Comments (4)
Unless I misremember, the keyed vector format relies heavily on Python pickling, so I don't think that something we would want to support.
from finalfusion-rust.
Thanks, it may come useful at some point. I'm trying to polish my rust and nlp skills by creating elixir bindings for finalfusion.
from finalfusion-rust.
Thanks for the answer.
from finalfusion-rust.
You might be able to build a finalfusion model in finalfusion-python by loading the KeyedVector
model via gensim
and constructing the necessary objects. Writing to finalfusion
is supported on the Embeddings
class through its write()
method. You'd then be able to use it in the Rust crate.
Conversion might not be lossless, depending on what kind of "extra" information the gensim model has
from finalfusion-rust.
Related Issues (20)
- Test non-Linux UNIX platforms HOT 1
- NdNorms ChunkIdentifier not in header. HOT 1
- Quantized Array interface HOT 1
- Documentation outdated in 'io.rs'? HOT 1
- Zero-allocation lookups HOT 3
- Analogy interface is too inflexible
- Vocab should be a supertrait of SubwordVocab HOT 1
- Memory mappable vocab HOT 2
- How to use Ngrams to find words not in dictionary HOT 2
- compiling to wasm HOT 2
- Capacity overflow on Windows HOT 3
- Release Explicit Conversions? HOT 3
- Indexing a BucketVocab with the empty String returns Some HOT 1
- Support for phrase embeddings HOT 1
- Relicensing to MIT or Apache License version 2.0 HOT 3
- Reading GoogleNews word2vec model fails HOT 1
- Question: why don't we have support for computing analogy test accuracy for embeddings with quantized storage? HOT 1
- Release 0.11 HOT 25
- Properly handle memory mapped storage on big-endian platforms HOT 5
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from finalfusion-rust.