--- base_model: BAAI/bge-base-en-v1.5 library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:143 - loss:MultipleNegativesRankingLoss widget: - source_sentence: 'JSON APIs: Node.js. An introduction to JSON API, using Node.js.. tags: nodejs, json, API. Languages: Course language: JavaScript. Prerequisites: Prerequisite course required: RESTful APIs: Node.js. Target audience: Professionals who would like to learn the core concepts of JSON API, using Node.js..' sentences: - 'Course Name:Visualizing Data with Matplotlib in Python|Course Description:This course covers the basics of data visualization and exploratory data analysis. It helps students learn different plots and their use cases.|Tags:EDA, matplotlib, visualization, data story telling, analytics, python, static visualization|Course language: Python|Target Audience:Professionals with basic Python experience who would like to expand their skill set to more Python visualization techniques and tools.|Prerequisite course required: Intro to Python' - 'Course Name:Intro to JavaScript Syntax & Basic Constructs|Course Description:A course that builds a foundational understanding of JavaScript basic constructs and syntax, and allows students to have hands-on experience building JavaScript driven websites.|Tags:JS, internet, CSS, web, web page, browser, frontend, HTML, javascript|Course language: HTML, JavaScript|Target Audience:Professionals who would like to learn the basic constructs of JavaScript and be able to create modern JS driven websites.|Prerequisite course required: Intro to CSS, Part 2' - 'Course Name:JSON APIs: Node.js|Course Description:An introduction to JSON API, using Node.js.|Tags:nodejs, json, API|Course language: JavaScript|Target Audience:Professionals who would like to learn the core concepts of JSON API, using Node.js.|Prerequisite course required: RESTful APIs: Node.js' - source_sentence: 'React Ecosystem: State Management & Redux. A course that builds on the React Ecosystem. It explains how state management works in React and goes over the Redux state management library. tags: browser, react, internet, redux. Languages: Course language: JavaScript. Prerequisites: Prerequisite course required: React Ecosystem: Forms. Target audience: Professionals who would like to learn about state management in React.' sentences: - 'Course Name:Probability Distributions in Python|Course Description:This course is designed for learners who would like to learn about statistics and apply it for decision-making. This course is a comprehensive review of advanced statistics topics on probability distributions like binomial, mulitnomial, hypergeometric, poisson, exponential and uniform distributions enabling us to obtain estimates of the probability that a certain event may occur, or estimate the variability of occurrence.|Tags:binomial, hypergeometric, uniform, multinomial, distribution, poisson, exponential|Course language: Python|Target Audience:Professionals some Python experience who would like to expand their skill set to more advanced Python visualization techniques and tools.|Prerequisite course required: Foundations of Statistics in Python' - 'Course Name:Applications of AI for Anomaly Detection|Course Description:Learn to detect anomalies in large data sets to identify network intrusions using supervised and unsupervised machine learning techniques, such as accelerated XGBoost, autoencoders, and generative adversarial networks (GANs).|Tags:Anomaly Detection, autoencoder, GPU acceleration, XGBoost, GANs|Course language: Python|Target Audience:Professionals with knowledge of neural networks and wants to create AI models that can be trained and deployed to automatically analyze datasets, define “normal behavior,” and\nidentify breaches in patterns quickly and effectively.|No prerequisite course required' - 'Course Name:React Ecosystem: State Management & Redux|Course Description:A course that builds on the React Ecosystem. It explains how state management works in React and goes over the Redux state management library|Tags:browser, react, internet, redux|Course language: JavaScript|Target Audience:Professionals who would like to learn about state management in React|Prerequisite course required: React Ecosystem: Forms' - source_sentence: 'Ensemble Methods in Python. This course covers an overview of ensemble learning methods like random forest and boosting. At the end of this course, students will be able to implement and compare random forest algorithm and boosting.. tags: ensemble methods, classification, random forest, gbm, boosting, python, gradient boosting machines, sckit-learn. Languages: Course language: Python. Prerequisites: Prerequisite course required: Decision Trees. Target audience: Professionals with some experience in building basic algorithms who would like to expand their skill set to more advanced Python classification techniques..' sentences: - 'Course Name:k-Nearest Neighbors in R|Course Description:Classification algorithms are powerful and intuitive data science tools that can predict behaviors and trends. This course walks students through how to implement algorithms like k-Nearest Neighbors, as well as evaluate and interpret their results. By the end of the course, students will be able to build classification models to anticipate events and assess the accuracy of predictive algorithms.|Tags:R, classification, analytics, machine learning, kNN, supervised learning|Course language: R|Target Audience:Professionals with some R experience who would like to expand their skillset to learn what supervised learning techniques are and classification techniques in particular. Analysts with experience in another similar programming language who would like to the above techniques and frameworks in R.