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								curl -X POST \
-H "Authorization: Bearer YOUR_ORG_OR_USER_API_TOKEN" \
-H "Content-Type: application/json" \
-d '"json encoded string"' \
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pradhyra/AWSBlogBert pradhyra/AWSBlogBert
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Contributed by

pradhyra Pradhyumna Ramesh
1 model

How to use this model directly from the 🤗/transformers library:

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from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("pradhyra/AWSBlogBert") model = AutoModelWithLMHead.from_pretrained("pradhyra/AWSBlogBert")

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 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 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.