Please sum up this text in 3 bullet points: Our key contributions are:
1. We extract and clean a natural language corpus built from all the non-default text strings in the ABC dataset [16]. This
includes the names of parts and modeling features along with the name of the document which contained them.
2. We quantitatively evaluate a pre-trained DistilBERT language model on three self-supervised tasks. We demonstrate
that, without fine-tuning, the language model has superior performance over numerous benchmarks including TechNet
[7], which was specifically designed to extract mechanical engineering knowledge from patent data.
3. We show that fine-tuning the language model allows further improvements in performance. The model is able to learn
new relationships between part numbers and other domain-specific vocabulary, providing a pathway to better understand
part assembly relationships by training on larger datasets.
 Write exactly 7 paragraphs each separated with two new lines answering this question. The first paragraph must start with "credit".
user