mrpc removed

.history/trainml_20240218172323.py

@@ -0,0 +1,94 @@
+# First, we grab tools from our toolbox. These tools help us with different tasks like reading books (datasets),
+# learning new languages (tokenization), and solving puzzles (models).
+from datasets import load_dataset  # This tool helps us get our book, where the puzzles are.
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, AdamW, get_scheduler  # These help us understand and solve puzzles.
+from transformers import DataCollatorWithPadding  # This makes sure all puzzle pieces are the same size.
+from torch.utils.data import DataLoader  # This helps us handle one page of puzzles at a time.
+import torch  # This is like the brain of our operations, helping us think through puzzles.
+from tqdm.auto import tqdm  # This is our progress bar, showing us how far we've come in solving the book.
+import evaluate  # This tells us how well we did in solving puzzles.
+from accelerate import Accelerator  # This makes everything go super fast, like a rocket!
+
+def train_and_save_model():
+    # Now, let's pick up the book we're going to solve today.
+    raw_datasets = load_dataset("glue")  # This is a book filled with puzzles about matching sentences.
+
+    # Before we start solving puzzles, we need to understand the language they're written in.
+    checkpoint = "bert-base-uncased"  # This is a guidebook to help us understand the puzzles' language.
+    tokenizer = AutoTokenizer.from_pretrained(checkpoint)  # This tool helps us read and understand the language in our book.
+
+    # To solve puzzles, we need to make sure we understand each sentence properly.
+    def tokenize_function(example):  # This is like reading each sentence carefully and understanding each word.
+        return tokenizer(example["sentence1"], example["sentence2"], truncation=True)
+
+    # We prepare all puzzles in the book so they're ready to solve.
+    tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)  # This is like marking all the important parts of the sentences.
+
+    # Puzzles can be different sizes, but our puzzle solver works best when all puzzles are the same size.
+    data_collator = DataCollatorWithPadding(tokenizer=tokenizer)  # This adds extra paper to smaller puzzles to make them all the same size.
+
+    # We're setting up our puzzle pages, making sure we're ready to solve them one by one.
+    tokenized_datasets = tokenized_datasets.remove_columns(["sentence1", "sentence2", "idx"])  # We remove stuff we don't need.
+    tokenized_datasets = tokenized_datasets.rename_column("label", "labels")  # We make sure the puzzle answers are labeled correctly.
+    tokenized_datasets.set_format("torch")  # We make sure our puzzles are in the right format for our brain to understand.
+
+    # Now, we're ready to start solving puzzles, one page at a time.
+    train_dataloader = DataLoader(
+        tokenized_datasets["train"], shuffle=True, batch_size=8, collate_fn=data_collator
+    )  # This is our training puzzles.
+    eval_dataloader = DataLoader(
+        tokenized_datasets["validation"], batch_size=8, collate_fn=data_collator
+    )  # These are puzzles we use to check our progress.
+
+    # We need a puzzle solver, which is specially trained to solve these types of puzzles.
