--- tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:362 - loss:CosineSimilarityLoss base_model: sentence-transformers/all-MiniLM-L6-v2 widget: - source_sentence: What's her experience like? sentences: - What's her experience like? - Alyza and her teammates delivered a 94% improvement in time efficiency. - Alyza developed dashboards for Sell-In and Sell-Out analysis, analyzed and prepared sales data for meetings, collaborated on analyzing promotion-sales correlations, predicted future sales, analyzed stock on hand and offtake data, designed monthly sales plans, updated performance data, managed master data, created promotional cooperation letters, and addressed claim issues. - source_sentence: Good afternoon sentences: - Good afternoon! I'm here to help you learn about Alyza Rahima Pramudya. What would you like to know about her? - It's a predictive model to identify Telco customers likely to churn, helping reduce customer loss. - Can you tell me about the Urban Visual Pollutants Detection project? - source_sentence: What responsibilities did she have at Auto2000? sentences: - Can you name some of her technical projects and applications? - As a Digital Project Consultant, Alyza identified, assessed, developed, tested, and implemented Robotic Process Automation (RPA) using UiPath, designed and developed Power BI dashboards, and developed automation scripts for report generation. - Hello! I'm here to help you learn about Alyza Rahima Pramudya. What would you like to know about her education, work experience, projects, or achievements? - source_sentence: Can you tell me about the news classification project? sentences: - Can you tell me about the news classification project? - Can you describe her duties as a Digital Project Consultant? - Alyza placement at Auto2000 was part of the Astra1st program. - source_sentence: What prestigious programs has Alyza been selected for? sentences: - 'Alyza''s projects include DearCSV, Ask Me Girl!, Prompt & Prejudice, Dog Breed Classifierz, IKN Sentiment App, Frezz : Fruit Freshness Detector, Covid-19 in US: Weather & Socioeconomic Factors, Urban Visual Pollutants Detection, WHO: Life Expectancy Analysis, News Category Classification, Jakarta Air Quality Classification, Diabetes Classification & Regression, and Telco Customer Churn Prediction.' - Alyza is an Astra1st Batch XII Awardee (chosen from over 6,900 applicants, 0.62% acceptance rate) and her team was honored as the Best Team in Astra1st Batch XII. She is also a Mastering AI Batch IV Awardee, receiving a full scholarship for the bootcamp by Skill Academy Pro x Ruangguru Engineering Academy. - Alyza worked as a Digital Project Consultant from June 2024 to November 2024. pipeline_tag: sentence-similarity library_name: sentence-transformers --- # SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 384 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': 256, 'do_lower_case': False, 'architecture': 'BertModel'}) (1): Pooling({'word_embedding_dimension': 384, '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}) (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("sentence_transformers_model_id") # Run inference sentences = [ 'What prestigious programs has Alyza been selected for?', 'Alyza is an Astra1st Batch XII Awardee (chosen from over 6,900 applicants, 0.62% acceptance rate) and her team was honored as the Best Team in Astra1st Batch XII. She is also a Mastering AI Batch IV Awardee, receiving a full scholarship for the bootcamp by Skill Academy Pro x Ruangguru Engineering Academy.', "Alyza's projects include DearCSV, Ask Me Girl!, Prompt & Prejudice, Dog Breed Classifierz, IKN Sentiment App, Frezz : Fruit Freshness Detector, Covid-19 in US: Weather & Socioeconomic Factors, Urban Visual Pollutants Detection, WHO: Life Expectancy Analysis, News Category Classification, Jakarta Air Quality Classification, Diabetes Classification & Regression, and Telco Customer Churn Prediction.", ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities) # tensor([[1.0000, 0.9866, 0.9782], # [0.9866, 1.0000, 0.9715], # [0.9782, 0.9715, 1.0000]]) ``` ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 362 training samples * Columns: sentence_0, sentence_1, and label * Approximate statistics based on the first 362 samples: | | sentence_0 | sentence_1 | label | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence_0 | sentence_1 | label | |:---------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------|:-----------------| | What is alyza's full name? | Alyza's full name is Alyza Rahima Pramudya. | 1.0 | | Can you tell me about Prompt & Prejudice? | Prompt & Prejudice creates dreamy romance ideas based on user inputs or random generation. | 1.0 | | How does the News Category Classification project work? | How does the News Category Classification project work? | 1.0 | * Loss: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `num_train_epochs`: 10 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 8 - `per_device_eval_batch_size`: 8 - `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`: 10 - `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 - `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 - `router_mapping`: {} - `learning_rate_mapping`: {}
### Framework Versions - Python: 3.13.3 - Sentence Transformers: 5.0.0 - Transformers: 4.52.4 - PyTorch: 2.7.1+cpu - Accelerate: 1.8.1 - Datasets: 3.6.0 - Tokenizers: 0.21.2 ## 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", } ```