--- language: [] library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:2036 - loss:MultipleNegativesRankingLoss base_model: distilbert/distilroberta-base datasets: [] metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max widget: - source_sentence: Proven ability to establish and lead complex projects and programs within a multilayered, hierarchical organization. sentences: - Managed multiple concurrent projects in a large healthcare organization - Assisted in project documentation without direct management responsibilities - Skilled in creating presentations using Microsoft PowerPoint - source_sentence: Experience in evaluating and planning projects to minimize scheduled overtime requirements. sentences: - Validated release packages and coordinated Salesforce release cycles - Oversaw daily housekeeping operations - Successfully managed facility renovation projects to reduce overtime - source_sentence: Candidates should have significant experience in a commercial construction environment, ideally with a minimum of 10 years in the field. sentences: - Built strong partnerships with cross-functional teams to deliver projects - over 12 years of experience managing commercial construction projects - 2 years of experience in residential construction - source_sentence: Possession of strong leadership skills in a Workday professional context. sentences: - 3 years of experience with cardiac mapping technologies - Managed Workday implementation projects and trained team members - Developed marketing strategies for new products - source_sentence: Ability to manage TikTok Shop setup and troubleshoot operational issues effectively. sentences: - Troubleshot various operational issues during the setup of a TikTok Shop - Handled customer support queries for social media platforms - Consistently maintained client trust through transparent communication pipeline_tag: sentence-similarity model-index: - name: SentenceTransformer based on distilbert/distilroberta-base results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev type: sts-dev metrics: - type: pearson_cosine value: 0.7648934072908906 name: Pearson Cosine - type: spearman_cosine value: 0.7804762875391312 name: Spearman Cosine - type: pearson_manhattan value: 0.7679148495261325 name: Pearson Manhattan - type: spearman_manhattan value: 0.76763834201618 name: Spearman Manhattan - type: pearson_euclidean value: 0.7662522690859208 name: Pearson Euclidean - type: spearman_euclidean value: 0.7664213152704937 name: Spearman Euclidean - type: pearson_dot value: 0.45916097674624634 name: Pearson Dot - type: spearman_dot value: 0.4523102899073801 name: Spearman Dot - type: pearson_max value: 0.7679148495261325 name: Pearson Max - type: spearman_max value: 0.7804762875391312 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test type: sts-test metrics: - type: pearson_cosine value: 0.716782585664628 name: Pearson Cosine - type: spearman_cosine value: 0.7026933640919135 name: Spearman Cosine - type: pearson_manhattan value: 0.7172025512970919 name: Pearson Manhattan - type: spearman_manhattan value: 0.6972416685539203 name: Spearman Manhattan - type: pearson_euclidean value: 0.7148825937289236 name: Pearson Euclidean - type: spearman_euclidean value: 0.6948642143635732 name: Spearman Euclidean - type: pearson_dot value: 0.4194725128577338 name: Pearson Dot - type: spearman_dot value: 0.4186318420591598 name: Spearman Dot - type: pearson_max value: 0.7172025512970919 name: Pearson Max - type: spearman_max value: 0.7026933640919135 name: Spearman Max --- # SentenceTransformer based on distilbert/distilroberta-base This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base). 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:** [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **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: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## 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("trbeers/distilroberta-base-nli-v0") # Run inference sentences = [ 'Ability to manage TikTok Shop setup and troubleshoot operational issues effectively.', 'Troubleshot various operational issues during the setup of a TikTok Shop', 'Handled customer support queries for social media platforms', ] 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 #### Semantic Similarity * Dataset: `sts-dev` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.7649 | | **spearman_cosine** | **0.7805** | | pearson_manhattan | 0.7679 | | spearman_manhattan | 0.7676 | | pearson_euclidean | 0.7663 | | spearman_euclidean | 0.7664 | | pearson_dot | 0.4592 | | spearman_dot | 0.4523 | | pearson_max | 0.7679 | | spearman_max | 0.7805 | #### Semantic Similarity * Dataset: `sts-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.7168 | | **spearman_cosine** | **0.7027** | | pearson_manhattan | 0.7172 | | spearman_manhattan | 0.6972 | | pearson_euclidean | 0.7149 | | spearman_euclidean | 0.6949 | | pearson_dot | 0.4195 | | spearman_dot | 0.4186 | | pearson_max | 0.7172 | | spearman_max | 0.7027 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 2,036 training samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:-----------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------| | Sensitivity to the needs of patients, families, and physicians to deliver compassionate care. | worked closely with families to address patient concerns | specialized in technical equipment management without direct patient contact | | Ability to lift 25 lbs. or more as required for handling athletic equipment. | Handled and organized equipment, ensuring safe lifting of heavy items | Coordinated scheduling for team practices and meetings | | The candidate should have significant development experience, preferably around 10 years. | developed and implemented data architecture projects for a decade | worked in customer service for 5 years | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 510 evaluation samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:-----------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:---------------------------------------------------------------| | Qualified to provide personalized and friendly client interactions | Assisted clients with inquiries and ensured a welcoming environment | Conducted market research for product development | | Understanding of network architecture principles and design patterns is critical. | Designed and implemented network architectures for cloud-based solutions | Managed on-premises server infrastructure | | Knowledge of cloud technologies and their implications for customer engagement. | Managed customer onboarding for cloud-based services | Handled sales inquiries for software licenses | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_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`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `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 - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | loss | sts-dev_spearman_cosine | sts-test_spearman_cosine | |:-----:|:----:|:------:|:-----------------------:|:------------------------:| | 0 | 0 | - | 0.6375 | - | | 0.625 | 10 | 2.0178 | 0.7805 | - | | 1.0 | 16 | - | - | 0.7027 | ### Framework Versions - Python: 3.10.11 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.3.1 - Accelerate: 0.31.0 - Datasets: 2.19.1 - 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", } ``` #### 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} } ```