oscardp96/ medcpt-query:latest

147 11 months ago

MedCPT generates embeddings of biomedical texts that can be used for semantic search (dense retrieval). Compute the embeddings of short texts (e.g., questions, search queries, sentences).

embedding
... /
model
2488ea22d97c · 219MB
    Metadata
  • general.architecture
    bert
  • general.file_type
    F16
  • bert.attention.causal
    false
  • bert.attention.head_count
    12
  • bert.attention.layer_norm_epsilon
    1e-12
  • bert.block_count
    12
  • bert.context_length
    512
  • bert.embedding_length
    768
  • bert.feed_forward_length
    3072
  • tokenizer.ggml.bos_token_id
    2
  • tokenizer.ggml.cls_token_id
    2
  • tokenizer.ggml.eos_token_id
    3
  • tokenizer.ggml.mask_token_id
    4
  • tokenizer.ggml.model
    bert
  • tokenizer.ggml.padding_token_id
    0
  • tokenizer.ggml.scores
    [-1000, -1000, -1000, -1000, -1000, ...]
  • tokenizer.ggml.seperator_token_id
    3
  • tokenizer.ggml.token_type
    [3, 3, 3, 3, 3, ...]
  • tokenizer.ggml.token_type_count
    2
  • tokenizer.ggml.tokens
    [[PAD], [UNK], [CLS], [SEP], [MASK], ...]
  • tokenizer.ggml.unknown_token_id
    1
    • Tensor
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