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
| 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. |