Create README.md

README.md

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+---
+license: afl-3.0
+datasets:
+- HuggingFaceTB/cosmopedia
+metrics:
+- accuracy
+library_name: adapter-transformers
+pipeline_tag: text-classification
+tags:
+- code
+---
+# Install the necessary libraries
+!pip install transformers
+!pip install torch
+
+import torch
+from transformers import RobertaTokenizer, RobertaForSequenceClassification, XLNetTokenizer, XLNetForSequenceClassification
+from transformers import Trainer, TrainingArguments
+from sklearn.model_selection import train_test_split
+import numpy as np
+from sklearn.metrics import accuracy_score, precision_recall_fscore_support
+
+# Example dataset for text classification (replace with your own dataset)
+texts = [...]  # List of input texts
+labels = [...]  # List of corresponding labels (0 or 1 for binary classification)
+
+# Split the dataset into training and testing sets
+train_texts, test_texts, train_labels, test_labels = train_test_split(texts, labels, test_size=0.2, random_state=42)
+
+# Define the tokenizer and model for RoBERTa
+roberta_tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
+roberta_model = RobertaForSequenceClassification.from_pretrained("roberta-base")
+
+# Define the tokenizer and model for XLNet
+xlnet_tokenizer = XLNetTokenizer.from_pretrained("xlnet-base-cased")
+xlnet_model = XLNetForSequenceClassification.from_pretrained("xlnet-base-cased")
+
+# Tokenize and encode the training and testing sets
+train_encodings_roberta = roberta_tokenizer(train_texts, truncation=True, padding=True)
+test_encodings_roberta = roberta_tokenizer(test_texts, truncation=True, padding=True)
+
+train_encodings_xlnet = xlnet_tokenizer(train_texts, truncation=True, padding=True)
+test_encodings_xlnet = xlnet_tokenizer(test_texts, truncation=True, padding=True)
+
+class MyDataset(torch.utils.data.Dataset):
+    def __init__(self, encodings, labels):
+        self.encodings = encodings
+        self.labels = labels
+
+    def __getitem__(self, idx):
+        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
+        item['labels'] = torch.tensor(self.labels[idx])
+        return item
+
+    def __len__(self):
+        return len(self.labels)
+
+train_dataset_roberta = MyDataset(train_encodings_roberta, train_labels)
+test_dataset_roberta = MyDataset(test_encodings_roberta, test_labels)
+
+train_dataset_xlnet = MyDataset(train_encodings_xlnet, train_labels)
+test_dataset_xlnet = MyDataset(test_encodings_xlnet, test_labels)
+
+# Fine-tune RoBERTa model
+training_args = TrainingArguments(
+    per_device_train_batch_size=8,
+    per_device_eval_batch_size=8,
+    num_train_epochs=3,
+    logging_dir='./logs',
+    logging_steps=10,
+)
+
+trainer_roberta = Trainer(
+    model=roberta_model,
+    args=training_args,
+    train_dataset=train_dataset_roberta,
+    eval_dataset=test_dataset_roberta,
+)
+
+trainer_roberta.train()
+
+# Fine-tune XLNet model
+trainer_xlnet = Trainer(
+    model=xlnet_model,
+    args=training_args,
+    train_dataset=train_dataset_xlnet,
+    eval_dataset=test_dataset_xlnet,
+)
+
+trainer_xlnet.train()
+
+# Evaluate models
+def evaluate_model(model, test_dataset):
+    predictions = []
+    labels = []
+    for batch in test_dataset:
+        input_ids = batch['input_ids'].to(model.device)
+        attention_mask = batch['attention_mask'].to(model.device)
+        labels.extend(batch['labels'].tolist())
+        with torch.no_grad():
+            outputs = model(input_ids, attention_mask=attention_mask)
+        logits = outputs.logits
+        predictions.extend(torch.argmax(logits, axis=1).tolist())
+    accuracy = accuracy_score(labels, predictions)
+    precision, recall, f1, _ = precision_recall_fscore_support(labels, predictions, average='binary')
+    return accuracy, precision, recall, f1
+
+accuracy_roberta, precision_roberta, recall_roberta, f1_roberta = evaluate_model(roberta_model, test_dataset_roberta)
+accuracy_xlnet, precision_xlnet, recall_xlnet, f1_xlnet = evaluate_model(xlnet_model, test_dataset_xlnet)
+
+print("RoBERTa Model Evaluation:")
+print(f"Accuracy: {accuracy_roberta}")
+print(f"Precision: {precision_roberta}")
+print(f"Recall: {recall_roberta}")
+print(f"F1 Score: {f1_roberta}")
+
+print("\nXLNet Model Evaluation:")
+print(f"Accuracy: {accuracy_xlnet}")
+print(f"Precision: {precision_xlnet}")
+print(f"Recall: {recall_xlnet}")
+print(f"F1 Score: {f1_xlnet}")