samskritam-xlm-roberata-base

SentenceTransformer based on FacebookAI/xlm-roberta-base

This is a sentence-transformers model finetuned from FacebookAI/xlm-roberta-base on the en-sa dataset. It maps sentences & paragraphs to a 768-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: FacebookAI/xlm-roberta-base <!-- at revision e73636d4f797dec63c3081bb6ed5c7b0bb3f2089 -->
• Maximum Sequence Length: 128 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • en-sa

<!-- - **Language:** Unknown --> <!-- - **License:** Unknown -->

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, '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:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("saikasyap/samskritam-xlm-roberata-base")
# Run inference
sentences = [
    'अनन्तरं Microbiology इति टङ्कनं करोमि ।',
    'Then I will type Microbiology.',
    'The comic image basically consists of three parts.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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.* --> <!-- ## 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

en-sa

• Dataset: en-sa
• Size: 140,359 training samples
• Columns: <code>non_english</code>, <code>english</code>, and <code>label</code>
• Approximate statistics based on the first 1000 samples:

  	non_english	english	label
  type	string	string	list
  details	<ul><li>min: 26 tokens</li><li>mean: 42.11 tokens</li><li>max: 128 tokens</li></ul>	<ul><li>min: 19 tokens</li><li>mean: 50.63 tokens</li><li>max: 128 tokens</li></ul>	<ul><li>size: 768 elements</li></ul>

• Samples:

  non_english	english	label
  <code>ॐ तपः स्वाध्यायनिरतं तपस्वी वाग्विदां वरम्। नारदं परिपप्रच्छ वाल्मीकिर्मुनिपुङ्गवम्॥</code>	<code>The ascetic Vālmīki asked Nārada, the best of sages and foremost of those conversant with words, ever engaged in austerities and Vedic studies.</code>	<code>[0.15034635365009308, 0.35359007120132446, -0.3348075747489929, 0.15415771305561066, 0.020571526139974594, ...]</code>
  <code>कोन्वस्मिन् साम्प्रतं लोके गुणवान् कश्च वीर्यवान्। धर्मज्ञश्च कृतज्ञश्च सत्यवाक्यो दृढत्नतः॥</code>	<code>Who at present in this world is like crowned with qualities, and with prowess, knowing duty, and grateful, and truthful, and firm in vow.</code>	<code>[-0.46556514501571655, 0.4740210175514221, -0.2033461034297943, -1.6129034757614136, -0.016881834715604782, ...]</code>
  <code>चारित्रेण च को युक्तः सर्वभूतेषु को हितः। विद्वान् कः कः समर्थश्च कश्चैकप्रियदर्शनः॥</code>	<code>Who is qualified by virtue of his character, and who is engaged in the welfare of all creatures? Who is learned and capable. Who alone is ever lovely to behold?</code>	<code>[-0.09693514555692673, 0.4206468462944031, -0.3034357726573944, -1.2955875396728516, 0.3836270868778229, ...]</code>

• Loss: <code>MSELoss</code>

Evaluation Dataset

en-sa

• Dataset: en-sa
• Size: 1,000 evaluation samples
• Columns: <code>non_english</code>, <code>english</code>, and <code>label</code>
• Approximate statistics based on the first 1000 samples:

  	non_english	english	label
  type	string	string	list
  details	<ul><li>min: 4 tokens</li><li>mean: 27.87 tokens</li><li>max: 91 tokens</li></ul>	<ul><li>min: 4 tokens</li><li>mean: 21.35 tokens</li><li>max: 68 tokens</li></ul>	<ul><li>size: 768 elements</li></ul>

• Samples:

  non_english	english	label
  <code>तथा दिमागी रूप से तंदुरुस्त हों ।</code>	<code>And also be mentally fit.</code>	<code>[0.2053176611661911, -0.15136581659317017, -0.1492331326007843, -0.13915303349494934, -0.08056919276714325, ...]</code>
  <code>अपरञ्च युष्माकम् आनन्दो यत् सम्पूर्णो भवेद् तदर्थं वयम् एतानि लिखामः।</code>	<code>"""And these things write we unto you, that your joy may be full."""</code>	<code>[0.0013895286247134209, 0.09506042301654816, -0.3513864576816559, -0.6496815085411072, 0.7649527192115784, ...]</code>
  <code>पञ्च व्यञ्जनानां तेषां च एकम्-एकम स्वास्थ्यसम्बन्धीकार्याणां च सूचीं निर्मातु।</code>	<code>List five spices and one health benefits of each.</code>	<code>[0.37307825684547424, 0.8675527572631836, 0.6388981342315674, -0.27114301919937134, -0.30143851041793823, ...]</code>

• Loss: <code>MSELoss</code>

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• learning_rate: 2e-05
• num_train_epochs: 5
• warmup_ratio: 0.1
• fp16: True

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: 64
• per_device_eval_batch_size: 64
• 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: 2e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 5
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• 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: True
• 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: False
• 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: proportional

</details>

Training Logs

Framework Versions

• Python: 3.10.17
• Sentence Transformers: 4.1.0
• Transformers: 4.46.3
• PyTorch: 2.2.0+cu121
• Accelerate: 1.1.1
• Datasets: 2.18.0
• Tokenizers: 0.20.3

Citation

BibTeX

Sentence Transformers

@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",
}

MSELoss

@inproceedings{reimers-2020-multilingual-sentence-bert,
    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2020",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/2004.09813",
}

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