|Prerequisite course required: Data Wrangling in R' - 'Course Name:Ensemble Methods in Python|Course Description:This course covers an overview of ensemble learning methods like random forest and boosting. At the end of this course, students will be able to implement and compare random forest algorithm and boosting.|Tags:ensemble methods, classification, random forest, gbm, boosting, python, gradient boosting machines, sckit-learn|Course language: Python|Target Audience:Professionals with some experience in building basic algorithms who would like to expand their skill set to more advanced Python classification techniques.|Prerequisite course required: Decision Trees' - 'Course Name:Simple Linear Regression|Course Description:This course covers a supervised regression technique called Simple Linear which is used to model a relationship between a single feature and a continuous target variable. Students will learn how this relationship is then used to predict changes in the target variable. The course includes the background, how to build, evaluate and interpret these Simple Linear regression models.|Tags:regression, simplelinear|Course language: Python|Target Audience:This is an introductory level course for data scientists who want to learn to understand and estimate relationships between a independent variable and a continuous dependent variable.|Prerequisite course required: Data Wrangling in Python' - source_sentence: 'Enzyme. A course that explores Enzyme, which is a JavaScript utility for React applications. The course equips users to simulate runs and test React components'' outputs.. tags: react, enzyme, react components, javascript, tests. Languages: Course language: TBD. Prerequisites: Prerequisite course required: React Testing Library. Target audience: For anyone who has built an application in React and wants to test the React components.' sentences: - 'Course Name:Authentication Python|Course Description:An introduction to Authentication concepts and how it can be implemented using Python.|Tags:tokens, authentication|Course language: Python|Target Audience:Professionals who would like to learn the core concepts of authentication using Python.|Prerequisite course required: Basic GraphQL: Python' - 'Course Name:Advanced Clustering in R|Course Description:This course covers the unsupervised learning method called clustering which is used to find patterns or groups in data without the need for labelled data. This course includes application of different methods of clustering on categorical or mixed data, equipping learners to build, evaluate, and interpret these models.|Tags:MeanShift, R, unsupervised learning, analytics, machine learning, K-Modes, clustering, K-Prototypes|Course language: R|Target Audience:Professionals with some R experience who would like to expand their skillset to learn the core unsupervised learning techniques. Analysts with experience in another similar programming language who would like to learn core unsupervised learning frameworks and packages in R.|Prerequisite course required: Spherical k-Means and Hierarchical Clustering in R' - 'Course Name:Enzyme|Course Description:A course that explores Enzyme, which is a JavaScript utility for React applications. The course equips users to simulate runs and test React components'' outputs.|Tags:react, enzyme, react components, javascript, tests|Course language: TBD|Target Audience:For anyone who has built an application in React and wants to test the React components|Prerequisite course required: React Testing Library' - source_sentence: 'React Ecosystem: Styling. A course that expands on your react knowledge to make your own styled components and leverage material UI library. tags: styling, react, internet, MUI, browser. Languages: Course language: TBD. Prerequisites: Prerequisite course required: React Ecosystem: State Management & Redux. Target audience: Professionals who would like to explore the world of browsers, domains, and websites..' sentences: - 'Course Name:Control Flow and Functions in Python|Course Description:This course will unravel different functionalities that Python offers. The students will be able to write modular code, conditional statements, loops, function definitions and list comprehensions.|Tags:functions, control-flow|Course language: Python|Target Audience:This is an intermediate level course of Python for aspiring data scientists who want to learn to implement control flow structures in Python.|Prerequisite course required: Intro to Python' - 'Course Name:React Ecosystem: Styling|Course Description:A course that expands on your react knowledge to make your own styled components and leverage material UI library|Tags:styling, react, internet, MUI, browser|Course language: TBD|Target Audience:Professionals who would like to explore the world of browsers, domains, and websites.|Prerequisite course required: React Ecosystem: State Management & Redux' - 'Course Name:Spark Data Structures & Parallelism|Course Description:A 4-hour course for intermediate-level data scientists / engineers that covers Spark architecture and fundamentals including RDDs, DataFrames, Datasets.