sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:69370 loss:CosineSimilarityLoss arxiv:1908.10084 base_model:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 base_model:finetune:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:69370
- loss:CosineSimilarityLoss
widget:
- source_sentence: It is you who have to judge whether I fill the bill.”
  sentences:
  - ସେ ଆହୁରି କହିଥିଲେ ଯେ ଏହି ଆନ୍ଦୋଳନ ଏତିକିରେ ଅଟକି ଯିବ ନାହିଁ, ବରଂ ଏହା ଅନ୍ୟ ନଦୀକୁ ମଧ୍ୟ
    ପରିବ୍ୟାପ୍ତ ହେବ ।
  - ସେ ଦୃଷ୍ଟିରୁ ମୁଁ ତମର ଯୋଗୀ ହେବି କି ନା ସେ କଥା ତମେ ହି ବିଚାର କରିବା କଥା |”
  - ଭାରତର ପ୍ରଥମ ରାଷ୍ଟ୍ରପତି ଭାବେ ତାଙ୍କ ନେତୃତ୍ୱ ଏବଂ ମାର୍ଗଦର୍ଶନ ଦେଶ ଗଠନର ପ୍ରାରମ୍ଭିକ ସମୟରେ
    ଅତ୍ୟନ୍ତ ମୂଲ୍ୟବାନ ଥିଲା ।
- source_sentence: Further, the MoU will facilitate in improving rehabilitation of
    persons with disabilities especially for persons with intellectual disability
    and mental illness in both the countries.
  sentences:
  - ତେଣୁ ଆଜି ବାସର ଘରଟିକୁ ସେ ସବୁଦିନଠାରୁ ଅଧ୍ରକ ଆଡ଼ମ୍ବରପୂର୍ଣ୍ଣ କରି ସଜାଇଛି ।
  - ଭାରତର ଏହି ମହାନ୍ ଭୂମି ଅନେକ ମହାପୁରୁଷଙ୍କୁ ଜନ୍ମ ଦେଇଛି ଏବଂ ସେହି ମହାପୁରୁଷମାନେ ମାନବ ଜାତି
    ପାଇଁ କିଛି ଚମକ୍ରାର ଏବଂ ଅବିସ୍ମରଣୀୟ କାର୍ଯ୍ୟ କରିଛନ୍ତି ।
  - ପାଠ୍ୟକ୍ରମକୁ ବିକଶିତ କରିବା ଏବଂ ଅଧ୍ୟାପନା କର୍ମୀମାନଙ୍କ କ୍ଷମତା ନିର୍ମାଣ କ୍ଷେତ୍ରରେ ମଧ୍ୟ
    ମାନବ ସମ୍ବଳ ବିକାଶ ମନ୍ତ୍ରଣାଳୟ, ଆଫଗାନିସ୍ତାନର ଉଚ୍ଚ ଶିକ୍ଷା ମନ୍ତ୍ରଣାଳୟକୁ ସହାୟତା ପ୍ରଦାନ
    କରିବ ।
- source_sentence: 'And all the people saw the cloudy pillar stand at the tabernacle
    door: and all the people rose up and worshipped, every man in his tent door.'
  sentences:
