Add new SentenceTransformer model.

951adb6 verified 13 days ago

19.7 kB

	---
	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) <!-- 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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