Instructions to use nanom/vizwiz-bert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nanom/vizwiz-bert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nanom/vizwiz-bert-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("nanom/vizwiz-bert-base") model = AutoModelForMaskedLM.from_pretrained("nanom/vizwiz-bert-base") - Notebooks
- Google Colab
- Kaggle
VizWiz-Bert model (uncased)
Fine-tuning the BERT-base model for the Fill Mask task in VizWiz-Vision Skills for VQA
Fine-tuining information
- model: bert-base-uncased
- downstream_tasks: fill-mask
Dataset information
- annotations (CSV files)
- size: ~22K
- max_token_len: 78

Training information
- random_seed: 16
- max_token_len: 78
- train_batch_size: 32
- val_batch_size: 16
- num_epochs: 5,
- learning_rate: 5e-06,
- split_train: 0.8
- optimizer: adamw
Learning curves
- Downloads last month
- 6
