Instructions to use pradhyra/AWSBlogBert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pradhyra/AWSBlogBert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="pradhyra/AWSBlogBert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("pradhyra/AWSBlogBert") model = AutoModelForMaskedLM.from_pretrained("pradhyra/AWSBlogBert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 789 Bytes
ff8d1ae | 1 2 3 4 5 6 7 8 9 | This model is pre-trained on blog articles from AWS Blogs.
## Pre-training corpora
The input text contains around 3000 blog articles on [AWS Blogs website](https://aws.amazon.com/blogs/) technical subject matter including AWS products, tools and tutorials.
## Pre-training details
I picked a Roberta architecture for masked language modeling (6-layer, 768-hidden, 12-heads, 82M parameters) and its corresponding ByteLevelBPE tokenization strategy. I then followed HuggingFace's Transformers [blog post](https://huggingface.co/blog/how-to-train) to train the model.
I chose to follow the following training set-up: 28k training steps with batches of 64 sequences of length 512 with an initial learning rate 5e-5. The model acheived a training loss of 3.6 on the MLM task over 10 epochs.
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