yas

README.md

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----
-language: py
-library_name: PyTorch
-pipeline_tag: text-generation
-tags:
-- pytorch
-- text2text
-- tekkonetes
----

predict.py

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+# Import libraries
+import nltk
+import numpy as np
+import pickle
+nltk.download('punkt')
+# Define predict_word function
+def predict_word(model, last_word):
+    if last_word in model:
+        return np.random.choice(model[last_word])
+    else:
+        return None
+# Load the model
+with open("model.pkl", "rb") as f:
+    model = pickle.load(f)
+
+# Run the prediction for 10 words
+input_words = input('Input words: ')
+for i in range(10):
+    input_words_list = nltk.word_tokenize(input_words)
+    last_word = input_words_list[-1]
+    predicted_word = predict_word(model, last_word)
+    input_words = f"{input_words}" + " " + f'{predicted_word}'
+
+print(input_words)

pytorch_model.bin

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-version https://git-lfs.github.com/spec/v1
-oid sha256:a4d1d24b0310816195ff64dfb22d22ea033844dc332f0c2bbc53a0af421483c3
-size 15748927

requirements.txt

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+nltk==3.8.1
+numpy==1.24.2

train.py

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+# Download nltk and numpy
+import os
+os.system('pip install nltk numpy')
+import nltk
+import numpy as np
+
+nltk.download('punkt')
+def train_model(corpus):
+    tokens = nltk.word_tokenize(corpus)
+    model = {}
+    for i in range(len(tokens) - 1):
+        if tokens[i] in model:
+            model[tokens[i]].append(tokens[i + 1])
+        else:
+            model[tokens[i]] = [tokens[i + 1]]
+    return model
+
+import pickle
+
+# Train the model on a given corpus
+corpus = open('corpus.txt').read()
+model = train_model(corpus)
+
+# Save the model to a file
+with open("model.pkl", "wb") as f:
+    pickle.dump(model, f)