+    model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)  # This is our puzzle-solving robot.
+
+    # Our robot needs instructions on how to get better at solving puzzles.
+    optimizer = AdamW(model.parameters(), lr=5e-5)  # This tells our robot how to improve.
+    num_epochs = 1  # This is how many times we'll go through the whole book of puzzles.
+    num_training_steps = num_epochs * len(train_dataloader)  # This is the total number of puzzles we'll solve.
+    lr_scheduler = get_scheduler(
+        "linear",
+        optimizer=optimizer,
+        num_warmup_steps=0,
+        num_training_steps=num_training_steps,
+    )  # This adjusts how quickly our robot learns over time.
+
+    # To solve puzzles super fast, we're going to use a rocket!
+    accelerator = Accelerator()  # This is our rocket that makes everything go faster.
+    model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(
+        model, optimizer, train_dataloader, eval_dataloader
+    )  # We make sure our robot, our puzzles, and our instructions are all ready for the rocket.
+
+    # It's time to start solving puzzles!
+    progress_bar = tqdm(range(num_training_steps))  # This shows us our progress.
+    model.train()  # We tell our robot it's time to start learning.
+    for epoch in range(num_epochs):  # We go through our book of puzzles multiple times to get really good.
+        for batch in train_dataloader:  # Each time, we take a page of puzzles to solve.
+            outputs = model(**batch)  # Our robot tries to solve the puzzles.
+            loss = outputs.loss  # We check how many mistakes it made.
+            accelerator.backward(loss)  # We give feedback to our robot so it can learn from its mistakes.
+            optimizer.step()  # We update our robot's puzzle-solving strategy.
+            lr_scheduler.step()  # We adjust how quickly our robot is learning.
+            optimizer.zero_grad()  # We reset some settings to make sure our robot is ready for the next page.
+            progress_bar.update(1)  # We update our progress bar to show how many puzzles we've solved.
+
+    # After all that practice, it's time to test how good our robot has become at solving puzzles.
+    metric = evaluate.load("glue", "mrpc")  # This is like the answer key to check our robot's work.
+    model.eval()  # We tell our robot it's time to show what it's learned.
+    for batch in eval_dataloader:  # We take a page of puzzles we haven't solved yet.
+        with torch.no_grad():  # We make sure we're just testing, not learning anymore.
+            outputs = model(**batch)  # Our robot solves the puzzles.
+        logits = outputs.logits  # We look at our robot's answers.
+        predictions = torch.argmax(logits, dim=-1)  # We decide which answer our robot thinks is right.
+        metric.add_batch(predictions=predictions, references=batch["labels"])  # We compare our robot's answers to the correct answers.
+
+    final_score = metric.compute()  # We calculate how well our robot did.
+    print(final_score)  # We print out the score to see how well our robot solved the puzzles!
+
+    model.save_pretrained("path/to/save/model")
+    tokenizer.save_pretrained("path/to/save/tokenizer")
+
+if __name__ == "__main__":
+    train_and_save_model()
+