|Tags:spark, DataFrame, Spark UI, parallel processing, Dataset, RDDs|Course language: Scala|Target Audience:Students with basic knowledge of scala and want to expand their knowledge on scala and spark|Prerequisite course required: Intro to Scala Collections' --- # SentenceTransformer based on BAAI/bge-base-en-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) - **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': True}) 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("datasocietyco/bge-base-en-v1.5-course-recommender-v4") # Run inference sentences = [ 'React Ecosystem: Styling. A course that expands on your react knowledge to make your own styled components and leverage material UI library. tags: styling, react, internet, MUI, browser. Languages: Course language: TBD. Prerequisites: Prerequisite course required: React Ecosystem: State Management & Redux. Target audience: Professionals who would like to explore the world of browsers, domains, and websites..', 'Course Name:React Ecosystem: Styling|Course Description:A course that expands on your react knowledge to make your own styled components and leverage material UI library|Tags:styling, react, internet, MUI, browser|Course language: TBD|Target Audience:Professionals who would like to explore the world of browsers, domains, and websites.|Prerequisite course required: React Ecosystem: State Management & Redux', 'Course Name:Spark Data Structures & Parallelism|Course Description:A 4-hour course for intermediate-level data scientists / engineers that covers Spark architecture and fundamentals including RDDs, DataFrames, Datasets.|Tags:spark, DataFrame, Spark UI, parallel processing, Dataset, RDDs|Course language: Scala|Target Audience:Students with basic knowledge of scala and want to expand their knowledge on scala and spark|Prerequisite course required: Intro to Scala Collections', ] 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] ``` ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 143 training samples * Columns: anchor and positive * Approximate statistics based on the first 143 samples: | | anchor | positive | |:--------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Reinforcement Learning. This course covers the specialized branch of machine learning and deep learning called reinforcement learning (RL). By the end of this course students will be able to define RL use cases and real world scenarios where RL models are used, they will be able to create a simple RL model and evaluate its performance.. tags: deep learning, Keras, reinforcement learning, neural networks, TensorFlow. Languages: Course language: Python. Prerequisites: Prerequisite course required: Working with Complex Pre-trained CNNs in Python. Target audience: Professionals some Python experience who would like to expand their skillset to more advanced machine learning algorithms for reinforcement learning.. | Course Name:Reinforcement Learning|Course Description:This course covers the specialized branch of machine learning and deep learning called reinforcement learning (RL). By the end of this course students will be able to define RL use cases and real world scenarios where RL models are used, they will be able to create a simple RL model and evaluate its performance.|Tags:deep learning, Keras, reinforcement learning, neural networks, TensorFlow|Course language: Python|Target Audience:Professionals some Python experience who would like to expand their skillset to more advanced machine learning algorithms for reinforcement learning.|Prerequisite course required: Working with Complex Pre-trained CNNs in Python | | Optimizing Ensemble Methods in Python. This course covers advanced topics in optimizing ensemble learning methods – specifically random forest and gradient boosting. Students will learn to implement base models and perform hyperparameter tuning to enhance the performance of models.. tags: ensemble methods, classification, random forest, hyperparameter tuning, gbm, boosting, python, gradient boosting machines. Languages: Course language: Python. Prerequisites: Prerequisite course required: Ensemble Methods in Python. Target audience: Professionals experience in ensemble methods and who want to enhance their skill set in advanced Python classification techniques.. | Course Name:Optimizing Ensemble Methods in Python|Course Description:This course covers advanced topics in optimizing ensemble learning methods – specifically random forest and gradient boosting. Students will learn to implement base models and perform hyperparameter tuning to enhance the performance of models.|Tags:ensemble methods, classification, random forest, hyperparameter tuning, gbm, boosting, python, gradient boosting machines|Course language: Python|Target Audience:Professionals experience in ensemble methods and who want to enhance their skill set in advanced Python classification techniques.|Prerequisite course required: Ensemble Methods in Python | | Fundamentals of Accelerated Computing with OpenACC. Find out how to write and configure code parallelization with OpenACC, optimize memory movements between the CPU and GPU accelerator, and apply the techniques to accelerate a CPU-only Laplace Heat Equation to achieve performance gains.. tags: NVIDIA Nsight, OpenACC. Languages: Course language: Python. Prerequisites: No prerequisite course required. Target audience: Professionals who want to learn how to write code, configure code parallelization with OpenACC, optimize memory movements between the CPU and GPU accelerator, and implement the workflow learnt for massive performance gains.. | Course Name:Fundamentals of Accelerated Computing with OpenACC|Course Description:Find out how to write and configure code parallelization with OpenACC, optimize memory movements between the CPU and GPU accelerator, and apply the techniques to accelerate a CPU-only Laplace Heat Equation to achieve performance gains.