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yangky11 avatar yangky11 commented on July 24, 2024
  1. In the TermEncoder class, we calculate the node embeddings from bottom to top.
  2. In the GallinaTermParser class, we use lark to parse the S-expressions of Coq terms into ASTs.

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brando90 avatar brando90 commented on July 24, 2024
  1. In the TermEncoder class, we calculate the node embeddings from bottom to top.
  2. In the GallinaTermParser class, we use lark to parse the S-expressions of Coq terms into ASTs.

Thanks! Also, do you embed individual symbols? (e.g. characters?) There are 128 chars so what is the point of embedding those? Asking after reading the paper, will attach relevant parts that are unclear to me:

Screen Shot 2020-12-08 at 8 49 56 AM
Screen Shot 2020-12-08 at 8 51 50 AM

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brando90 avatar brando90 commented on July 24, 2024

so do you embed symbols or use one hot vectors for them is basically my question.

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yangky11 avatar yangky11 commented on July 24, 2024

We use embeddings. See

self.symbol_embeddings = nn.Embedding(len(self.grammar.symbols), opts.symbol_dim)
,

where the embedding dimension is 256:

parser.add_argument('--symbol_dim', type=int, default=256, help='dimension of the terminal/nonterminal symbol embeddings')

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