  - ଇଶ୍ରାୟେଲର ରାଜା ୟିହୋଶାଫଟ୍କୁ କହିଲେ, "ମୁଁ ଛଦ୍ମ ବେଶ ରେ ୟୁଦ୍ଧ କରିବାକୁ ୟିବି, ମାତ୍ର ତୁମ୍ଭେ
    ତୁମ୍ଭର ରାଜ ପୋଷାକ ପିନ୍ଧିବ।" ତେଣୁ ଇଶ୍ରାୟେଲର ରାଜା ଛଦ୍ମ ବେଶ ରେ ୟୁଦ୍ଧକୁ ଗଲେ।
  - ତେଣୁ ଯେତବେେଳେ ଲୋକମାନେ ତମ୍ବୁର ଦ୍ବାର ଦେଶ ରେ ମେଘ ସ୍ତମ୍ଭ ଦେଖିଲେ। ସେତବେେଳେ ଲୋକମାନେ
    ଆପଣା ଆପଣା ତମ୍ବୁ ଦ୍ବାର ରେ ଥାଇ ପ୍ରଣାମ କଲେ।
  - ଆମେ ଏବେ ଗୋଟିଏ ପରେ ଗୋଟିଏ ରାଷ୍ଟ୍ର ସହ ଏଥି ଘେନି ରାଜିନାମା ସ୍ୱାକ୍ଷର କରୁଛୁ ।
- source_sentence: 'And nation was destroyed of nation, and city of city: for God
    did vex them with all adversity.'
  sentences:
  - ଏହା ଆମର ପାରସ୍ପରିକ ବୁଝାମଣା ଏବଂ ଆଭିମୁଖ୍ୟରେ ସମୃଦ୍ଧ ଥିଲା ।
  - ଗୋଟିଏ ଦେଶ ଆଉ ଏକ ଦେଶକୁ ବିନାଶ କରୁଥିଲା ଓ ଗୋଟିଏ ନଗର ଆଉ ଗୋଟିଏ ନଗରକୁ ଧ୍ବଂସ କରୁଥିଲା।
    ପରମେଶ୍ବର ସମାନଙ୍କେ ପ୍ରତି ସବୁ ପ୍ରକାର ଦୁର୍ଦ୍ଦଶା ଘଟାଉ ଥିବାରୁ ଏହିପରି ଘଟୁଥିଲା।
  - ଏହି ପର୍ବ ସମସ୍ତଙ୍କ ଜୀବନରେ ସୁଖ, ସମୃଦ୍ଧି ଏବଂ ଶାରୀରିକ ସୁସ୍ଥତା ଭରିଦେଉ ।
- source_sentence: For I will defend this city, to save it, for mine own sake, and
    for my servant David's sake.
  sentences:
  - ଆମ ଦେଶର ସୌଭାଗ୍ୟ ଯେ ଆମେମାନେ ଯୌଥ ପରିବାରରେ ବଢ଼ୁ, ବଡ଼ ପରିବାରରେ ମିଳିମିଶି ରହୁ ଫଳରେ ମାନସିକ
    ଅବସାଦର ସମ୍ଭାବନା କମ୍ ଥାଏ ।
  - ସେଠାକାର ଲୋକମାନଙ୍କୁ ଆଶ୍ୱାସନା ଦେଇ ପ୍ରଧାନମନ୍ତ୍ରୀ କହିଥିଲେ ଯେ ସେମାନଙ୍କୁ ସବୁପ୍ରକାରର
    ସହାୟତା ଯୋଗାଇ ଦିଆଯିବ ।
  - ମୁଁ ଏହି ନଗରକୁ ରକ୍ଷା କରିବି। ମାେ ନିଜ ସକାେଶ ଓ ମାରେ ଦାସ ଦାଉଦ ନିମନ୍ତେ ମୁଁ ଏ ନଗରକୁ ସୁରକ୍ଷା
    ଦବେି।"
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: dev evaluation
      type: dev-evaluation
    metrics:
    - type: pearson_cosine
      value: .nan
      name: Pearson Cosine
    - type: spearman_cosine
      value: .nan
      name: Spearman Cosine
---

# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-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/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision 8d6b950845285729817bf8e1af1861502c2fed0c -->
- **Maximum Sequence Length:** 128 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': 128, '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})
)
```

## 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("Debk/Oriya_paraphrase-multilingual-MiniLM-L12-v2_odien")
# Run inference
sentences = [
    "For I will defend this city, to save it, for mine own sake, and for my servant David's sake.",
    'ମୁଁ ଏହି ନଗରକୁ ରକ୍ଷା କରିବି। ମାେ ନିଜ ସକାେଶ ଓ ମାରେ ଦାସ ଦାଉଦ ନିମନ୍ତେ ମୁଁ ଏ ନଗରକୁ ସୁରକ୍ଷା ଦବେି।"',
    'ସେଠାକାର ଲୋକମାନଙ୍କୁ ଆଶ୍ୱାସନା ଦେଇ ପ୍ରଧାନମନ୍ତ୍ରୀ କହିଥିଲେ ଯେ ସେମାନଙ୍କୁ ସବୁପ୍ରକାରର ସହାୟତା ଯୋଗାଇ ଦିଆଯିବ ।',
]
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]
```

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

| Metric              | Value   |
|:--------------------|:--------|
| pearson_cosine      | nan     |
| **spearman_cosine** | **nan** |

<!--
## 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: 69,370 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: 3 tokens</li><li>mean: 29.5 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 41.43 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 0.9</li><li>mean: 0.9</li><li>max: 0.9</li></ul> |
* Samples:
  | sentence_0                                                                          | sentence_1                                                                      | label            |
  |:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:-----------------|
  | <code>“Wishing our most beloved & widely respected Atal Ji a happy birthday.</code> | <code>“ଆମର ଅତ୍ୟନ୍ତ ପ୍ରିୟ ଓ ସ୍ନେହର ଆଦରଣୀୟ ଅଟଳ ଜୀଙ୍କୁ ଜନ୍ମ ଦିନର ଶୁଭେଚ୍ଛା ।</code> | <code>0.9</code> |
  | <code>The project is also an example of environment friendly development.</code>    | <code>ଏହି ପରିଯୋଜନା ପରିବେଶ ଅନୁକୂଳ ବିକାଶର ଏକ ଉଦାହରଣ ମଧ୍ୟ ।</code>                 | <code>0.9</code> |
  | <code>Old as the hills</code>                                                       | <code>ପାହାଡ଼ ପ୍ର ପୁରୁଣା</code>                                                  | <code>0.9</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
- `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
- `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
- `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`: 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
- `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
- `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 | dev-evaluation_spearman_cosine |
|:------:|:-----:|:-------------:|:------------------------------:|
| 0.1153 | 500   | 0.1806        | -                              |
| 0.2306 | 1000  | 0.0047        | nan                            |
| 0.3459 | 1500  | 0.0013        | -                              |
| 0.4613 | 2000  | 0.0006        | nan                            |
| 0.5766 | 2500  | 0.0003        | -                              |
| 0.6919 | 3000  | 0.0002        | nan                            |
| 0.8072 | 3500  | 0.0001        | -                              |
| 0.9225 | 4000  | 0.0001        | nan                            |
| 1.0    | 4336  | -             | nan                            |
| 1.0378 | 4500  | 0.0           | -                              |
| 1.1531 | 5000  | 0.0           | nan                            |
| 1.2685 | 5500  | 0.0           | -                              |
| 1.3838 | 6000  | 0.0           | nan                            |
| 1.4991 | 6500  | 0.0           | -                              |
| 1.6144 | 7000  | 0.0           | nan                            |
| 1.7297 | 7500  | 0.0           | -                              |
| 1.8450 | 8000  | 0.0           | nan                            |
| 1.9603 | 8500  | 0.0           | -                              |
| 2.0    | 8672  | -             | nan                            |
| 2.0756 | 9000  | 0.0           | nan                            |
| 2.1910 | 9500  | 0.0           | -                              |
| 2.3063 | 10000 | 0.0           | nan                            |
| 2.4216 | 10500 | 0.0           | -                              |
| 2.5369 | 11000 | 0.0           | nan                            |
| 2.6522 | 11500 | 0.0           | -                              |
| 2.7675 | 12000 | 0.0           | nan                            |
| 2.8828 | 12500 | 0.0           | -                              |
| 2.9982 | 13000 | 0.0           | nan                            |
| 3.0    | 13008 | -             | nan                            |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.47.1
- PyTorch: 2.5.1+cu121
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0

## 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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