 22000 | 0.001         | 0.6623                       |
| 77.0    | 22176 | -             | 0.6607                       |
| 78.0    | 22464 | -             | 0.6613                       |
| 78.125  | 22500 | 0.0009        | -                            |
| 79.0    | 22752 | -             | 0.6613                       |
| 79.8611 | 23000 | 0.0009        | 0.6615                       |
| 80.0    | 23040 | -             | 0.6615                       |
| 81.0    | 23328 | -             | 0.6617                       |
| 81.5972 | 23500 | 0.0008        | -                            |
| 82.0    | 23616 | -             | 0.6604                       |
| 83.0    | 23904 | -             | 0.6605                       |
| 83.3333 | 24000 | 0.0008        | 0.6602                       |
| 84.0    | 24192 | -             | 0.6628                       |
| 85.0    | 24480 | -             | 0.6603                       |
| 85.0694 | 24500 | 0.0008        | -                            |
| 86.0    | 24768 | -             | 0.6602                       |
| 86.8056 | 25000 | 0.0008        | 0.6592                       |
| 87.0    | 25056 | -             | 0.6611                       |
| 88.0    | 25344 | -             | 0.6612                       |
| 88.5417 | 25500 | 0.0008        | -                            |
| 89.0    | 25632 | -             | 0.6607                       |
| 90.0    | 25920 | -             | 0.6598                       |
| 90.2778 | 26000 | 0.0008        | 0.6607                       |
| 91.0    | 26208 | -             | 0.6615                       |
| 92.0    | 26496 | -             | 0.6615                       |
| 92.0139 | 26500 | 0.0007        | -                            |
| 93.0    | 26784 | -             | 0.6609                       |
| 93.75   | 27000 | 0.0007        | 0.6607                       |
| 94.0    | 27072 | -             | 0.6612                       |
| 95.0    | 27360 | -             | 0.6624                       |
| 95.4861 | 27500 | 0.0007        | -                            |
| 96.0    | 27648 | -             | 0.6627                       |
| 97.0    | 27936 | -             | 0.6618                       |
| 97.2222 | 28000 | 0.0007        | 0.6619                       |
| 98.0    | 28224 | -             | 0.6621                       |
| 98.9583 | 28500 | 0.0007        | -                            |
| 99.0    | 28512 | -             | 0.6623                       |
| 100.0   | 28800 | -             | 0.6623                       |

</details>

### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.0.0
- Transformers: 4.41.2
- PyTorch: 2.1.2
- Accelerate: 0.30.1
- Datasets: 2.19.2
- Tokenizers: 0.19.1

## 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",
}
```

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