ric9176/cjo-ft-v0

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
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity

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("ric9176/cjo-ft-v0")
# Run inference
sentences = [
    'What platforms are mentioned in the context for social media engagement?',
    'out for your first newsletter in your inbox soon!instagramtiktokfacebooktwitteryoutubeAbout usPress officeInvestor relationsOur awardsWork for Time OutEditorial guidelinesPrivacy noticeDo not sell my informationCookie policyAccessibility statementTerms of useModern slavery statementManage cookiesContact usGet ListedClaim your listingTime Out Offers FAQAdvertisingTime Out MarketTime Out productsTime Out OffersTime Out WorldwideMoviesRestaurantsSite Map© 2025 Time Out England Limited and affiliated companies owned by Time Out Group Plc. All rights reserved. Time Out is a registered trademark of Time Out Digital Limited.',
    'Steve Beech / ShutterstockPhotograph: Steve Beech / ShutterstockLondon events in March 2025Our guide to the best events, festivals, workshops, exhibitions and things to do throughout March 2025 in LondonWednesday 12 February 2025ShareCopy LinkFacebookTwitterPinterestEmailWhatsAppWritten by Rosie HewitsonThings to Do Editor, LondonAdvertisingThe days are getting gradually lighter, the snowdrops and crocuses have arrived in London’s park, and London’s cultural scene has burst into life after a mid-winter lull. It can only mean one thing; March is right around the corner.',
]
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]

Evaluation

Metrics

Information Retrieval

• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.8846
cosine_accuracy@3	1.0
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.8846
cosine_precision@3	0.3333
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.8846
cosine_recall@3	1.0
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.9574
cosine_mrr@10	0.9423
cosine_map@100	0.9423

Training Details

Training Dataset

Unnamed Dataset

• Size: 154 training samples
• Columns: sentence_0 and sentence_1
• Approximate statistics based on the first 154 samples:

  	sentence_0	sentence_1
  type	string	string
  details	min: 8 tokens mean: 18.04 tokens max: 30 tokens	min: 5 tokens mean: 129.57 tokens max: 226 tokens

• Samples:

  sentence_0	sentence_1
  What types of events and activities are highlighted for the weekend in London?	30 Wonderful Things To Do This Weekend in London – weekend events and activities in LondonGo to the contentGo to the footerNo thanksSubscribe🙌Awesome, you're subscribed!Thanks for subscribing! Look out for your first newsletter in your inbox soon!Get us in your inboxSign up to our newsletter for the latest and greatest from your city and beyondEnter email addressDéjà vu! We already have this email. Try another?By entering your email address you agree to our Terms of Use and Privacy Policy and consent to receive emails from Time Out about news, events, offers and partner promotions.No thanks Awesome, you're subscribed!Thanks for subscribing! Look out for your first newsletter in your inbox soon!The best of London for free.Sign up for
  How can individuals stay updated on the latest happenings in London according to the context?	30 Wonderful Things To Do This Weekend in London – weekend events and activities in LondonGo to the contentGo to the footerNo thanksSubscribe🙌Awesome, you're subscribed!Thanks for subscribing! Look out for your first newsletter in your inbox soon!Get us in your inboxSign up to our newsletter for the latest and greatest from your city and beyondEnter email addressDéjà vu! We already have this email. Try another?By entering your email address you agree to our Terms of Use and Privacy Policy and consent to receive emails from Time Out about news, events, offers and partner promotions.No thanks Awesome, you're subscribed!Thanks for subscribing! Look out for your first newsletter in your inbox soon!The best of London for free.Sign up for
  What benefits do subscribers receive by signing up for the email newsletter?	free.Sign up for our email to enjoy London without spending a thing (as well as some options when you’re feeling flush).Enter email addressDéjà vu! We already have this email. Try another?No thanksBy entering your email address you agree to our Terms of Use and Privacy Policy and consent to receive emails from Time Out about news, events, offers and partner promotions.No thanks Awesome, you're subscribed!Thanks for subscribing! Look out for your first newsletter in your inbox soon!Love the mag?Our newsletter hand-delivers the best bits to your inbox. Sign up to unlock our digital magazines and also receive the latest news, events, offers and partner promotions.Enter email addressDéjà vu! We already have this email. Try another?No

• Loss: MatryoshkaLoss 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

• eval_strategy: steps
• per_device_train_batch_size: 10
• per_device_eval_batch_size: 10
• num_train_epochs: 10
• 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: 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

Training Logs

Epoch	Step	cosine_ndcg@10
1.0	16	0.9213
2.0	32	0.9355
3.0	48	0.9290
3.125	50	0.9432
4.0	64	0.9574
5.0	80	0.9574
6.0	96	0.9574
6.25	100	0.9574
7.0	112	0.9574
8.0	128	0.9574
9.0	144	0.9574
9.375	150	0.9574
10.0	160	0.9574

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}
}