Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

WaveOAK/smollm2-nigerian-cultural-blindspots

Domaine:

natural language processing

Type de record:

dataset
Créateur:
Wav
Hôte:
Project: Blind Spots of Frontier Models (SmolLM2-1.7B) Model Tested SmolLM2-1.7B Loading Code Python from transformers import AutoModelForCausalLM, AutoTokenizer import torch model_id = "HuggingFaceTB/SmolLM2-1.7B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16) Discussion of Errors

Visit

huggingface.co

Tasks

language modeling

Licenses

apache-2.0

Similaires

saaga/yoruba-cultural-reasoning-blindspotszox-BT/gemma-2b-cameroon-cultural-blindspotsSmolLM2 Blind SpotsKibalama/smollm2-finetune-eng-2-lugandakedhamyas/my-blindspots-datasetChiz/omniASR-igbo-blindspots

saaga/yoruba-cultural-reasoning-blindspots

# Yoruba Cultural Reasoning Blind Spots in Frontier Models ## Overview This dataset captures the "b

zox-BT/gemma-2b-cameroon-cultural-blindspots

This dataset highlights the "blind spots" of the Google Gemma-2-2b base model regarding Cameroonian

SmolLM2 Blind Spots

HuggingFaceTB/SmolLM2-1.7B Used HuggingFace Transformers with AutoModelForCausalLM on Google Colab

Kibalama/smollm2-finetune-eng-2-luganda

kedhamyas/my-blindspots-dataset

This dataset was created to probe blind spots in a small, recently released base language model by f

Chiz/omniASR-igbo-blindspots

This dataset investigates three interrelated questions about multilingual ASR performance on tonal l