Model: Galatea007/finetuned_arctic
Author: Galatea007
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:600, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss, arxiv:1908.10084, arxiv:2205.13147, arxiv:1705.00652, base_model:Snowflake/snowflake-arctic-embed-m, base_model:finetune:Snowflake/snowflake-arctic-embed-m, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: Snowflake/snowflake-arctic-embed-m
Card metadata: base_model: Snowflake/snowflake-arctic-embed-m
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, dot_accuracy@1, dot_accuracy@3, dot_accuracy@5, dot_accuracy@10, dot_precision@1, dot_precision@3, dot_precision@5, dot_precision@10, dot_recall@1, dot_recall@3, dot_recall@5, dot_recall@10, dot_ndcg@10, dot_mrr@10, dot_map@100
pipeline_tag: sentence-similarity
tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:600, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss
widget: source_sentence: What types of additional risks might future updates incorporate?
sentences: Inaccuracies in these labels can impact the “stability” or robustness of these benchmarks, which many GAI practitioners consider during the model selection process., For example, when prompted to generate images of CEOs, doctors, lawyers, and judges, current text-to-image models underrepresent women and/or racial minorities , and people with disabilities ., Future updates may incorporate additional risks or provide further details on the risks identiﬁed below., source_sentence: What are some potential consequences of the abuse and misuse of AI systems by humans?
sentences: Even when trained on “clean” data, increasingly capable GAI models can synthesize or produce synthetic NCII and CSAM., 3 the abuse, misuse, and unsafe repurposing by humans (adversarial or not ), and others result from interactions between a human and an AI system., Energy and carbon emissions vary based on what is being done with the GAI model (i.e., pre -training, ﬁne -tuning, inference), the modality of the content , hardware used, and type of task or application ., source_sentence: What types of digital content can be included in GAI?
sentences: Errors in t hird-party GAI components can also have downstream impacts on accuracy and robustness ., In direct prompt injections, attackers might craft malicious prompts and input them directly to a GAI system , with a variety of downstream negative consequences to interconnected systems., This can include images, videos, audio, text, and other digital content.” While not all GAI is derived from foundation models, for purposes of this document, GAI generally refers to generative foundation models ., source_sentence: What are the implications of harmful bias and homogenization in relation to stereotypical content?
sentences: These risks provide a lens through which organizations can frame and execute risk management eﬀorts., 13 • Not every suggested action appl ies to every AI Actor14 or is relevant to every AI Actor Task ., The spread of denigrating or stereotypical content can also further exacerbate representational harms (see Harmful Bias and Homogenization below)., source_sentence: What are the inventory exemptions defined in organizational policies for GAI systems embedded into application software?
sentences: Methods for creating smaller versions of train ed models, such as model distillation or compression, could reduce environmental impacts at inference time, but training and tuning such models may still contribute to their environmental impacts ., For example, predictive inferences made by GAI models based on PII or protected attributes c an contribute to adverse decisions , leading to representational or allocative harms to individuals or groups (see Harmful Bias and Homogenization below)., Information Security GV-1.6-002 Deﬁne any inventory exemptions in organizational policies for GAI systems embedded into application software .
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: 0.9
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.98
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.99
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.9
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.3266666666666667
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.19799999999999998
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09999999999999998
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.9
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.98
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.99
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 1
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.9563669441556807
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.9417619047619047
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.9417619047619047
name: Cosine Map@100
verified: no, type: dot_accuracy@1
value: 0.9
name: Dot Accuracy@1
verified: no, type: dot_accuracy@3
value: 0.98
name: Dot Accuracy@3
verified: no, type: dot_accuracy@5
value: 0.99
name: Dot Accuracy@5
verified: no, type: dot_accuracy@10
value: 1
name: Dot Accuracy@10
verified: no, type: dot_precision@1
value: 0.9
name: Dot Precision@1
verified: no, type: dot_precision@3
value: 0.3266666666666667
name: Dot Precision@3
verified: no, type: dot_precision@5
value: 0.19799999999999998
name: Dot Precision@5
verified: no, type: dot_precision@10
value: 0.09999999999999998
name: Dot Precision@10
verified: no, type: dot_recall@1
value: 0.9
name: Dot Recall@1
verified: no, type: dot_recall@3
value: 0.98
name: Dot Recall@3
verified: no, type: dot_recall@5
value: 0.99
name: Dot Recall@5
verified: no, type: dot_recall@10
value: 1
name: Dot Recall@10
verified: no, type: dot_ndcg@10
value: 0.9563669441556807
name: Dot Ndcg@10
verified: no, type: dot_mrr@10
value: 0.9417619047619047
name: Dot Mrr@10
verified: no, type: dot_map@100
value: 0.9417619047619047
name: Dot Map@100
verified: no
Likes: 0 | All-time downloads: 10
Created: 2024-09-28 17:05:35