--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:10 - loss:CosineSimilarityLoss base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 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। 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") # 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] ``` ## 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: 10 training samples * Columns: sentence_0, sentence_1, and label * Approximate statistics based on the first 10 samples: | | sentence_0 | sentence_1 | label | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence_0 | sentence_1 | label | |:-----------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------|:-----------------| | 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. | ସଦାପ୍ରଭୁଙ୍କ ବିନା ମୁଁ ଏ ଦେଶ ଧଂସ କରିବାକୁ ଆସି ନାହିଁ। ସଦାପ୍ରଭୁ ମାେତେ କହିଲେ, "ଏହି ଦେଶ ବିରୁଦ୍ଧ ରେ ୟାଅ ଓ ତାକୁ ଧ୍ବଂସ କର!" | 0.9 | | He said that Yoga could lead to a calm, creative and content life, removing tensions and needless anxiety. | ଅବସାଦ ଏବଂ ଅଯଥା ଚିନ୍ତା ଦୂର କରି ଯୋଗ ଏକ ଶାନ୍ତ, ସୃଜନଶୀଳ ଏବଂ ସାମଗ୍ରୀକ ଜୀବନ ଆଡ଼କୁ ନେଇଯାଇପାରେ । | 0.9 | | But that night was special. | ଆଜି ରାତ୍ରିର କଥା କିନ୍ତୁ ସ୍ଵତନ୍ତ୍ର । | 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
### 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", } ```