Model: marroyo777/bge-99GPT-v1-test
Author: marroyo777
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:60341, loss:MultipleNegativesRankingLoss, arxiv:1908.10084, arxiv:1705.00652, base_model:BAAI/bge-small-en-v1.5, base_model:finetune:BAAI/bge-small-en-v1.5, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: BAAI/bge-small-en-v1.5
Card metadata: base_model: BAAI/bge-small-en-v1.5
library_name: sentence-transformers
metrics: cosine_accuracy, dot_accuracy, manhattan_accuracy, euclidean_accuracy, max_accuracy
pipeline_tag: sentence-similarity
tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:60341, loss:MultipleNegativesRankingLoss
widget: source_sentence: What is the focus of the research conducted by the MHCI x 99P Labs Capstone Team?
sentences: To determine the destination of a given car based on an initial start position in time, we developed a Markov Model. We then creatively combined DBScan, K-NN, and XGboost algorithms to achieve accurate dwell time forecasts., Transportation networks touch all three pillars of sustainability. They shape our daily lives by connecting us to work, retail, and recreation; however, a system that does not connect everyone equitably reproduces social disparities., Two weeks of digging deep into exploratory, generative research
Written by the MHCI x 99P Labs Capstone TeamEdited by 99P Labs
The MHCI x 99P Labs Capstone Team is part of the Master of Human-Computer Interaction (MHCI) program at Carnegie Mellon University., source_sentence: What limits are being considered for data quality checks?
sentences: Unlike many other Agile teams, we don t do a Retro every sprint, mostly because we do one-week sprints., Our team has been exploring implementing data quality checks into our data platform. We ve been trying to establish our goals, limits, and expectations, some of which were discussed in Part 1 of our Data Quality blog posts., Literature and Topical ReviewEach team member performed a literature review on telematics research, identifying its applications, methodologies, and critical insights., source_sentence: What are the potential consequences of not researching before coding?
sentences: This indicates a degree of variance in the model s accuracy across different times and conditions., In order to objectively test ourselves on the knowledge we ve gained, we decide to take a quiz. The quiz contains 50 images of either dogs or cats and we have to determine which animal the image most closely resembles., To reiterate, before even writing any code, it s important to do proper research into your team s documentation and online resources. A lot of time can be saved by reusing code that can adapt to your use case instead of starting from scratch every time., source_sentence: What might be the implications of having a performance of 3%?
sentences: Then, I will highlight the top three winning projects from each track., Channels can be used only by organizations that are invited to the channel and are invisible to other members of the network. Each channel has a separate blockchain ledger., 3%, only slightly better than the worst-performing model, K Nearest Neighbors., source_sentence: In what context is traffic flow theory typically discussed?
sentences: As a result, I was familiar with many terms discussed conceptually but I discovered some of the more official terminology used when discussing traffic flow theory and network control., We called it plus-deltas (+/ ). Seeing the output and outcomes we accomplished in each session was encouraging and allowed us to acknowledge things we did that made us successful so we could carry it on to the next session., There are different types of projects within C.
model-index: name: SentenceTransformer based on BAAI/bge-small-en-v1.5
results: task: type: triplet
name: Triplet
dataset: name: 99GPT Finetuning Embedding test 01
type: 99GPT-Finetuning-Embedding-test-01
metrics: type: cosine_accuracy
value: 0.9987405541561712
name: Cosine Accuracy
verified: no, type: dot_accuracy
value: 0.0011931592204693093
name: Dot Accuracy
verified: no, type: manhattan_accuracy
value: 0.9987405541561712
name: Manhattan Accuracy
verified: no, type: euclidean_accuracy
value: 0.9987405541561712
name: Euclidean Accuracy
verified: no, type: max_accuracy
value: 0.9987405541561712
name: Max Accuracy
verified: no
Likes: 0 | All-time downloads: 14
Created: 2024-09-28 03:04:05