Create README.md
Browse files
README.md
ADDED
|
@@ -0,0 +1,32 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- zh
|
| 4 |
+
---
|
| 5 |
+
# Word2vec
|
| 6 |
+
|
| 7 |
+
## Train script
|
| 8 |
+
|
| 9 |
+
https://github.com/zake7749/word2vec-tutorial
|
| 10 |
+
|
| 11 |
+
## Dataset
|
| 12 |
+
|
| 13 |
+
wiki-zh_tw 2022/12
|
| 14 |
+
|
| 15 |
+
## Use
|
| 16 |
+
|
| 17 |
+
```python
|
| 18 |
+
import gensim
|
| 19 |
+
import jieba
|
| 20 |
+
import numpy as np
|
| 21 |
+
|
| 22 |
+
sentence = "今天天氣真好"
|
| 23 |
+
words = jieba.cut(sentence, cut_all=False)
|
| 24 |
+
|
| 25 |
+
model = gensim.models.KeyedVectors.load(str(Path(path, "model.kv")))
|
| 26 |
+
|
| 27 |
+
word_vec = list()
|
| 28 |
+
for word in words:
|
| 29 |
+
word_vec.append(model.get_vector(word, norm=True))
|
| 30 |
+
|
| 31 |
+
sentence_vec = np.array(word_vec, dtype="float32").mean(0)
|
| 32 |
+
```
|