Embedding models on very large sentence level datasets.
embedding
22m
33m
191K Pulls Updated 7 months ago
cb39eaabff82 · 67MB
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bert.attention.causalfalse
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bert.attention.head_count12
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bert.attention.layer_norm_epsilon1e-12
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bert.block_count12
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bert.context_length512
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bert.embedding_length384
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bert.feed_forward_length1536
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bert.pooling_type1
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general.architecturebert
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general.file_type1
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general.nameall-MiniLM-L12-v2
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tokenizer.ggml.bos_token_id101
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tokenizer.ggml.cls_token_id101
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tokenizer.ggml.eos_token_id102
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tokenizer.ggml.mask_token_id103
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tokenizer.ggml.modelbert
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tokenizer.ggml.padding_token_id0
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tokenizer.ggml.scores[-1000, -1000, -1000, -1000, -1000, ...]
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tokenizer.ggml.seperator_token_id102
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tokenizer.ggml.token_type[3, 1, 1, 1, 1, ...]
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tokenizer.ggml.token_type_count2
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tokenizer.ggml.tokens[[PAD], [unused0], [unused1], [unused2], [unused3], ...]
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tokenizer.ggml.unknown_token_id100
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Metadata
Tensor
blk.0
blk.1
blk.2
blk.3
blk.4
blk.5
blk.6
blk.7
blk.8
blk.9
blk.10
blk.11