.history/trainml_20240218172329.py

@@ -0,0 +1,94 @@
+# First, we grab tools from our toolbox. These tools help us with different tasks like reading books (datasets),
+# learning new languages (tokenization), and solving puzzles (models).
+from datasets import load_dataset  # This tool helps us get our book, where the puzzles are.
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, AdamW, get_scheduler  # These help us understand and solve puzzles.
+from transformers import DataCollatorWithPadding  # This makes sure all puzzle pieces are the same size.
+from torch.utils.data import DataLoader  # This helps us handle one page of puzzles at a time.
+import torch  # This is like the brain of our operations, helping us think through puzzles.
+from tqdm.auto import tqdm  # This is our progress bar, showing us how far we've come in solving the book.
+import evaluate  # This tells us how well we did in solving puzzles.
+from accelerate import Accelerator  # This makes everything go super fast, like a rocket!
+
+def train_and_save_model():
+    # Now, let's pick up the book we're going to solve today.
+    raw_datasets = load_dataset("glue")  # This is a book filled with puzzles about matching sentences.
+
+    # Before we start solving puzzles, we need to understand the language they're written in.
+    checkpoint = "bert-base-uncased"  # This is a guidebook to help us understand the puzzles' language.
+    tokenizer = AutoTokenizer.from_pretrained(checkpoint)  # This tool helps us read and understand the language in our book.
+
+    # To solve puzzles, we need to make sure we understand each sentence properly.
+    def tokenize_function(example):  # This is like reading each sentence carefully and understanding each word.
+        return tokenizer(example["sentence1"], example["sentence2"], truncation=True)
+
+    # We prepare all puzzles in the book so they're ready to solve.
+    tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)  # This is like marking all the important parts of the sentences.
+
+    # Puzzles can be different sizes, but our puzzle solver works best when all puzzles are the same size.
+    data_collator = DataCollatorWithPadding(tokenizer=tokenizer)  # This adds extra paper to smaller puzzles to make them all the same size.
+
+    # We're setting up our puzzle pages, making sure we're ready to solve them one by one.
+    tokenized_datasets = tokenized_datasets.remove_columns(["sentence1", "sentence2", "idx"])  # We remove stuff we don't need.
+    tokenized_datasets = tokenized_datasets.rename_column("label", "labels")  # We make sure the puzzle answers are labeled correctly.
+    tokenized_datasets.set_format("torch")  # We make sure our puzzles are in the right format for our brain to understand.
+
+    # Now, we're ready to start solving puzzles, one page at a time.
+    train_dataloader = DataLoader(
+        tokenized_datasets["train"], shuffle=True, batch_size=8, collate_fn=data_collator
+    )  # This is our training puzzles.
+    eval_dataloader = DataLoader(
+        tokenized_datasets["validation"], batch_size=8, collate_fn=data_collator
+    )  # These are puzzles we use to check our progress.
+
+    # We need a puzzle solver, which is specially trained to solve these types of puzzles.
+    model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)  # This is our puzzle-solving robot.
+
+    # Our robot needs instructions on how to get better at solving puzzles.
+    optimizer = AdamW(model.parameters(), lr=5e-5)  # This tells our robot how to improve.
+    num_epochs = 1  # This is how many times we'll go through the whole book of puzzles.
+    num_training_steps = num_epochs * len(train_dataloader)  # This is the total number of puzzles we'll solve.
+    lr_scheduler = get_scheduler(
+        "linear",
+        optimizer=optimizer,
+        num_warmup_steps=0,
+        num_training_steps=num_training_steps,
+    )  # This adjusts how quickly our robot learns over time.
+
+    # To solve puzzles super fast, we're going to use a rocket!
+    accelerator = Accelerator()  # This is our rocket that makes everything go faster.
+    model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(
+        model, optimizer, train_dataloader, eval_dataloader
+    )  # We make sure our robot, our puzzles, and our instructions are all ready for the rocket.
+
+    # It's time to start solving puzzles!
+    progress_bar = tqdm(range(num_training_steps))  # This shows us our progress.
+    model.train()  # We tell our robot it's time to start learning.
+    for epoch in range(num_epochs):  # We go through our book of puzzles multiple times to get really good.
+        for batch in train_dataloader:  # Each time, we take a page of puzzles to solve.
+            outputs = model(**batch)  # Our robot tries to solve the puzzles.
+            loss = outputs.loss  # We check how many mistakes it made.
+            accelerator.backward(loss)  # We give feedback to our robot so it can learn from its mistakes.
+            optimizer.step()  # We update our robot's puzzle-solving strategy.
+            lr_scheduler.step()  # We adjust how quickly our robot is learning.
+            optimizer.zero_grad()  # We reset some settings to make sure our robot is ready for the next page.
+            progress_bar.update(1)  # We update our progress bar to show how many puzzles we've solved.
+
+    # After all that practice, it's time to test how good our robot has become at solving puzzles.
+    metric = evaluate.load("glue")  # This is like the answer key to check our robot's work.
+    model.eval()  # We tell our robot it's time to show what it's learned.
+    for batch in eval_dataloader:  # We take a page of puzzles we haven't solved yet.
+        with torch.no_grad():  # We make sure we're just testing, not learning anymore.
+            outputs = model(**batch)  # Our robot solves the puzzles.
+        logits = outputs.logits  # We look at our robot's answers.
+        predictions = torch.argmax(logits, dim=-1)  # We decide which answer our robot thinks is right.
+        metric.add_batch(predictions=predictions, references=batch["labels"])  # We compare our robot's answers to the correct answers.
+
+    final_score = metric.compute()  # We calculate how well our robot did.
+    print(final_score)  # We print out the score to see how well our robot solved the puzzles!
+
+    model.save_pretrained("path/to/save/model")
+    tokenizer.save_pretrained("path/to/save/tokenizer")
+
+if __name__ == "__main__":
+    train_and_save_model()
+

