drewgenai/midterm-compare-arctic-embed-m-ft

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 <!-- at revision fc74610d18462d218e312aa986ec5c8a75a98152 -->
• —Maximum Sequence Length: 512 tokens
• —Output Dimensionality: 768 dimensions
• —Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

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("drewgenai/midterm-compare-arctic-embed-m-ft")
# Run inference
sentences = [
    'How does the Work-Related Stress Scale (WRSS-8) assess the impact of workplace stress?',
    'Linked Psychological & Physical Assessment\nPain Coping Strategy Scale (PCSS-9)\nThe PCSS-9 measures how individuals adjust to chronic pain and its impact on their lifestyle, using\na structured 9-item scale.\nAssessment Questions:\nI change my daily routine to reduce pain impact. (Scale: 0-5)\nI mentally prepare myself before engaging in painful activities. (Scale: 0-5)\nI use relaxation techniques to minimize pain perception. (Scale: 0-5)\nI focus on positive thinking to help manage pain. (Scale: 0-5)\nWork-Related Stress Scale (WRSS-8)\nThe WRSS-8 evaluates work-induced stress and its psychological effects.\nAssessment Questions:\nI feel exhausted after a standard workday. (Scale: 0-3)\nI struggle to stay motivated due to workplace stress. (Scale: 0-3)\nI feel overwhelmed when handling multiple responsibilities. (Scale: 0-3)\nI find it difficult to disconnect from work-related concerns. (Scale: 0-3)\nDecision-Making Confidence Scale (DMCS-6)\nThe DMCS-6 evaluates confidence in making personal and professional decisions.\nAssessment Questions:\nI feel confident when making important decisions. (Scale: 0-3)\nI second-guess myself often when making choices. (Scale: 0-3)\nI trust my instincts when faced with uncertainty. (Scale: 0-3)',
    'Linked Psychological & Physical Assessment\nPain Coping Strategy Scale (PCSS-9)\nThe PCSS-9 measures how individuals adjust to chronic pain and its impact on their lifestyle, using\na structured 9-item scale.\nAssessment Questions:\nI change my daily routine to reduce pain impact. (Scale: 0-5)\nI mentally prepare myself before engaging in painful activities. (Scale: 0-5)\nI use relaxation techniques to minimize pain perception. (Scale: 0-5)\nI focus on positive thinking to help manage pain. (Scale: 0-5)\nWork-Related Stress Scale (WRSS-8)\nThe WRSS-8 evaluates work-induced stress and its psychological effects.\nAssessment Questions:\nI feel exhausted after a standard workday. (Scale: 0-3)\nI struggle to stay motivated due to workplace stress. (Scale: 0-3)\nI feel overwhelmed when handling multiple responsibilities. (Scale: 0-3)\nI find it difficult to disconnect from work-related concerns. (Scale: 0-3)\nDecision-Making Confidence Scale (DMCS-6)\nThe DMCS-6 evaluates confidence in making personal and professional decisions.\nAssessment Questions:\nI feel confident when making important decisions. (Scale: 0-3)\nI second-guess myself often when making choices. (Scale: 0-3)\nI trust my instincts when faced with uncertainty. (Scale: 0-3)',
]
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]

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Downstream Usage (Sentence Transformers)

