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Hello!
I am using this package to compile reasonable word vectors, but for some short compilations of words, all my words are OOV. I tried using FastText, but I get:
```
*** RuntimeError: Mod…
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For an example like:
```python
import pandas as pd
from fse.models import uSIF
from fse import SplitIndexedList
from gensim.models.keyedvectors import FastTextKeyedVectors
fasttext_model_p…
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已启动生成…
1>------ 已启动生成: 项目: symbol-parser, 配置: Debug x64 ------
2>------ 已启动生成: 项目: kdserial, 配置: Debug x64 ------
3>------ 已启动生成: 项目: hyperdbg-test, 配置: Debug x64 ------
3>Assembling asm-test.asm.…
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I'm trying to test the usif but I'm getting an error in the SVD part about nan values in the vector.
I took the example of the Average and changed to usif
`
from gensim.models import FastText
…
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I got nan values error with the uSIF model
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I noticed that you are calculating sentence embedding using an average of the individual word vectors when performing clustering, etc. Did you happen to evaluate whether SIF or uSIF would be advantag…
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```
2019-10-04 12:19:33,452 : MainThread : INFO : worker thread finished; awaiting finish of 1 more threads
2019-10-04 12:19:33,452 : MainThread : INFO : worker thread finished; awaiting finish of 0…
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It appears that when I download any model from the downloader api in gensim or saved a Word2Vec and re-load it using a KeyedVectors format, the vocab object is storing a reverse index in the "count" v…
ghost updated
4 years ago
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Hello
Model training can done easily. But after save my model i can load it.
```
from fse.models import SIF
model = SIF(w2v, workers=8)
model.train(doc)
model.save("my_sıf_model")
model.loa…
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Hi I see that the train method of an fse object returns the sentence embedding.
Is threre a predict method to apply the trained modeled on new data?
Or train stays for predict?
Best