This model is an export of these
FastText English Vectors
(
wiki-news-300d-1M-subword.vec.zip
) for
staticvectors
.
staticvectors
enables running inference in Python with NumPy. This helps it maintain solid runtime performance.
Usage with StaticVectors
from staticvectors import StaticVectors
model = StaticVectors("neuml/fasttext")
model.embeddings(["word"])
Given that pre-trained embeddings models can get quite large, there is also a SQLite version that lazily loads vectors.
from staticvectors import StaticVectors
model = StaticVectors("neuml/fasttext/model.sqlite")
model.embeddings(["word"])
Runs of NeuML fasttext on huggingface.co
1.1K
Total runs
0
24-hour runs
39
3-day runs
300
7-day runs
-170
30-day runs
More Information About fasttext huggingface.co Model
fasttext huggingface.co is an AI model on huggingface.co that provides fasttext's model effect (), which can be used instantly with this NeuML fasttext model. huggingface.co supports a free trial of the fasttext model, and also provides paid use of the fasttext. Support call fasttext model through api, including Node.js, Python, http.
fasttext huggingface.co is an online trial and call api platform, which integrates fasttext's modeling effects, including api services, and provides a free online trial of fasttext, you can try fasttext online for free by clicking the link below.
fasttext is an open source model from GitHub that offers a free installation service, and any user can find fasttext on GitHub to install. At the same time, huggingface.co provides the effect of fasttext install, users can directly use fasttext installed effect in huggingface.co for debugging and trial. It also supports api for free installation.