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Evaluation

Metrics

Information Retrieval

• —Evaluated with <code>InformationRetrievalEvaluator</code>

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Training Details

Training Dataset

Unnamed Dataset

• —Size: 8 training samples
• —Columns: <code>sentence0</code> and <code>sentence1</code>
• —Approximate statistics based on the first 8 samples: | | sentence0 | sentence1 | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 13 tokens</li><li>mean: 21.25 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 272 tokens</li><li>mean: 283.5 tokens</li><li>max: 296 tokens</li></ul> |
• —Samples: | sentence0 | sentence1 | |:-----------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>What does the ERI-9 assess in individuals?</code> | <code>The ERI-9 assesses an individual's ability to regulate emotions under stress.<br>Assessment Questions:<br>I can calm myself down after getting upset. (Scale: 0-3)<br>I tend to overreact to small inconveniences. (Scale: 0-3)<br>I struggle to manage my emotions under pressure. (Scale: 0-3)<br>I practice deep breathing exercises to stay emotionally stable. (Scale: 0-3)<br>Social Confidence Measure (SCM-6)<br>The SCM-6 evaluates levels of confidence in social interactions and public speaking.<br>Assessment Questions:<br>I feel comfortable introducing myself to new people. (Scale: 0-3)<br>I feel anxious in large social gatherings. (Scale: 0-3)<br>I express myself clearly in conversations. (Scale: 0-3)<br>I maintain eye contact while speaking. (Scale: 0-3)<br>Memory Retention Index (MRI-6)<br>The MRI-6 evaluates short-term and long-term memory recall abilities.<br>Assessment Questions:<br>I easily remember names and faces. (Scale: 0-3)<br>I often forget where I placed important items. (Scale: 0-3)<br>I have difficulty recalling specific details...</code> | | <code>How does the SCM-6 measure confidence in social interactions?</code> | <code>The ERI-9 assesses an individual's ability to regulate emotions under stress.<br>Assessment Questions:<br>I can calm myself down after getting upset. (Scale: 0-3)<br>I tend to overreact to small inconveniences. (Scale: 0-3)<br>I struggle to manage my emotions under pressure. (Scale: 0-3)<br>I practice deep breathing exercises to stay emotionally stable. (Scale: 0-3)<br>Social Confidence Measure (SCM-6)<br>The SCM-6 evaluates levels of confidence in social interactions and public speaking.<br>Assessment Questions:<br>I feel comfortable introducing myself to new people. (Scale: 0-3)<br>I feel anxious in large social gatherings. (Scale: 0-3)<br>I express myself clearly in conversations. (Scale: 0-3)<br>I maintain eye contact while speaking. (Scale: 0-3)<br>Memory Retention Index (MRI-6)<br>The MRI-6 evaluates short-term and long-term memory recall abilities.<br>Assessment Questions:<br>I easily remember names and faces. (Scale: 0-3)<br>I often forget where I placed important items. (Scale: 0-3)<br>I have difficulty recalling specific details...</code> | | <code>What is the purpose of the Pain Coping Strategy Scale (PCSS-9)?</code> | <code>Linked Psychological & Physical Assessment<br>Pain Coping Strategy Scale (PCSS-9)<br>The PCSS-9 measures how individuals adjust to chronic pain and its impact on their lifestyle, using<br>a structured 9-item scale.<br>Assessment Questions:<br>I change my daily routine to reduce pain impact. (Scale: 0-5)<br>I mentally prepare myself before engaging in painful activities. (Scale: 0-5)<br>I use relaxation techniques to minimize pain perception. (Scale: 0-5)<br>I focus on positive thinking to help manage pain. (Scale: 0-5)<br>Work-Related Stress Scale (WRSS-8)<br>The WRSS-8 evaluates work-induced stress and its psychological effects.<br>Assessment Questions:<br>I feel exhausted after a standard workday. (Scale: 0-3)<br>I struggle to stay motivated due to workplace stress. (Scale: 0-3)<br>I feel overwhelmed when handling multiple responsibilities. (Scale: 0-3)<br>I find it difficult to disconnect from work-related concerns. (Scale: 0-3)<br>Decision-Making Confidence Scale (DMCS-6)<br>The DMCS-6 evaluates confidence in making personal and pr...</code> |
• —Loss: <code>MatryoshkaLoss</code> 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: 10
• —per_device_eval_batch_size: 10
• —num_train_epochs: 5
• —multi_dataset_batch_sampler: round_robin

All Hyperparameters

<details><summary>Click to expand</summary>

• —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: 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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —fsdp_transformer_layer_cls_to_wrap: None
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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

</details>

Training Logs

Framework Versions

• —Python: 3.13.1
• —Sentence Transformers: 3.4.1
• —Transformers: 4.49.0
• —PyTorch: 2.6.0+cu124
• —Accelerate: 1.4.0
• —Datasets: 3.3.2
• —Tokenizers: 0.21.0

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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