---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:69370
- loss:CosineSimilarityLoss
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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।"
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
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)
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
### 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]
```
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `dev-evaluation`
* Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:--------|
| pearson_cosine | nan |
| **spearman_cosine** | **nan** |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 69,370 training samples
* Columns: sentence_0, sentence_1, and label
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------|
| type | string | string | float |
| details | - min: 3 tokens
- mean: 29.5 tokens
- max: 128 tokens
| - min: 3 tokens
- mean: 41.43 tokens
- max: 128 tokens
| - min: 0.9
- mean: 0.9
- max: 0.9
|
* Samples:
| sentence_0 | sentence_1 | label |
|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:-----------------|
| “Wishing our most beloved & widely respected Atal Ji a happy birthday. | “ଆମର ଅତ୍ୟନ୍ତ ପ୍ରିୟ ଓ ସ୍ନେହର ଆଦରଣୀୟ ଅଟଳ ଜୀଙ୍କୁ ଜନ୍ମ ଦିନର ଶୁଭେଚ୍ଛା । | 0.9 |
| The project is also an example of environment friendly development. | ଏହି ପରିଯୋଜନା ପରିବେଶ ଅନୁକୂଳ ବିକାଶର ଏକ ଉଦାହରଣ ମଧ୍ୟ । | 0.9 |
| Old as the hills | ପାହାଡ଼ ପ୍ର ପୁରୁଣା | 0.9 |
* Loss: [CosineSimilarityLoss](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
Click to expand
- `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