|Tags:NVIDIA Nsight, OpenACC|Course language: Python|Target Audience:Professionals who want to learn how to write code, configure code parallelization with OpenACC, optimize memory movements between the CPU and GPU accelerator, and implement the workflow learnt for massive performance gains.|No prerequisite course required | * 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: 36 evaluation samples * Columns: anchor and positive * Approximate statistics based on the first 36 samples: | | anchor | positive | |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Intro to CSS, Part 2. A course that continues to build on the foundational understanding of CSS syntax and allows students to work with responsive design and media queries.. tags: styling, internet, CSS, web, web page, browser, HTML. Languages: Course language: CSS, HTML. Prerequisites: Prerequisite course required: Intro to CSS, Part 1. Target audience: Professionals who would like to continue learning the core concepts of CSS and be able to style simple web pages.. | Course Name:Intro to CSS, Part 2|Course Description:A course that continues to build on the foundational understanding of CSS syntax and allows students to work with responsive design and media queries.|Tags:styling, internet, CSS, web, web page, browser, HTML|Course language: CSS, HTML|Target Audience:Professionals who would like to continue learning the core concepts of CSS and be able to style simple web pages.|Prerequisite course required: Intro to CSS, Part 1 | | Foundations of Statistics in Python. This course is designed for learners who would like to learn about statistics and apply it for decision-making. This course is a comprehensive review of statistical terms ranging from foundational (mean, median, mode, standard deviation, variance, covariance, correlation) to more complex concepts such as normality in data, confidence intervals, and p-values. Additional topics include how to calculate summary statistics and how to carry out hypothesis testing to inform decisions.. tags: two-tailed test, statistics, sampling, hypothesis testing, confidence intervals, one-tailed test, central limit theorem. Languages: Course language: Python. Prerequisites: Prerequisite course required: Intro to Visualization in Python. Target audience: Professionals some Python experience who would like to expand their skill set to more advanced Python visualization techniques and tools.. | Course Name:Foundations of Statistics in Python|Course Description:This course is designed for learners who would like to learn about statistics and apply it for decision-making. This course is a comprehensive review of statistical terms ranging from foundational (mean, median, mode, standard deviation, variance, covariance, correlation) to more complex concepts such as normality in data, confidence intervals, and p-values. Additional topics include how to calculate summary statistics and how to carry out hypothesis testing to inform decisions.|Tags:two-tailed test, statistics, sampling, hypothesis testing, confidence intervals, one-tailed test, central limit theorem|Course language: Python|Target Audience:Professionals some Python experience who would like to expand their skill set to more advanced Python visualization techniques and tools.|Prerequisite course required: Intro to Visualization in Python | | Spherical k-Means and Hierarchical Clustering in R. This course covers the unsupervised learning method called clustering which is used to find patterns or groups in data without the need for labelled data. This course includes different methods of clustering on numerical data including density-based and hierarchical-based clustering and how to build, evaluate and interpret these models.. tags: DBSCAN, R, unsupervised learning, analytics, machine learning, hierarchical, clustering. Languages: Course language: R. Prerequisites: Prerequisite course required: Intro to Clustering in R. Target audience: Professionals with some R experience who would like to expand their skillset to more clustering techniques like hierarchical clustering and DBSCAN.. | Course Name:Spherical k-Means and Hierarchical Clustering in R|Course Description:This course covers the unsupervised learning method called clustering which is used to find patterns or groups in data without the need for labelled data. This course includes different methods of clustering on numerical data including density-based and hierarchical-based clustering and how to build, evaluate and interpret these models.|Tags:DBSCAN, R, unsupervised learning, analytics, machine learning, hierarchical, clustering|Course language: R|Target Audience:Professionals with some R experience who would like to expand their skillset to more clustering techniques like hierarchical clustering and DBSCAN.|Prerequisite course required: Intro to Clustering in R | * 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`: 16 - `per_device_eval_batch_size`: 16 - `learning_rate`: 3e-06 - `max_steps`: 64 - `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`: 16 - `per_device_eval_batch_size`: 16 - `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`: 3e-06 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 3.0 - `max_steps`: 64 - `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 - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | loss | |:------:|:----:|:-------------:|:------:| | 2.2222 | 20 | 0.0598 | 0.0318 | | 4.4444 | 40 | 0.0167 | 0.0250 | | 6.6667 | 60 | 0.0109 | 0.0233 | ### Framework Versions - Python: 3.9.13 - Sentence Transformers: 3.1.1 - Transformers: 4.45.1 - PyTorch: 2.2.2 - Accelerate: 0.34.2 - Datasets: 3.0.0 - Tokenizers: 0.20.0 ## 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", } ``` #### 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} } ```