| | --- |
| | tags: |
| | - sentence-transformers |
| | - sentence-similarity |
| | - feature-extraction |
| | - generated_from_trainer |
| | - dataset_size:69500 |
| | - loss:Infonce |
| | base_model: Snowflake/snowflake-arctic-embed-l-v2.0 |
| | widget: |
| | - source_sentence: What aspect of human relationship to nature is omitted from the |
| | text |
| | sentences: |
| | - 'There are a few good ones, though. Here are the best WWE apps and WWE games for |
| | Android! The first five are the best games... |
| | |
| | Go Android Apps (blog) |
| | |
| | The Best Themes for Android Free Download: Hi friend we are again back with our |
| | new top ten best free themes for android list. This article is especially dedicated |
| | for those persons who want to make their smartphone... |
| | |
| | Paragon Software has created an app for Android that allows your device to natively |
| | read partitions in file systems that Android normally can''t handle, such as Microsoft''s |
| | NTFS, allowing immediate and easy use of... While the Sentio Desktop app can be |
| | used on its own, it was primarily meant to complement Sentio''s Superbook, a crowdfunded |
| | laptop shell for Android smartphones and tablets that''s just entering production |
| | after... |
| | |
| | ... phone then GBWhatsapp is the app for you. GBWhatsapp is basically similar |
| | to Whatsapp+ in terms of features. The newest available version right now is GBWhatsapp |
| | 6.40 APK for Android devices.' |
| | - A true entertainer. date city state venue 11/23/2012 West Palm Beach FL Kravis |
| | Center 11/24/2012 Sarasota FL Van Wezel Performing Arts Hall 11/25/2012 Clearwater |
| | FL Capitol Theatre 11/29/2012 Durham NC Durham Performing Arts Center 12/1/2012 |
| | Atlantic City NJ Trump Taj Mahal 12/2/2012 Staten Island NY St. George Theatre |
| | 12/4/2012 Bethlehem PA Musikfest Cafe 12/5/2012 Verona NY Turning Stone Casino |
| | 12/6/2012 Stamford CT Palace Theatre Stamford 12/8/2012 Shippensburg PA Luhrs |
| | Center 12/9/2012 Boston MA Wilbur Theatre 12/11/2012 Greensburg PA The Palace |
| | Theatre 12/12/2012 Easton MD Avalon Theatre 12/15/2012 Saint Charles IL Arcada |
| | Theater 12/16/2012 Milwaukee WI Potawatomi Bingo Casino 12/18/2012 Beaver Creek |
| | CO Vilar Performing Arts Center 12/20/2012 Chandler AZ Ovations Live! |
| | - The reader will gain a better understanding of the direction nature and culture |
| | is heading today by learning how connections were made in the past. It omits that |
| | which Raymond Williams called "a working landscape" -- the most intimate human |
| | relationship to nature which is people who live and work on it. |
| | - source_sentence: Why is it recommended to contact a wedding agency or consultant |
| | before making a decision |
| | sentences: |
| | - Perhaps owing to this humiliation I resigned as Chief Winery Warlord, and took |
| | a position elsewhere. Following my resignation, we rebooked our date with axe |
| | throwing destiny, and converted the night from a team building exercise to a majestic |
| | send off in honour of my 10ish glorious years at Coffin Ridge. We arrived in our |
| | most impeccable vestments. |
| | - Therefore, those private companies increased their own rate of cash burn since |
| | the financial markets were willing to fund money-losing enterprises without hesitation. |
| | Out of the 100 largest North American-based technology companies, 16 have lost |
| | money over the past year. |
| | - Yet , it is best to contact a wedding agency or consultant before you make your |
| | concluding decision. This will make certain you are dealing with a respectable |
| | company. |
| | - source_sentence: What is the Electronic Music Education and Preservation Project |
| | (EMEAPP) and what are its functions |
| | sentences: |
| | - The Electronic Music Education and Preservation Project (EMEAPP) is the steward |
| | of a privately held world-class curated collection of rare vintage electronic |
| | instruments and stage-used gear. This includes effects units, amps, organs, synthesizers, |
| | electro-mechanical instruments, guitars, prototypes, vintage audio/video media |
| | and analog studio gear. In addition, EMEAPP itself is cultivating its own humble |
| | collection. It is our charge to cultivate and reap excellent knowledge from these |
| | unique resources and return it to our members and the world. We do this as a learning |
| | center, through research projects, creative endeavors, media programming and tours, |
| | enlightening many people along the way. There is so much to be harvested from |
| | history; EMEAPP has a key to the vault. EMEAPP is a private museum, a critical |
| | learning center and a multi-media production studio nicely packed into a brick-and-mortar |
