--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:8 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: Snowflake/snowflake-arctic-embed-m widget: - source_sentence: How does the Work-Related Stress Scale (WRSS-8) assess the psychological effects of work-induced stress? sentences: - 'The ERI-9 assesses an individual''s ability to regulate emotions under stress. Assessment Questions: I can calm myself down after getting upset. (Scale: 0-3) I tend to overreact to small inconveniences. (Scale: 0-3) I struggle to manage my emotions under pressure. (Scale: 0-3) I practice deep breathing exercises to stay emotionally stable. (Scale: 0-3) Social Confidence Measure (SCM-6) The SCM-6 evaluates levels of confidence in social interactions and public speaking. Assessment Questions: I feel comfortable introducing myself to new people. (Scale: 0-3) I feel anxious in large social gatherings. (Scale: 0-3) I express myself clearly in conversations. (Scale: 0-3) I maintain eye contact while speaking. (Scale: 0-3) Memory Retention Index (MRI-6) The MRI-6 evaluates short-term and long-term memory recall abilities. Assessment Questions: I easily remember names and faces. (Scale: 0-3) I often forget where I placed important items. (Scale: 0-3) I have difficulty recalling specific details from past events. (Scale: 0-3) I use memory techniques to help retain information. (Scale: 0-3)' - 'Linked Psychological & Physical Assessment Pain Coping Strategy Scale (PCSS-9) The PCSS-9 measures how individuals adjust to chronic pain and its impact on their lifestyle, using a structured 9-item scale. Assessment Questions: I change my daily routine to reduce pain impact. (Scale: 0-5) I mentally prepare myself before engaging in painful activities. (Scale: 0-5) I use relaxation techniques to minimize pain perception. (Scale: 0-5) I focus on positive thinking to help manage pain. (Scale: 0-5) Work-Related Stress Scale (WRSS-8) The WRSS-8 evaluates work-induced stress and its psychological effects. Assessment Questions: I feel exhausted after a standard workday. (Scale: 0-3) I struggle to stay motivated due to workplace stress. (Scale: 0-3) I feel overwhelmed when handling multiple responsibilities. (Scale: 0-3) I find it difficult to disconnect from work-related concerns. (Scale: 0-3) Decision-Making Confidence Scale (DMCS-6) The DMCS-6 evaluates confidence in making personal and professional decisions. Assessment Questions: I feel confident when making important decisions. (Scale: 0-3) I second-guess myself often when making choices. (Scale: 0-3) I trust my instincts when faced with uncertainty. (Scale: 0-3)' - 'Linked Psychological & Physical Assessment Pain Coping Strategy Scale (PCSS-9) The PCSS-9 measures how individuals adjust to chronic pain and its impact on their lifestyle, using a structured 9-item scale. Assessment Questions: I change my daily routine to reduce pain impact. (Scale: 0-5) I mentally prepare myself before engaging in painful activities. (Scale: 0-5) I use relaxation techniques to minimize pain perception. (Scale: 0-5) I focus on positive thinking to help manage pain. (Scale: 0-5) Work-Related Stress Scale (WRSS-8) The WRSS-8 evaluates work-induced stress and its psychological effects. Assessment Questions: I feel exhausted after a standard workday. (Scale: 0-3) I struggle to stay motivated due to workplace stress. (Scale: 0-3) I feel overwhelmed when handling multiple responsibilities. (Scale: 0-3) I find it difficult to disconnect from work-related concerns. (Scale: 0-3) Decision-Making Confidence Scale (DMCS-6) The DMCS-6 evaluates confidence in making personal and professional decisions. Assessment Questions: I feel confident when making important decisions. (Scale: 0-3) I second-guess myself often when making choices. (Scale: 0-3) I trust my instincts when faced with uncertainty. (Scale: 0-3)' pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 model-index: - name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-m results: - task: type: information-retrieval name: Information Retrieval dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy@1 value: 1.0 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 1.0 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 1.0 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 1.0 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3333333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.2 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.1 name: Cosine Precision@10 - type: cosine_recall@1 value: 1.0 name: Cosine Recall@1 - type: cosine_recall@3 value: 1.0 name: Cosine Recall@3 - type: cosine_recall@5 value: 1.0 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 1.0 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 1.0 name: Cosine Mrr@10 - type: cosine_map@100 value: 1.0 name: Cosine Map@100 --- # SentenceTransformer based on Snowflake/snowflake-arctic-embed-m This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/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](https://huggingface.co/Snowflake/snowflake-arctic-embed-m) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### 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: ```bash 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("drewgenai/demo-compare-arctic-embed-m-ft") # Run inference sentences = [ 'How does the Work-Related Stress Scale (WRSS-8) assess the psychological effects of work-induced 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] ``` ## Evaluation ### Metrics #### Information Retrieval * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:--------| | cosine_accuracy@1 | 1.0 | | cosine_accuracy@3 | 1.0 | | cosine_accuracy@5 | 1.0 | | cosine_accuracy@10 | 1.0 | | cosine_precision@1 | 1.0 | | cosine_precision@3 | 0.3333 | | cosine_precision@5 | 0.2 | | cosine_precision@10 | 0.1 | | cosine_recall@1 | 1.0 | | cosine_recall@3 | 1.0 | | cosine_recall@5 | 1.0 | | cosine_recall@10 | 1.0 | | **cosine_ndcg@10** | **1.0** | | cosine_mrr@10 | 1.0 | | cosine_map@100 | 1.0 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 8 training samples * Columns: sentence_0 and sentence_1 * Approximate statistics based on the first 8 samples: | | sentence_0 | sentence_1 | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence_0 | sentence_1 | |:-----------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | What does the ERI-9 assess in individuals? | The ERI-9 assesses an individual's ability to regulate emotions under stress.
