sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:10 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:10
- loss:CosineSimilarityLoss
widget:
- source_sentence: He said he has personally visited the North Eastern States several
    times to review development work.
  sentences:
  - ଏହି ପରିବର୍ତ୍ତନ ଦ୍ୱାରା ଭାରତର ରାଜନୀତିରେ ଦୁଇଟି ଗୁରୁତ୍ୱପୂର୍ଣ୍ଣ ପରିବର୍ତ୍ତନ ହେଲା ।
  - ଆଜି ରାତ୍ରିର କଥା କିନ୍ତୁ ସ୍ଵତନ୍ତ୍ର ।
  - ସେ ବ୍ୟକ୍ତିଗତ ଭାବେ ଅନେକ ଥର ବିକାଶ କାର୍ଯ୍ୟର ସମୀକ୍ଷା କରିବା ପାଇଁ ଉତ୍ତରପୂର୍ବାଂଚଳ ରାଜ୍ୟମାନଙ୍କୁ
    ଗସ୍ତ କରିଛନ୍ତି ।
- source_sentence: That they may keep thee from the strange woman, from the stranger
    which flattereth with her words.
  sentences:
  - ସମାନେେ ତାହା ମଧିଅରେ ନିରାପଦ ରେ ବାସ କରିବେ। ସମାନେେ ଗୃହ ନିର୍ମାଣ କରିବେ ଓ ଦ୍ରାକ୍ଷାକ୍ଷେତ୍ର
    ରୋପଣ କରିବେ। ମୁଁ ତା'ର ଚତୁର୍ଦ୍ଦିଗସ୍ଥିତ ସମସ୍ତ ଦେଶକୁ ଦଣ୍ଡିତ କରିବି ଯେଉଁମାନେ ସମାନଙ୍କେୁ
    ତିରସ୍କାର କଲେ, ତା'ପ ରେ ସମାନେେ ନିରାପଦ ରେ ବାସ କରିବେ, ତହିଁରେ ମୁଁ ଯେ ସଦାପ୍ରଭୁ ଓ ସମାନଙ୍କେର
    ପରମେଶ୍ବର ଅଟେ ଏହା ସମାନେେ ଜାଣିବେ।"
  - ଏହି ବୁଝାମଣାର ଉଦ୍ଦେଶ୍ୟ, ଦୁଗ୍ଧ ଉତ୍ପାଦନ ବିକାଶ ଏବଂ ସମ୍ବଳ ସୁଦୃଢ଼ୀକରଣ ଆଧାରରେ ବର୍ତ୍ତମାନର
    ଜ୍ଞାନକୁ ବ୍ୟାପକ କରିବା ଲାଗି ପଶୁପାଳନ ଏବଂ ଦୁଗ୍ଧ ଉତ୍ପାଦନ କ୍ଷେତ୍ରରେ ଦ୍ୱିପାକ୍ଷିକ ସହଯୋଗକୁ
    ପ୍ରୋତ୍ସାହନ ଦେବା ।
  - ତବେେ ତାହା ତୁମ୍ଭକୁ ଅନ୍ୟ ପର ସ୍ତ୍ରୀଠାରୁ ରକ୍ଷା କରିବ। ଏବଂ ବ୍ଯଭିଚାରିଣୀ ସ୍ତ୍ରୀଙ୍କଠାରୁ
    ମଧ୍ଯ ରକ୍ଷା କରିବ।
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")
# Run inference
sentences = [
    'That they may keep thee from the strange woman, from the stranger which flattereth with her words.',
    'ତବେେ ତାହା ତୁମ୍ଭକୁ ଅନ୍ୟ ପର ସ୍ତ୍ରୀଠାରୁ ରକ୍ଷା କରିବ। ଏବଂ ବ୍ଯଭିଚାରିଣୀ ସ୍ତ୍ରୀଙ୍କଠାରୁ ମଧ୍ଯ ରକ୍ଷା କରିବ।',
    'ସମାନେେ ତାହା ମଧିଅରେ ନିରାପଦ ରେ ବାସ କରିବେ। ସମାନେେ ଗୃହ ନିର୍ମାଣ କରିବେ ଓ ଦ୍ରାକ୍ଷାକ୍ଷେତ୍ର ରୋପଣ କରିବେ। ମୁଁ ତା\'ର ଚତୁର୍ଦ୍ଦିଗସ୍ଥିତ ସମସ୍ତ ଦେଶକୁ ଦଣ୍ଡିତ କରିବି ଯେଉଁମାନେ ସମାନଙ୍କେୁ ତିରସ୍କାର କଲେ, ତା\'ପ ରେ ସମାନେେ ନିରାପଦ ରେ ବାସ କରିବେ, ତହିଁରେ ମୁଁ ଯେ ସଦାପ୍ରଭୁ ଓ ସମାନଙ୍କେର ପରମେଶ୍ବର ଅଟେ ଏହା ସମାନେେ ଜାଣିବେ।"',
]
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: 10 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 10 samples:
  |         | sentence_0                                                                       | sentence_1                                                                        | label                                                         |
  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------|
  | type    | string                                                                           | string                                                                            | float                                                         |
  | details | <ul><li>min: 6 tokens</li><li>mean: 27.6 tokens</li><li>max: 66 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 37.8 tokens</li><li>max: 107 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>Am I now come up without the LORD against this place to destroy it? The LORD said to me, Go up against this land, and destroy it.</code> | <code>ସଦାପ୍ରଭୁଙ୍କ ବିନା ମୁଁ ଏ ଦେଶ ଧଂସ କରିବାକୁ ଆସି ନାହିଁ। ସଦାପ୍ରଭୁ ମାେତେ କହିଲେ, "ଏହି ଦେଶ ବିରୁଦ୍ଧ ରେ ୟାଅ ଓ ତାକୁ ଧ୍ବଂସ କର!"</code> | <code>0.9</code> |
  | <code>He said that Yoga could lead to a calm, creative and content life, removing tensions and needless anxiety.</code>                        | <code>ଅବସାଦ ଏବଂ ଅଯଥା ଚିନ୍ତା ଦୂର କରି ଯୋଗ ଏକ ଶାନ୍ତ, ସୃଜନଶୀଳ ଏବଂ ସାମଗ୍ରୀକ ଜୀବନ ଆଡ଼କୁ ନେଇଯାଇପାରେ ।</code>                           | <code>0.9</code> |
  | <code>But that night was special.</code>                                                                                                       | <code>ଆଜି ରାତ୍ରିର କଥା କିନ୍ତୁ ସ୍ଵତନ୍ତ୍ର ।</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 | dev-evaluation_spearman_cosine |
|:-----:|:----:|:------------------------------:|
| 1.0   | 1    | nan                            |
| 2.0   | 2    | nan                            |
| 3.0   | 3    | 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",
}
```

<!--
## 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.*
-->