.lh/trainml.py.json

@@ -3,7 +3,7 @@
     "activeCommit": 0,
     "commits": [
         {
-            "activePatchIndex": 6,
+            "activePatchIndex": 8,
             "patches": [
                 {
                     "date": 1708166375103,
@@ -32,6 +32,14 @@
                 {
                     "date": 1708176288213,
                     "content": "Index: \n===================================================================\n--- \n+++ \n@@ -44,9 +44,9 @@\n     model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)  # This is our puzzle-solving robot.\n \n     # Our robot needs instructions on how to get better at solving puzzles.\n     optimizer = AdamW(model.parameters(), lr=5e-5)  # This tells our robot how to improve.\n-    num_epochs = 3  # This is how many times we'll go through the whole book of puzzles.\n+    num_epochs = 1  # This is how many times we'll go through the whole book of puzzles.\n     num_training_steps = num_epochs * len(train_dataloader)  # This is the total number of puzzles we'll solve.\n     lr_scheduler = get_scheduler(\n         \"linear\",\n         optimizer=optimizer,\n"
+                },
+                {
+                    "date": 1708257203286,
+                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -10,9 +10,9 @@\n from accelerate import Accelerator  # This makes everything go super fast, like a rocket!\n \n def train_and_save_model():\n     # Now, let's pick up the book we're going to solve today.\n-    raw_datasets = load_dataset(\"glue\", \"mrpc\")  # This is a book filled with puzzles about matching sentences.\n+    raw_datasets = load_dataset(\"glue\")  # This is a book filled with puzzles about matching sentences.\n \n     # Before we start solving puzzles, we need to understand the language they're written in.\n     checkpoint = \"bert-base-uncased\"  # This is a guidebook to help us understand the puzzles' language.\n     tokenizer = AutoTokenizer.from_pretrained(checkpoint)  # This tool helps us read and understand the language in our book.\n"
+                },
+                {
+                    "date": 1708257209627,
+                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -73,9 +73,9 @@\n             optimizer.zero_grad()  # We reset some settings to make sure our robot is ready for the next page.\n             progress_bar.update(1)  # We update our progress bar to show how many puzzles we've solved.\n \n     # After all that practice, it's time to test how good our robot has become at solving puzzles.\n-    metric = evaluate.load(\"glue\", \"mrpc\")  # This is like the answer key to check our robot's work.\n+    metric = evaluate.load(\"glue\")  # This is like the answer key to check our robot's work.\n     model.eval()  # We tell our robot it's time to show what it's learned.\n     for batch in eval_dataloader:  # We take a page of puzzles we haven't solved yet.\n         with torch.no_grad():  # We make sure we're just testing, not learning anymore.\n             outputs = model(**batch)  # Our robot solves the puzzles.\n"
                 }
             ],
             "date": 1708166375103,

trainml.py

@@ -11,7 +11,7 @@ from accelerate import Accelerator  # This makes everything go super fast, like
 
 def train_and_save_model():
     # Now, let's pick up the book we're going to solve today.
-    raw_datasets = load_dataset("glue", "mrpc")  # This is a book filled with puzzles about matching sentences.
+    raw_datasets = load_dataset("glue")  # This is a book filled with puzzles about matching sentences.
 
     # Before we start solving puzzles, we need to understand the language they're written in.
     checkpoint = "bert-base-uncased"  # This is a guidebook to help us understand the puzzles' language.
@@ -74,7 +74,7 @@ def train_and_save_model():
             progress_bar.update(1)  # We update our progress bar to show how many puzzles we've solved.
 
     # After all that practice, it's time to test how good our robot has become at solving puzzles.
-    metric = evaluate.load("glue", "mrpc")  # This is like the answer key to check our robot's work.
+    metric = evaluate.load("glue")  # This is like the answer key to check our robot's work.
     model.eval()  # We tell our robot it's time to show what it's learned.
     for batch in eval_dataloader:  # We take a page of puzzles we haven't solved yet.
         with torch.no_grad():  # We make sure we're just testing, not learning anymore.