| | facility outside of Philadelphia, Pennsylvania. EMEAPP is a 501(c)(3) non-profit |
| | organization. |
| | - You got a problem? Yo, she'll splode it. |
| | - I love sex; I think sex is completely absurdly demonized in our culture. But in |
| | the end, however much sex you want to have, with however many people in how many |
| | ways, to be loved and to love is what human beings really want. |
| | - source_sentence: What year did the Duchess die and where did it happen |
| | sentences: |
| | - 'League One |
| | |
| | |
| | League table |
| | |
| | |
| | Results summary |
| | |
| | |
| | Results by matchday |
| | |
| | |
| | Matches |
| | |
| | On 21 June 2018, the League One fixtures for the forthcoming season were announced. |
| | FA Cup |
| | |
| | |
| | The first round draw was made live on BBC by Dennis Wise and Dion Dublin on 22 |
| | October.' |
| | - "The Duchess was widowed in 2007 and died in London in 2011. Issue \n\nThe Duke\ |
| | \ and Duchess of Buccleuch and Queensberry had four children:\nRichard Scott,\ |
| | \ 10th Duke of Buccleuch (b. 1954), married Lady Elizabeth Kerr, daughter of the\ |
| | \ Marquess of Lothian, and has issue two sons and two daughters. Lord John (born\ |
| | \ 9 August 1957), married Berrin Torolsan, and lives in Istanbul, Turkey. Lady\ |
| | \ Charlotte-Anne (born 9 January 1966), married Count Bernard de Castellane in\ |
| | \ 1991, and has issue two sons and a daughter. Lord Damian (born 8 October 1969),\ |
| | \ married Elizabeth Powis, and has issue. External links\nJane in her wedding\ |
| | \ dress \nMovie clip of Jane's wedding\n\nReferences \n\n1929 births\n2011 deaths\n\ |
| | British duchesses by marriage\nJane\nScottish female models\nBritish cookbook\ |
| | \ writers\nWomen cookbook writers" |
| | - Is this common, do other people with epilepsy have dangerously low appetites? |
| | So we left there and stopped and got her a bite to eat. |
| | - source_sentence: Why is it important to keep moving over the summer |
| | sentences: |
| | - It's important to keep moving over the summer! |
| | - '2008. CHENG HF, LEE YM, Chu CH, Leung WK & Mok TMY. - Journal Editor (Hong Kong |
| | Medical Journal) 2008 |
| | |
| | - Editor-in-Chief (Hong Kong Dental Journal) 2007 |
| | |
| | - Editor-in-Chief (Hong Kong Dental Journal) 2006 |
| | |
| | - Deputy Editor (Hong Kong Dental Journal) 2004' |
| | - Both demand collective action and shared resources. While one is distinctly egalitarian |
| | and the other hierarchical in nature, both speak of sublimating private goals |
| | for the achievement of larger, shared ones. |
| | pipeline_tag: sentence-similarity |
| | library_name: sentence-transformers |
| | --- |
| | |
| | # SentenceTransformer based on Snowflake/snowflake-arctic-embed-l-v2.0 |
| |
|
| | This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0). 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-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0) <!-- at revision 7f311bb640ad3babc0a4e3a8873240dcba44c9d2 --> |
| | - **Maximum Sequence Length:** 1024 tokens |
| | - **Output Dimensionality:** 1024 tokens |
| | - **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': 1024, 'do_lower_case': False}) with Transformer model: XLMRobertaModel |
| | (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: |
| |
|
| | ```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("Jrinky/snowflake") |
| | # Run inference |
| | sentences = [ |
| | 'Why is it important to keep moving over the summer', |
| | "It's important to keep moving over the summer!", |
| | '2008. CHENG HF, LEE YM, Chu CH, Leung WK & Mok TMY. - Journal Editor (Hong Kong Medical Journal) 2008\n- Editor-in-Chief (Hong Kong Dental Journal) 2007\n- Editor-in-Chief (Hong Kong Dental Journal) 2006\n- Deputy Editor (Hong Kong Dental Journal) 2004', |
| | ] |
| | 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: 69,500 training samples |
| | * Columns: <code>anchor</code> and <code>positive</code> |
| | * Approximate statistics based on the first 1000 samples: |
| | | | anchor | positive | |
| | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| |
| | | type | string | string | |
| | | details | <ul><li>min: 6 tokens</li><li>mean: 17.47 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 113.33 tokens</li><li>max: 1024 tokens</li></ul> | |
| | * Samples: |
| | | anchor | positive | |
| | |:-----------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | | <code>What might have been unnecessary if better emergency plans had been implemented</code> | <code>If better emergency plans had been in place, maybe chemical dipersants wouldn't be needed. And on and on.</code> | |
| | | <code>What was the year of publication for the 3rd Edition of 'Regular Polytopes' by H.S.M. Coxeter</code> | <code>Coxeter, Regular Polytopes, 3rd Edition, Dover New York, 1973 <br> Kaleidoscopes: Selected Writings of H.S.M. Coxeter, edited by F. Arthur Sherk, Peter McMullen, Anthony C. Thompson, Asia Ivic Weiss, Wiley-Interscience Publication, 1995, <br> (Paper 22) H.S.M.</code> | |
| | | <code>Who is the author of the GURPS Shapeshifters supplement</code> | <code>GURPS Shapeshifters () is a supplement by Robert M. Schroeck for the GURPS role-playing game system, third edition.</code> | |
| | * Loss: <code>selfloss.Infonce</code> with these parameters: |
| | ```json |
| | { |
| | "scale": 20.0, |
| | "similarity_fct": "cos_sim" |
| | } |
| | ``` |
| |
|
| | ### Evaluation Dataset |
| |
|
| | #### Unnamed Dataset |
| |
|
| |
|
| | * Size: 17,376 evaluation samples |
| | * Columns: <code>anchor</code> and <code>positive</code> |
| | * Approximate statistics based on the first 1000 samples: |
| | | | anchor | positive | |
| | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| |
| | | type | string | string | |
| | | details | <ul><li>min: 6 tokens</li><li>mean: 16.87 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 115.36 tokens</li><li>max: 1024 tokens</li></ul> | |
| | * Samples: |
| | | anchor | positive | |
| | |:---------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | | <code>What impressive achievements did the Warriors accomplish during their last season in Division III</code> | <code>The Warriors were among the most lethal offensive teams in Division III this past year, posting a team batting average of .344 and averaging nearly seven runs per game, smacking 29 home runs, and collecting nearly 600 total bases. They shared the Little East Conference regular-season championship and later knocked off the top seed in the NCAA regional tournament (Montclair State) en route to their winningest season in 14 years.</code> | |
| | | <code>How many bars had nectar and capped honey on them</code> | <code>Eight of the bars had nectar and capped honey on them. There are eighteen bars with brood in some form on them and a mix of workers and drones.</code> | |
| | | <code>What idea is being requested regarding the 'triangle'</code> | <code>Next up...the "triangle". Please, seriously, if anyone could float me an idea, I would really appreciate it.</code> | |
| | * Loss: <code>selfloss.Infonce</code> with these parameters: |
| | ```json |
| | { |
| | "scale": 20.0, |
| | "similarity_fct": "cos_sim" |
| | } |
| | ``` |
| |
|
| | ### Training Hyperparameters |
| | #### Non-Default Hyperparameters |
| |
|
| | - `eval_strategy`: steps |
| | - `per_device_train_batch_size`: 3 |
| | - `per_device_eval_batch_size`: 3 |
| | - `learning_rate`: 5e-06 |
| | - `num_train_epochs`: 5 |
| | - `warmup_ratio`: 0.1 |
| | - `fp16`: True |
| | - `batch_sampler`: no_duplicates |
| | |
| | #### 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`: 3 |
| | - `per_device_eval_batch_size`: 3 |
| | - `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-06 |
| | - `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`: True |
| | - `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 |
| | - `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 |
| | - `eval_use_gather_object`: False |
| | - `batch_sampler`: no_duplicates |
| | - `multi_dataset_batch_sampler`: proportional |
| |
|
| | </details> |
| |
|
| | ### Training Logs |
| | | Epoch | Step | Training Loss | Validation Loss | |
| | |:------:|:----:|:-------------:|:---------------:| |
| | | 0.0777 | 150 | 0.0257 | 0.0134 | |
| | | 0.1554 | 300 | 0.0136 | 0.0082 | |
| | | 0.2332 | 450 | 0.0079 | 0.0062 | |
| | | 0.3109 | 600 | 0.0065 | 0.0051 | |
| | | 0.3886 | 750 | 0.0059 | 0.0045 | |
| | | 0.4663 | 900 | 0.0057 | 0.0040 | |
| | | 0.5440 | 1050 | 0.0064 | 0.0037 | |
| | | 0.6218 | 1200 | 0.005 | 0.0034 | |
| | | 0.6995 | 1350 | 0.0052 | 0.0034 | |
| | | 0.7772 | 1500 | 0.0041 | 0.0032 | |
| |
|
| |
|
| | ### Framework Versions |
| | - Python: 3.12.3 |
| | - Sentence Transformers: 3.2.0 |
| | - Transformers: 4.44.2 |
| | - PyTorch: 2.6.0+cu124 |
| | - Accelerate: 1.3.0 |
| | - Datasets: 2.19.0 |
| | - Tokenizers: 0.19.1 |
| |
|
| | ## 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", |
| | } |
| | ``` |
| |
|
| | #### Infonce |
| | ```bibtex |
| | @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} |
| | } |
| | ``` |
| |
|
| | <!-- |
| | ## Glossary |
| |
|
| | *Clearly define terms in order to be accessible across audiences.* |
| | --> |
| |
|
| | <!-- |
| | ## Model Card Authors |
| |
|
| | *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| | --> |
| |
|
| | <!-- |
| | ## Model Card Contact |
| |
|
| | *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
| | --> |