Assessment Questions:
I can calm myself down after getting upset. (Scale: 0-3)
I tend to overreact to small inconveniences. (Scale: 0-3)
I struggle to manage my emotions under pressure. (Scale: 0-3)
I practice deep breathing exercises to stay emotionally stable. (Scale: 0-3)
Social Confidence Measure (SCM-6)
The SCM-6 evaluates levels of confidence in social interactions and public speaking.
Assessment Questions:
I feel comfortable introducing myself to new people. (Scale: 0-3)
I feel anxious in large social gatherings. (Scale: 0-3)
I express myself clearly in conversations. (Scale: 0-3)
I maintain eye contact while speaking. (Scale: 0-3)
Memory Retention Index (MRI-6)
The MRI-6 evaluates short-term and long-term memory recall abilities.
Assessment Questions:
I easily remember names and faces. (Scale: 0-3)
I often forget where I placed important items. (Scale: 0-3)
I have difficulty recalling specific details... | | How does the SCM-6 measure confidence in social interactions? | The ERI-9 assesses an individual's ability to regulate emotions under stress.
Assessment Questions:
I can calm myself down after getting upset. (Scale: 0-3)
I tend to overreact to small inconveniences. (Scale: 0-3)
I struggle to manage my emotions under pressure. (Scale: 0-3)
I practice deep breathing exercises to stay emotionally stable. (Scale: 0-3)
Social Confidence Measure (SCM-6)
The SCM-6 evaluates levels of confidence in social interactions and public speaking.
Assessment Questions:
I feel comfortable introducing myself to new people. (Scale: 0-3)
I feel anxious in large social gatherings. (Scale: 0-3)
I express myself clearly in conversations. (Scale: 0-3)
I maintain eye contact while speaking. (Scale: 0-3)
Memory Retention Index (MRI-6)
The MRI-6 evaluates short-term and long-term memory recall abilities.
Assessment Questions:
I easily remember names and faces. (Scale: 0-3)
I often forget where I placed important items. (Scale: 0-3)
I have difficulty recalling specific details... | | What does the Pain Coping Strategy Scale (PCSS-9) measure in individuals dealing with chronic pain? | Linked Psychological & Physical Assessment
Pain Coping Strategy Scale (PCSS-9)
The PCSS-9 measures how individuals adjust to chronic pain and its impact on their lifestyle, using
a structured 9-item scale.
Assessment Questions:
I change my daily routine to reduce pain impact. (Scale: 0-5)
I mentally prepare myself before engaging in painful activities. (Scale: 0-5)
I use relaxation techniques to minimize pain perception. (Scale: 0-5)
I focus on positive thinking to help manage pain. (Scale: 0-5)
Work-Related Stress Scale (WRSS-8)
The WRSS-8 evaluates work-induced stress and its psychological effects.
Assessment Questions:
I feel exhausted after a standard workday. (Scale: 0-3)
I struggle to stay motivated due to workplace stress. (Scale: 0-3)
I feel overwhelmed when handling multiple responsibilities. (Scale: 0-3)
I find it difficult to disconnect from work-related concerns. (Scale: 0-3)
Decision-Making Confidence Scale (DMCS-6)
The DMCS-6 evaluates confidence in making personal and pr... | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "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
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`: 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`: 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 | 1 | 1.0 | | 2.0 | 2 | 1.0 | | 3.0 | 3 | 1.0 | | 4.0 | 4 | 1.0 | | 5.0 | 5 | 1.0 | ### Framework Versions - Python: 3.13.2 - 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 ```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} } ```