Instructions to use mnaylor/mega-base-wikitext with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mnaylor/mega-base-wikitext with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mnaylor/mega-base-wikitext")# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("mnaylor/mega-base-wikitext", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
language:
- en
library_name: transformers
---
# Mega Masked LM on wikitext-103
This is the location on the Hugging Face hub for the Mega MLM checkpoint. I trained this model on the `wikitext-103` dataset using standard
BERT-style masked LM pretraining using the [original Mega repository](https://github.com/facebookresearch/mega) and uploaded the weights
initially to hf.co/mnaylor/mega-wikitext-103. When the implementation of Mega into Hugging Face's `transformers` is finished, the weights here
are designed to be used with `MegaForMaskedLM` and are compatible with the other (encoder-based) `MegaFor*` model classes.
This model uses the RoBERTa base tokenizer since the Mega paper does not implement a specific tokenizer aside from the character-level
tokenizer used to illustrate long-sequence performance. |