Instructions to use lkonle/fiction-gbert-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lkonle/fiction-gbert-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="lkonle/fiction-gbert-large")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("lkonle/fiction-gbert-large") model = AutoModel.from_pretrained("lkonle/fiction-gbert-large") - Notebooks
- Google Colab
- Kaggle
The foundation this model
is the RoBERTa-style model
deepset/gbert-large.
Following Gururangan et al. (2020)
we gathered a collection of narrative fiction and
continued the models pre-training task with it.
The training is performed over 10 epochs on 2.3 GB of
text with a learning rate of 0.0001
(linear decrease) and a batch size of 512.
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