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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("thomfoolery/AIE5-MidTerm-finetuned-embeddings")
# Run inference
sentences = [
    'How can you access step and property information on the custom configuration page?',
    "Implementing a custom configuration page\n\nTo build a custom configuration page, you'll use Shopify App Bridge to render a page from your app page in Shopify Flow.\n\nFrom the context of the custom configuration page, you can then access step and property information that you can use to display the appropriate information.\n\nYou can also add additional buttons to the App Bridge title bar, or trigger a redirect to the previous page.\n\nUse Shopify App Bridge to render your app page\n\nNote: The specifics of the Custom Configuration Page integration varies between Shopify App Bridge versions. Make sure you implement the integration specific to your Shopify App Bridge version.\n\nTo render your custom configuration page, you need to integrate Shopify App Bridge on the route that you want to render. To learn about setting up Shopify App Bridge, refer to one of the following pages:\n\nGetting started with Shopify App Bridge\n\nGetting started with App Bridge React\n\nAccess action information",
    "To give merchants a more seamless action configuration experience, and to allow them to manage resources that are external to Shopify Flow, you can embed a page from your app in the Shopify Flow editor.\n\nIn your Shopify Flow action configuration, merchants see a preview with an image and text that's fetched from your custom configuration page preview URL. Merchants can click the button to access the custom configuration page.\n\nYour custom configuration page is then displayed in a frame in the Shopify admin.\n\nIn this tutorial, you'll learn how to render a custom configuration page in Shopify Flow, customize the page frame, and access data relevant to your action in the custom configuration page context.\n\nRequirements\n\nYou've created a Partner account.\n\nYou've created an app.\n\nResources\n\nTo implement this feature, you'll use the following:\n\nShopify App Bridge\n\nApp Bridge components\n\nApp Bridge actions specific to the custom configuration page\n\nImplementing a custom configuration page",
]
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	0.5417
cosine_accuracy@3	1.0
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.5417
cosine_precision@3	0.3333
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.5417
cosine_recall@3	1.0
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.8145
cosine_mrr@10	0.75
cosine_map@100	0.75

Training Details

Training Dataset

Unnamed Dataset

Size: 164 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 164 samples:
sentence_0 sentence_1
type string string
details
min: 13 tokens
mean: 19.51 tokens
max: 32 tokens
min: 77 tokens
mean: 198.13 tokens
max: 309 tokens

Samples:

sentence_0	sentence_1
`What is the purpose of templates in Shopify Flow?`	How templates work[object Object][object Object]A template in Shopify Flow is an example workflow that can be copied into a merchant's shop. Templates help merchants automate a specific use case with minimal or no additional configuration. Flow's template library offers hundreds of templates with features to filter, browse, and search. You can create a template for Shopify Flow that showcases your custom triggers and actions and help merchants do more.[object Object][object Object]Next steps[object Object][object Object]Follow our step by step guide on creating a Flow template.[object Object][object Object]Learn more about how localize your template, the approval process, and more in the reference resource
`How can merchants utilize the template library in Shopify Flow?`	How templates work[object Object][object Object]A template in Shopify Flow is an example workflow that can be copied into a merchant's shop. Templates help merchants automate a specific use case with minimal or no additional configuration. Flow's template library offers hundreds of templates with features to filter, browse, and search. You can create a template for Shopify Flow that showcases your custom triggers and actions and help merchants do more.[object Object][object Object]Next steps[object Object][object Object]Follow our step by step guide on creating a Flow template.[object Object][object Object]Learn more about how localize your template, the approval process, and more in the reference resource
`What is required for the test web server to successfully send information to Shopify Flow?`	Requirements[object Object][object Object]Make sure that you have the following:[object Object][object Object]A test web server that you can use to send information to Shopify Flow. You can use an existing web server. This web server needs to be able to send POST requests to Shopify's GraphQL Admin API.[object Object][object Object]A test app that works with the test web server and can send HTTP requests.[object Object][object Object]A development store that has Shopify Flow and the test app installed.[object Object][object Object]Your application has access to the read_customers scope. The trigger you will build using this tutorial will be using a customer reference which requires that scope.[object Object][object Object]Step 1: Create a Flow trigger extension[object Object][object Object]To give your Flow action a meaningful name, use the following guidelines:[object Object][object Object]Use an object acted on + past tense verb format. For example, Auction bid placed.[object Object][object Object]Use sentence case.[object Object][object Object]Don't use punctuation.[object Object][object Object]Separate words using spaces.[object Object][object Object]Using Shopify CLI[object Object][object Object]The following steps show how to create a trigger that sends bid information to Shopify Flow when a bid is placed on an auction.

Loss: MatryoshkaLoss with these parameters:
json
1{ 2 "loss": "MultipleNegativesRankingLoss", 3 "matryoshka_dims": [ 4 768, 5 512, 6 256, 7 128, 8 64 9 ], 10 "matryoshka_weights": [ 11 1, 12 1, 13 1, 14 1, 15 1 16 ], 17 "n_dims_per_step": -1 18}

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	17	0.8353
2.0	34	0.8299
2.9412	50	0.8507
3.0	51	0.8507
4.0	68	0.8353
5.0	85	0.8452
5.8824	100	0.8606
6.0	102	0.8606
7.0	119	0.8299
8.0	136	0.8145
8.8235	150	0.8145
9.0	153	0.8145
10.0	170	0.8145

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

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",
}

MatryoshkaLoss

bibtex

@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

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}
}

	sentence_0	sentence_1
type	string	string
details	min: 13 tokens mean: 19.51 tokens max: 32 tokens	min: 77 tokens mean: 198.13 tokens max: 309 tokens