Technocoloredgeek/midterm-finetuned-embedding

SentenceTransformer based on Snowflake/snowflake-arctic-embed-m

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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Technocoloredgeek/midterm-finetuned-embedding")
# Run inference
sentences = [
    'How does the framework address the concerns related to the rapid innovation and changing definitions of AI systems?',
    'SECTION  TITLE\nApplying The Blueprint for an AI Bill of Rights \nWhile many of the concerns addressed in this framework derive from the use of AI, the technical \ncapabilities and specific definitions of such systems change with the speed of innovation, and the potential \nharms of their use occur even with less technologically sophisticated tools. Thus, this framework uses a two-\npart test to determine what systems are in scope. This framework applies to (1) automated systems that (2)',
    'or inequality. Assessment could include both qualitative and quantitative evaluations of the system. This equity assessment should also be considered a core part of the goals of the consultation conducted as part of the safety and efficacy review.',
]
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	0.9271
cosine_accuracy@3	0.9948
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.9271
cosine_precision@3	0.3316
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.9271
cosine_recall@3	0.9948
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.9693
cosine_mrr@10	0.9588
cosine_map@100	0.9588
dot_accuracy@1	0.9271
dot_accuracy@3	0.9948
dot_accuracy@5	1.0
dot_accuracy@10	1.0
dot_precision@1	0.9271
dot_precision@3	0.3316
dot_precision@5	0.2
dot_precision@10	0.1
dot_recall@1	0.9271
dot_recall@3	0.9948
dot_recall@5	1.0
dot_recall@10	1.0
dot_ndcg@10	0.9693
dot_mrr@10	0.9588
dot_map@100	0.9588

Training Details

Training Dataset

Unnamed Dataset

• Size: 1,539 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: 12 tokens mean: 23.91 tokens max: 46 tokens	min: 3 tokens mean: 84.9 tokens max: 335 tokens

• Samples:

  sentence_0	sentence_1
  What are confabulations in the context of generative AI outputs, and how do they arise from the design of generative models?	Confabulations can occur across GAI outputs and contexts .9,10 Confabulations are a natural result of the way generative models are designed : they generate outputs that approximate the statistical distribution of their training data ; for example, LLMs predict the next token or word in a sentence or phrase . While such statistical prediction can produce factual ly accurate and consistent outputs , it can also produce
  What roles do Rashida Richardson and Karen Kornbluh hold in relation to technology and democracy as mentioned in the context?	products, advanced platforms and services, “Internet of Things” (IoT) devices, and smart city products and services. Welcome : •Rashida Richardson, Senior Policy Advisor for Data and Democracy, White House Office of Science andTechnology Policy •Karen Kornbluh, Senior Fellow and Director of the Digital Innovation and Democracy Initiative, GermanMarshall Fund Moderator :
  What are some best practices that entities should follow to ensure privacy and security in automated systems?	Privacy-preserving security. Entities creating, using, or governing automated systems should follow privacy and security best practices designed to ensure data and metadata do not leak beyond the specific consented use case. Best practices could include using privacy-enhancing cryptography or other types of privacy-enhancing technologies or fine-grained permissions and access control mechanisms, along with conventional system security protocols. 33

• 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: 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
0.6494	50	0.9436
1.0	77	0.9501
1.2987	100	0.9440
1.9481	150	0.9523
2.0	154	0.9488
2.5974	200	0.9549
3.0	231	0.9536
3.2468	250	0.9562
3.8961	300	0.9562
4.0	308	0.9562
4.5455	350	0.9562
5.0	385	0.9588

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.1.1
• Transformers: 4.44.2
• PyTorch: 2.4.1+cu121
• Accelerate: 0.34.2
• Datasets: 3.0.0
• Tokenizers: 0.19.1

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}
}