This is a sentence-transformers model finetuned from Snowflake/snowflake-arctic-embed-m. 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
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 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': 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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Mr-Cool/midterm-finetuned-embedding")
# Run inference
sentences = [
    'What processes should be updated for GAI acquisition and procurement vendor assessments?',
    'Inventory all third-party entities with access to organizational content and \\\\\nestablish approved GAI technology and service provider lists. \\\\\n\\end{tabular} & \\begin{tabular}{l}\nValue Chain and Component \\\\\nIntegration \\\\\n\\end{tabular} \\\\\n\\hline\nGV-6.1-008 & \\begin{tabular}{l}\nMaintain records of changes to content made by third parties to promote content \\\\\nprovenance, including sources, timestamps, metadata. \\\\\n\\end{tabular} & \\begin{tabular}{l}\nInformation Integrity; Value Chain \\\\\nand Component Integration; \\\\\nIntellectual Property \\\\\n\\end{tabular} \\\\\n\\hline\nGV-6.1-009 & \\begin{tabular}{l}\nUpdate and integrate due diligence processes for GAI acquisition and \\\\\nprocurement vendor assessments to include intellectual property, data privacy, \\\\\nsecurity, and other risks. For example, update processes to: Address solutions that \\\\\nmay rely on embedded GAI technologies; Address ongoing monitoring, \\\\\nassessments, and alerting, dynamic risk assessments, and real-time reporting \\\\',
    'Evaluation data; Ethical considerations; Legal and regulatory requirements. \\\\\n\\end{tabular} & \\begin{tabular}{l}\nInformation Integrity; Harmful Bias \\\\\nand Homogenization \\\\\n\\end{tabular} \\\\\n\\hline\nAI Actor Tasks: Al Deployment, Al Impact Assessment, Domain Experts, End-Users, Operation and Monitoring, TEVV &  &  \\\\\n\\hline\n\\end{tabular}\n\\end{center}',
]
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	0.8851
cosine_accuracy@3	0.954
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.8851
cosine_precision@3	0.318
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.0246
cosine_recall@3	0.0265
cosine_recall@5	0.0278
cosine_recall@10	0.0278
cosine_ndcg@10	0.2082
cosine_mrr@10	0.928
cosine_map@100	0.0258
dot_accuracy@1	0.8851
dot_accuracy@3	0.954
dot_accuracy@5	1.0
dot_accuracy@10	1.0
dot_precision@1	0.8851
dot_precision@3	0.318
dot_precision@5	0.2
dot_precision@10	0.1
dot_recall@1	0.0246
dot_recall@3	0.0265
dot_recall@5	0.0278
dot_recall@10	0.0278
dot_ndcg@10	0.2082
dot_mrr@10	0.928
dot_map@100	0.0258

Training Details

Training Dataset

Unnamed Dataset

Size: 678 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: 7 tokens
mean: 18.37 tokens
max: 36 tokens
min: 7 tokens
mean: 188.5 tokens
max: 396 tokens

Samples:

sentence_0	sentence_1
`What are the characteristics of trustworthy AI?`	`GOVERN 1.2: The characteristics of trustworthy AI are integrated into organizational policies, processes, procedures, and practices.`
`How are the characteristics of trustworthy AI integrated into organizational policies?`	`GOVERN 1.2: The characteristics of trustworthy AI are integrated into organizational policies, processes, procedures, and practices.`
`Why is it important to integrate trustworthy AI characteristics into organizational processes?`	`GOVERN 1.2: The characteristics of trustworthy AI are integrated into organizational policies, processes, procedures, and practices.`

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: 20
per_device_eval_batch_size: 20
num_train_epochs: 5
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: 20
per_device_eval_batch_size: 20
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: 5
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: 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: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	cosine_map@100
1.0	34	0.0250
1.4706	50	0.0258
2.0	68	0.0257
2.9412	100	0.0258
3.0	102	0.0258
4.0	136	0.0258
4.4118	150	0.0258
5.0	170	0.0258

Framework Versions

Python: 3.12.3
Sentence Transformers: 3.0.1
Transformers: 4.44.2
PyTorch: 2.6.0.dev20240922+cu121
Accelerate: 0.34.2
Datasets: 3.0.0
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",
}

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: 7 tokens mean: 18.37 tokens max: 36 tokens	min: 7 tokens mean: 188.5 tokens max: 396 tokens