travel_advisory-ft

SentenceTransformer based on Snowflake/snowflake-arctic-embed-l

This is a sentence-transformers model finetuned from Snowflake/snowflake-arctic-embed-l. It maps sentences & paragraphs to a 1024-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: Snowflake/snowflake-arctic-embed-l <!-- at revision d8fb21ca8d905d2832ee8b96c894d3298964346b -->
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity

<!-- - **Training Dataset:** Unknown --> <!-- - **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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

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("bharath25911/travel_advisory-ft")
# Run inference
sentences = [
    'What are the recommended methods for cleaning your hands to reduce the spread of infectious diseases?',
    'cleaning your hands regularly with soap and water for at least 20 seconds, or using hand sanitizer containing at least 60% alcohol\ncovering your coughs and sneezes with a tissue or your elbow, not your hands\n\nMass gatherings (large-scale events)Clean your hands to help reduce the spread of infectious diseasesRespiratory infectious diseases: How to reduce the spread with personal protective measures\nTo lower your risk of getting sexually transmissible infections (STIs), HIV, and mpox, you can:\n\nget vaccinated against mpox if you’re eligible\nhave fewer sexual partners\nuse protection during sexual activity, like condoms and dental dams.',
    'To reduce your risk of getting sick, avoid contact with animals like stray dogs, livestock (such as pigs and cows), monkeys, snakes, rodents, birds, and bats. Avoid eating undercooked meat.\nMake sure to closely watch children; they are more likely to try to touch animals.Person-to-person infections\nWhen travelling, you can reduce your risk of getting or spreading respiratory infections, like the flu or COVID-19, by:\n\nstaying at your accommodation and limiting contact with others if you’re sick\nwearing a well-fitting mask, especially:\xa0\n\nif you’re sick and need to be around others\xa0\nwhen you’re at large indoor events or in crowded settings, like sporting events, concerts, and airports',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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

Unnamed Dataset

• Size: 2,636 training samples
• Columns: <code>sentence_0</code> and <code>sentence_1</code>
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1
  type	string	string
  details	<ul><li>min: 9 tokens</li><li>mean: 17.36 tokens</li><li>max: 40 tokens</li></ul>	<ul><li>min: 4 tokens</li><li>mean: 99.16 tokens</li><li>max: 254 tokens</li></ul>

• Samples:

  sentence_0	sentence_1
  <code>What specific travel advisories are currently in place for Sierra Leone?</code>	<code>Travel advice and advisories for Sierra Leone<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>Skip to main content<br><br><br>Skip to "About this site"<br><br><br><br><br><br>Language selection<br><br>Français<br><br><br><br><br> Government of Canada / Gouvernement du Canada<br><br><br>Search Travel.gc.ca<br><br><br>Search website<br><br><br><br><br><br><br><br><br><br><br>Search<br><br><br><br><br><br><br><br>Menu<br>Main Menu <br><br>Jobs and the workplace<br>Immigration and citizenship<br>Travel and tourism<br>Business and industry<br>Benefits<br>Health<br>Taxes<br>Environment and natural resources<br>National security and defence<br>Culture, history and sport<br>Policing, justice and emergencies<br>Transport and infrastructure<br>Canada and the world<br>Money and finances<br>Science and innovation<br><br><br><br><br>You are here:<br><br><br>Canada.ca<br>Travel<br>Destinations<br><br><br><br><br><br><br><br><br><br><br><br><br><br> Sierra Leone travel advice</code>
  <code>Where can travelers find more information about travel advice for Sierra Leone?</code>	<code>Travel advice and advisories for Sierra Leone<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>Skip to main content<br><br><br>Skip to "About this site"<br><br><br><br><br><br>Language selection<br><br>Français<br><br><br><br><br> Government of Canada / Gouvernement du Canada<br><br><br>Search Travel.gc.ca<br><br><br>Search website<br><br><br><br><br><br><br><br><br><br><br>Search<br><br><br><br><br><br><br><br>Menu<br>Main Menu <br><br>Jobs and the workplace<br>Immigration and citizenship<br>Travel and tourism<br>Business and industry<br>Benefits<br>Health<br>Taxes<br>Environment and natural resources<br>National security and defence<br>Culture, history and sport<br>Policing, justice and emergencies<br>Transport and infrastructure<br>Canada and the world<br>Money and finances<br>Science and innovation<br><br><br><br><br>You are here:<br><br><br>Canada.ca<br>Travel<br>Destinations<br><br><br><br><br><br><br><br><br><br><br><br><br><br> Sierra Leone travel advice</code>
  <code>What is the recommended level of caution for travelers in Sierra Leone?</code>	<code>Exercise a high degree of caution<br><br><br><br><br>Latest updates: Editorial change<br>Last updated: February 18, 2025 15:41 ET<br><br><br><br><br><br><br>On this page<br><br>Risk level<br>Safety and security<br>Entry and exit requirements<br>Health<br>Laws and culture<br>Natural disasters and climate<br><br>Need help?<br><br><br><br><br>Risk level<br><br><br>Sierra Leone - Exercise a high degree of cautionExercise a high degree of caution in Sierra Leone due to crime.<br><br><br><br> Back to top</code>

• Loss: <code>MatryoshkaLoss</code> with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 10
• per_device_eval_batch_size: 10
• num_train_epochs: 10
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

<details><summary>Click to expand</summary>

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: no
• prediction_loss_only: True
• per_device_train_batch_size: 10
• per_device_eval_batch_size: 10
• 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: 10
• 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

Framework Versions

• Python: 3.11.11
• Sentence Transformers: 3.4.1
• Transformers: 4.48.3
• PyTorch: 2.5.1+cu124
• Accelerate: 1.3.0
• Datasets: 3.3.2
• Tokenizers: 0.21.0

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",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

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