452662 trigram

run.py

@@ -72,31 +72,13 @@ for i in range(len(expected)):
 # In[9]:
 
 
-from collections import defaultdict
-from nltk import ngrams
-from nltk.tokenize import word_tokenize
-
-model = defaultdict(lambda: defaultdict(float))
-dictionary = set()
-for line in corpus[:100000]:
-            tokens = word_tokenize(line)
-            for word1, word2, word3, word4 in ngrams(tokens, n=4, pad_right=True, pad_left=True):
-                if word1 and word2 and word3 and word4:
-                    model[(word2, word3, word4)][word1] += 1
-                    model[(word1, word2, word3)][word4] += 1
-                    dictionary.update([word1, word2, word3, word4])
-
-
-# In[15]:
-
-
 from collections import defaultdict
 from nltk import trigrams
 from nltk.tokenize import word_tokenize
 
 model_trigram = defaultdict(lambda: defaultdict(float))
 dictionary_trigram = set()
-for line in corpus[:100000]:
+for line in corpus[:200000]:
             tokens = word_tokenize(line)
             for word1, word2, word3 in trigrams(tokens, pad_right=True, pad_left=True):
                 if word1 and word2 and word3:
@@ -105,7 +87,7 @@ for line in corpus[:100000]:
                     dictionary_trigram.update([word1, word2, word3])
 
 
-# In[18]:
+# In[10]:
 
 
 from collections import defaultdict
@@ -114,7 +96,7 @@ from nltk.tokenize import word_tokenize
 
 model_bigram = defaultdict(lambda: defaultdict(float))
 dictionary_bigram = set()
-for line in corpus[:100000]:
+for line in corpus[:200000]:
             tokens = word_tokenize(line)
             for word1, word2 in bigrams(tokens, pad_right=True, pad_left=True):
                 if word1 and word2:
@@ -126,16 +108,6 @@ for line in corpus[:100000]:
 # In[11]:
 
 
-smoothing = 0.0001
-for trio in model:
-    count_sum = sum(model[trio].values()) + smoothing * len(dictionary)
-    for token in model[trio]:
-        model[trio][token] = (model[trio][token] + smoothing) / count_sum
-
-
-# In[17]:
-
-
 smoothing = 0.0001
 for trio in model_trigram:
     count_sum = sum(model_trigram[trio].values()) + smoothing * len(dictionary_trigram)
@@ -143,7 +115,7 @@ for trio in model_trigram:
         model_trigram[trio][token] = (model_trigram[trio][token] + smoothing) / count_sum
 
 
-# In[19]:
+# In[12]:
 
 
 smoothing = 0.0001
@@ -153,12 +125,12 @@ for trio in model_bigram:
         model_bigram[trio][token] = (model_bigram[trio][token] + smoothing) / count_sum
 
 
-# In[21]:
+# In[19]:
 
 
 from collections import Counter
 
-default = "the:0.30000 of:0.20000 and:0.10000 to:0.10000 in:0.10000 a:0.10000 :0.10000"
+default = "the:0.10000 of:0.05000 and:0.01000 to:0.01000 in:0.01000 a:0.01000 :0.81000"
 
 data = read_xz_file("dev-0\\in.tsv.xz")
 corpus_before=[]
@@ -172,16 +144,11 @@ with open("dev-0\\out.tsv", "w", encoding="utf-8") as output:
         tokens = word_tokenize(text)
         prediction = ""
 
-        if len(tokens) >= 4:
+        if len(tokens) >= 3:
-            results = dict(model[(tokens[0], tokens[1], tokens[2])])
+            results = dict(model_trigram[(tokens[0], tokens[1])])
             if results:
                 prediction = ' '.join(f"{term}:{round(prob, 5)}" for term, prob in Counter(results).most_common(6))
 
-        if prediction == "":
-            trigram_results = dict(model_trigram[(tokens[0], tokens[1])])
-            if trigram_results:
-                prediction = ' '.join(f"{term}:{round(prob, 5)}" for term, prob in Counter(trigram_results).most_common(6))
-
         if prediction == "":
             bigram_results = dict(model_bigram[tokens[0]])
             if bigram_results:
@@ -199,12 +166,12 @@ with open("dev-0\\out.tsv", "w", encoding="utf-8") as output:
 
 
 
-# In[23]:
+# In[22]:
 
 
 from collections import Counter
 
-default = "the:0.30000 of:0.20000 and:0.10000 to:0.10000 in:0.10000 a:0.10000 :0.10000"
+default = "the:0.10000 of:0.05000 and:0.01000 to:0.01000 in:0.01000 a:0.01000 :0.81000"
 
 data = read_xz_file("test-A\\in.tsv.xz")
 corpus_before=[]
@@ -218,16 +185,11 @@ with open("test-A\\out.tsv", "w", encoding="utf-8") as output:
         tokens = word_tokenize(text)
         prediction = ""
 
-        if len(tokens) >= 4:
+        if len(tokens) >= 3:
-            results = dict(model[(tokens[0], tokens[1], tokens[2])])
+            results = dict(model_trigram[(tokens[0], tokens[1])])
             if results:
                 prediction = ' '.join(f"{term}:{round(prob, 5)}" for term, prob in Counter(results).most_common(6))
 
-        if prediction == "":
-            trigram_results = dict(model_trigram[(tokens[0], tokens[1])])
-            if trigram_results:
-                prediction = ' '.join(f"{term}:{round(prob, 5)}" for term, prob in Counter(trigram_results).most_common(6))
-
         if prediction == "":
             bigram_results = dict(model_bigram[tokens[0]])
             if bigram_results: