ikegami-yukino / rakutenma-python

from rakutenma import RakutenMA

# Initialize a RakutenMA instance with an empty model
# the default ja feature set is set already
rma = RakutenMA()

# Let's analyze a sample sentence (from http://tatoeba.org/jpn/sentences/show/103809)
# With a disastrous result, since the model is empty!
print(rma.tokenize("彼は新しい仕事できっと成功するだろう。"))

# Feed the model with ten sample sentences from tatoeba.com
# "tatoeba.json" is available at https://github.com/rakuten-nlp/rakutenma
import json
tatoeba = json.load(open("tatoeba.json"))
for i in tatoeba:
    rma.train_one(i)

# Now what does the result look like?
print(rma.tokenize("彼は新しい仕事できっと成功するだろう。"))

# Initialize a RakutenMA instance with a pre-trained model
rma = RakutenMA(phi=1024, c=0.007812)  # Specify hyperparameter for SCW (for demonstration purpose)
rma.load("model_ja.json")

# Set the feature hash function (15bit)
rma.hash_func = rma.create_hash_func(15)

# Tokenize one sample sentence
print(rma.tokenize("うらにわにはにわにわとりがいる"));

# Re-train the model feeding the right answer (pairs of [token, PoS tag])
res = rma.train_one(
       [["うらにわ","N-nc"],
        ["に","P-k"],
        ["は","P-rj"],
        ["にわ","N-n"],
        ["にわとり","N-nc"],
        ["が","P-k"],
        ["いる","V-c"]])
# The result of train_one contains:
#   sys: the system output (using the current model)
#   ans: answer fed by the user
#   update: whether the model was updated
print(res)

# Now what does the result look like?
print(rma.tokenize("うらにわにはにわにわとりがいる"))