Upload 2 files

.gitattributes

@@ -52,3 +52,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+HF_data.txt filter=lfs diff=lfs merge=lfs -text

HF_data.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:393027e9869b5c02bdc216c92fced1ad7532c5bf031773c127ed09b5ed339325
+size 183289353

HF_dataset.py

@@ -0,0 +1,143 @@
+import os
+from os.path import exists
+
+import datasets
+from datasets.tasks import TextClassification
+from datasets import load_dataset
+
+import numpy as np 
+import json
+
+from transformers import AutoTokenizer
+
+
+logger = datasets.logging.get_logger(__name__)
+
+## Constants
+USE_FULL_DATASET = True
+PROJECT_PATH = "./"
+
+
+
+def _define_columns(example):
+    text_splited = example["text"].split('\t')
+    return {"text": text_splited[1].strip(), "labels": int(text_splited[0])}
+
+class Sentiment(datasets.GeneratorBasedBuilder):
+    '''Custom Dataset created using the HuggingFace api so we can 
+    use all of their's api on the dataset'''
+    def _info(self):
+        class_names = ["negative", "positive"]
+        return datasets.DatasetInfo(
+            description="Our nice dataset in HF format",
+            features=datasets.Features(
+                {"text": datasets.Value("string"), 
+                "labels": datasets.ClassLabel(num_classes=2, names=class_names)}  #  Value("int32")
+            ),
+            supervised_keys=("text", "labels"),
+        )
+
+    def _split_generators(self, _):
+        """Returns SplitGenerators."""
+
+        data_dir = "./"
+
+        data = load_dataset("text", data_files="./HF_data.txt")
+        data = data.map(_define_columns)
+
+        texts_dataset_clean = data["train"].train_test_split(train_size=0.95, seed=12345)
+        # Rename the default "test" split to "validation"
+        texts_dataset_clean["validation"] = texts_dataset_clean.pop("test")
+
+
+        for split, dataset in texts_dataset_clean.items():
+            dataset.to_json(data_dir + f"twitter-sentiment-analysis-{split}.jsonl")
+
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(data_dir, "twitter-sentiment-analysis-train.jsonl")}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(data_dir, "twitter-sentiment-analysis-validation.jsonl")}),
+        ]   
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        logger.info("generating examples from = %s", filepath)
+        with open(filepath, encoding="utf-8") as f:
+            for key, row in enumerate(f):
+                data = json.loads(row)
+                yield key, {
+                    "text": data["text"],
+                    "labels": data["labels"],
+                }
+
+
+def read_file(file_name_label_tuple):
+    fname, label = file_name_label_tuple
+    tweets, labels = [], []
+    with open(fname, 'r', encoding='utf-8') as f:
+        tweets = f.readlines()
+    
+    labels = [label] * (len(tweets))
+
+    return(tweets, labels)
+
+
+def load_train_data():
+
+    if USE_FULL_DATASET == True:
+        X_train_neg_path = PROJECT_PATH + "train_neg_full.txt"
+        X_train_pos_path = PROJECT_PATH + "train_pos_full.txt"
+        
+    else:
+        X_train_neg_path = PROJECT_PATH + "train_neg.txt"
+        X_train_pos_path = PROJECT_PATH + "train_pos.txt"
+    
+    tweets, labels = read_file((X_train_neg_path, 0))
+    tweets = list(set(tweets))
+    labels = labels[:len(tweets)]
+    print("There are ", len(tweets), " negative tweets after removing the duplicates.")
+    
+    tweets_2, labels_2 = read_file((X_train_pos_path, 1))
+    tweets_2 = list(set(tweets_2))
+    labels_2 = labels_2[:len(tweets_2)]
+    print("There are ", len(tweets_2), " positive tweets after removing the duplicates.")
+
+    
+    tweets += tweets_2
+    tweets_2 = []
+    del(tweets_2)
+    labels += labels_2
+    labels_2 = []
+    del(labels_2)
+    print(f"Loaded {len(tweets)} tweets!")
+
+    tweets, labels = np.array(tweets), np.array(labels)
+    print(tweets)
+
+    # To shuffle the data before cerating the .txt file dataset
+    nb_of_samples = len(tweets)
+    shuffled_indices = np.random.permutation(nb_of_samples)
+    tweets = tweets[shuffled_indices]
+    labels = labels[shuffled_indices]
+
+    print("Number of indices for training: ", len(shuffled_indices))
+
+    return tweets, labels
+
+
+def create_data_file(tweets, labels):
+    
+    with open("HF_data.txt", "wb") as f:
+        for i in range(len(tweets)):
+            # print(tweets[i])
+            f.write(f"{labels[i]} \t {tweets[i]}".encode('utf-8'))
+
+    
+    
+
+def main():
+    tweets, labels = load_train_data()
+    create_data_file(tweets, labels)
+
+if __name__ == "__main__":
+    main()