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 tokens
- 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("CoExperiences/snowflake-l-marketing-tuned")
# Run inference
sentences = [
"How does Alexis Krivkovich's perspective as a mother influence her optimism about the future of women in the workplace?",
'Lucia Rahilly: Sometimes, I feel that we’ve been talking about these issues since I was in college, and that can feel discouraging. What are you most optimistic about going into 2022, coming out of this Women in the Workplace report?\n\nAlexis Krivkovich: I’m most optimistic about the fact that we’re having an honest conversation, and now with a real fact base. We’re not talking about these things as perception but as real and measured experiences that companies can’t hide from—and they don’t want to.\n\nAs a mother of three young daughters, it gives me real hope because I’ve been thinking about this question for 20 years. But in 20 years, when they’re fully in the workplace, maybe we’ll have a totally different paradigm.',
'Learn more about the \nWork Happiness Score at: \ngo.indeed.com/happiness',
]
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 | 0.81 |
cosine_accuracy@3 | 0.93 |
cosine_accuracy@5 | 0.97 |
cosine_accuracy@10 | 0.98 |
cosine_precision@1 | 0.81 |
cosine_precision@3 | 0.31 |
cosine_precision@5 | 0.194 |
cosine_precision@10 | 0.098 |
cosine_recall@1 | 0.81 |
cosine_recall@3 | 0.93 |
cosine_recall@5 | 0.97 |
cosine_recall@10 | 0.98 |
cosine_ndcg@10 | 0.9037 |
cosine_mrr@10 | 0.8781 |
cosine_map@100 | 0.8798 |
dot_accuracy@1 | 0.81 |
dot_accuracy@3 | 0.93 |
dot_accuracy@5 | 0.97 |
dot_accuracy@10 | 0.98 |
dot_precision@1 | 0.81 |
dot_precision@3 | 0.31 |
dot_precision@5 | 0.194 |
dot_precision@10 | 0.098 |
dot_recall@1 | 0.81 |
dot_recall@3 | 0.93 |
dot_recall@5 | 0.97 |
dot_recall@10 | 0.98 |
dot_ndcg@10 | 0.9037 |
dot_mrr@10 | 0.8781 |
dot_map@100 | 0.8798 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 600 training samples
- Columns:
sentence_0
andsentence_1
- Approximate statistics based on the first 600 samples:
sentence_0 sentence_1 type string string details - min: 9 tokens
- mean: 20.08 tokens
- max: 39 tokens
- min: 5 tokens
- mean: 110.85 tokens
- max: 187 tokens
- Samples:
sentence_0 sentence_1 What significant change occurred in employees' perceptions of their employer's care for their wellbeing during the pandemic?
Workplace
Percent Who Feel Employer Cares About Their Wellbeing Plummets
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Workplace
March 18, 2022
Percent Who Feel Employer Cares About Their Wellbeing Plummets
by Jim Harter
Story Highlights
Employees' perceptions of their organization caring about their wellbeing drops
During the onset of the pandemic, employees felt employers had more care and concern
Employees who feel their employer cares about their wellbeing are 69% less likely to actively search for a jobHow does feeling cared for by an employer impact employees' job search behavior?
Workplace
Percent Who Feel Employer Cares About Their Wellbeing Plummets
Share on LinkedIn
Share on Twitter
Share on Facebook
Share via Email
Print
Share on LinkedIn
Share on Twitter
Share on Facebook
Share via Email
Print
Workplace
March 18, 2022
Percent Who Feel Employer Cares About Their Wellbeing Plummets
by Jim Harter
Story Highlights
Employees' perceptions of their organization caring about their wellbeing drops
During the onset of the pandemic, employees felt employers had more care and concern
Employees who feel their employer cares about their wellbeing are 69% less likely to actively search for a jobWhat percentage of U.S. employees feel strongly that their organization cares about their wellbeing?
Fewer than one in four U.S. employees feel strongly that their organization cares about their wellbeing -- the lowest percentage in nearly a decade.
This finding has significant implications, as work and life have never been more blended and employee wellbeing matters more than ever-- to employees and the resiliency of organizations. The discovery is based on a random sample of 15,001 full and part-time U.S. employees who were surveyed in February 2022. - Loss:
MatryoshkaLoss
with these parameters:{ "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: stepsper_device_train_batch_size
: 20per_device_eval_batch_size
: 20num_train_epochs
: 5multi_dataset_batch_sampler
: round_robin
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 20per_device_eval_batch_size
: 20per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1num_train_epochs
: 5max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.0warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseuse_liger_kernel
: Falseeval_use_gather_object
: Falsebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: round_robin
Training Logs
Epoch | Step | cosine_map@100 |
---|---|---|
1.0 | 30 | 0.8782 |
1.6667 | 50 | 0.8878 |
2.0 | 60 | 0.8854 |
3.0 | 90 | 0.8853 |
3.3333 | 100 | 0.8845 |
4.0 | 120 | 0.8793 |
5.0 | 150 | 0.8798 |
Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.2.0
- Transformers: 4.45.2
- PyTorch: 2.5.0+cu124
- Accelerate: 0.34.2
- Datasets: 3.0.1
- Tokenizers: 0.20.1
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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Model tree for CoExperiences/snowflake-l-marketing-tuned
Base model
Snowflake/snowflake-arctic-embed-lSpace using CoExperiences/snowflake-l-marketing-tuned 1
Evaluation results
- Cosine Accuracy@1 on Unknownself-reported0.810
- Cosine Accuracy@3 on Unknownself-reported0.930
- Cosine Accuracy@5 on Unknownself-reported0.970
- Cosine Accuracy@10 on Unknownself-reported0.980
- Cosine Precision@1 on Unknownself-reported0.810
- Cosine Precision@3 on Unknownself-reported0.310
- Cosine Precision@5 on Unknownself-reported0.194
- Cosine Precision@10 on Unknownself-reported0.098
- Cosine Recall@1 on Unknownself-reported0.810
- Cosine Recall@3 on Unknownself-reported0.930