SentenceTransformer based on Snowflake/snowflake-arctic-embed-m-v1.5

This is a sentence-transformers model finetuned from Snowflake/snowflake-arctic-embed-m-v1.5. It maps sentences & paragraphs to a 768-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-m-v1.5
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 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': 768, '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'What is the definition of a preliminary economic assessment in the context of evaluating projects for the recovery of critical raw materials?',
    '(39)\n\n‘preliminary economic assessment’ means an early-stage, conceptual assessment of the potential economic viability of a project for the recovery of critical raw materials from extractive waste;\n\n(40)\n\n‘magnetic resonance imaging device’ means a non-invasive medical device that uses magnetic fields to make anatomical images or any other device that uses magnetic fields to make images of the inside of object;\n\n(41)\n\n‘wind energy generator’ means the part of an onshore or offshore wind turbine that converts the mechanical energy of the rotor into electrical energy;\n\n(42)',
    'For the purposes of the first subparagraph of this paragraph, insurance undertakings referred to in point (a) of the first subparagraph of Article 1(3) of this Directive that are part of a group, on the basis of financial relationships referred to in point (c)(ii) of Article 212(1) of Directive 2009/138/EC, and which are subject to group supervision in accordance with points (a) to (c) of Article 213(2) of that Directive shall be treated as subsidiary undertakings of the parent undertaking of that group.\n\n9.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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	0.8225
cosine_accuracy@3	0.9526
cosine_accuracy@5	0.9725
cosine_accuracy@10	0.9873
cosine_precision@1	0.8225
cosine_precision@3	0.3175
cosine_precision@5	0.1945
cosine_precision@10	0.0987
cosine_recall@1	0.8225
cosine_recall@3	0.9526
cosine_recall@5	0.9725
cosine_recall@10	0.9873
cosine_ndcg@10	0.9141
cosine_mrr@10	0.8896
cosine_map@100	0.8903

Training Details

Training Dataset

Unnamed Dataset

Size: 29,911 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1
type string string
details
min: 13 tokens
mean: 41.63 tokens
max: 252 tokens

min: 4 tokens
mean: 233.72 tokens
max: 512 tokens

	sentence_0	sentence_1
type	string	string
details	min: 13 tokens mean: 41.63 tokens max: 252 tokens	min: 4 tokens mean: 233.72 tokens max: 512 tokens

Samples:

sentence_0	sentence_1
`What measures must Member States take to ensure that workers who believe they have been discriminated against in terms of equal pay can establish their case before a competent authority or national court?`	Article 18 Shift of burden of proof 1. Member States shall take the appropriate measures, in accordance with their national judicial systems, to ensure that, when workers who consider themselves wronged because the principle of equal pay has not been applied to them establish before a competent authority or national court facts from which it may be presumed that there has been direct or indirect discrimination, it shall be for the respondent to prove that there has been no direct or indirect discrimination in relation to pay. 2. Member States shall ensure that, in administrative procedures or court proceedings regarding alleged direct or indirect discrimination in relation to pay, where an employer has not implemented the pay transparency obligations set out in Articles 5, 6, 7, 9 and 10, it is for the employer to prove that there has been no such discrimination. The first subparagraph of this paragraph shall not apply where the employer proves that the infringement of the obligati...
`What are the key considerations for recognizing and addressing discrimination in the context of compensation and penalties, particularly in relation to the gender pay gap?`	discrimination, in particular for substantive and procedural purposes, including to recognise the existence of discrimination, to decide on the appropriate comparator, to assess the proportionality, and to determine, where relevant, the level of compensation awarded or penalties imposed. An intersectional approach is important for understanding and addressing the gender pay gap. This clarification should not change the scope of employers’ obligations in regard to the pay transparency measures under this Directive. In particular, employers should not be required to gather data related to protected grounds other than sex.
`What is the process for aircraft operators and shipping companies regarding the surrendering of allowances in relation to their total emissions from the previous calendar year?`	(b) each aircraft operator surrenders a number of allowances that is equal to its total emissions during the preceding calendar year, as verified in accordance with Article 15; (c) each shipping company surrenders a number of allowances that is equal to its total emissions during the preceding calendar year, as verified in accordance with Article 3ge. Member States, administering Member States and administering authorities in respect of a shipping company shall ensure that allowances surrendered in accordance with the first subparagraph are subsequently cancelled. ▼M15 3-e.

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: 6
per_device_eval_batch_size: 6
num_train_epochs: 4
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: 6
per_device_eval_batch_size: 6
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: 4
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