I got the error below running this code:
```
# Install Required Libraries
!pip install --upgrade transformers
!pip install datasets
!pip install evaluate
!pip install sacrebleu
!pip install bert_score

# Import Libraries
import evaluate
import torch
import random
import numpy as np
from transformers import (
    T5Tokenizer,
    T5ForConditionalGeneration,
    Seq2SeqTrainer,
    Seq2SeqTrainingArguments,
    DataCollatorForSeq2Seq,
    EarlyStoppingCallback,
    get_linear_schedule_with_warmup
)
from datasets import load_dataset, concatenate_datasets
from bert_score import score as bert_score_func

# Set Seed for Reproducibility
def set_seed(seed):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)

set_seed(42)

# Load and Prepare the Dataset
dataset1 = load_dataset("danielv835/personal_finance_v0.2")

# Remove the 'rejected' column from dataset1
dataset1 = dataset1.remove_columns(['rejected'])

# Check Available Splits
print("Dataset Splits:", dataset1)

# Inspect Data Alignment
print("\nInspecting Data Alignment:")
for i in range(5):
    sample = dataset1['train'][i]
    print(f"Sample {i + 1}:")
    print(f"  Context: {sample['context']}")
    print(f"  Chosen: {sample['chosen']}\n")

# Load the tokenizer
tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base")

# Tokenization function with dynamic padding handled by data collator
def tokenize_function(examples):
    inputs = tokenizer(
        examples["context"],
        max_length=256,  # Adjusted max_length
        truncation=True,
        padding=False  # Disable padding here
    )
    labels = tokenizer(
        examples["chosen"],
        max_length=256,  # Adjusted max_length
        truncation=True,
        padding=False  # Disable padding here
    )
    inputs["labels"] = labels["input_ids"]
    return inputs

# Tokenize dataset1
tokenized_datasets1 = dataset1.map(
    tokenize_function,
    batched=True,
    batch_size=1000  # Adjust as needed
)

# Verify Tokenization
print("\nSample Tokenized Inputs:")
print(tokenized_datasets1['train'][0])

# Load the pre-trained FLAN-T5 model for conditional generation
tw_model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base")

# Define the data collator
data_collator = DataCollatorForSeq2Seq(
    tokenizer=tokenizer,
    model=tw_model,
    label_pad_token_id=-100,  # Explicitly set label padding
    padding="longest",
    return_tensors="pt"
)

# Ensure the pad_token_id is set
if tw_model.config.pad_token_id is None:
    tw_model.config.pad_token_id = tokenizer.pad_token_id

# Adjust the model's label padding token to -100 to ignore during loss computation
tw_model.config.label_smoothing_factor = 0.0  # Disable label smoothing

# Define training arguments for the Trainer API
training_args = Seq2SeqTrainingArguments(
    output_dir="./trainer_wheel_model",    # Output directory for saving model checkpoints
    eval_strategy="epoch",                 # Evaluate at the end of every epoch
    learning_rate=3e-5,                    # Set model learning rate
    per_device_train_batch_size=8,         # Increased batch size
    per_device_eval_batch_size=4,          # Increased eval batch size
    gradient_accumulation_steps=2,         # Use gradient accumulation
    save_steps=500,                        # Save model every 500 steps
    save_total_limit=3,                    # Limit number of saved checkpoints
    logging_dir='./logs',                  # Directory for storing logs
    logging_steps=100,                     # Log every 100 steps
    num_train_epochs=3,                    # Increased from 1 to 3 epochs
    fp16=True,                             # Enable mixed precision if supported
    predict_with_generate=True,            # Generate predictions during evaluation
    generation_max_length=150,             # Maximum length of generated sequences
    generation_num_beams=5,                # Number of beams for beam search
    load_best_model_at_end=True,           # Load the best model at the end
    metric_for_best_model="bleu",          # Use BLEU for best model selection
    greater_is_better=True,                # Higher BLEU is better
    evaluation_strategy="epoch",           # Evaluate at each epoch
    save_strategy="epoch",                 # Save checkpoint at each epoch
    report_to="none",                      # Disable reporting to WandB or others
)

# Load additional metrics from Hugging Face Evaluate library
sacrebleu_metric = evaluate.load("sacrebleu")
bertscore_metric = evaluate.load("bertscore")

# Define Exact Match (Custom Implementation)
def exact_match_score(preds, labels):
    return sum([int(p.strip() == l.strip()) for p, l in zip(preds, labels)]) / len(labels) if len(labels) > 0 else 0

# Define F1 Score (Custom Implementation)
def f1_score_custom(preds, labels):
    f1_total = 0
    for p, l in zip(preds, labels):
        p_tokens = p.split()
        l_tokens = l.split()
        common = set(p_tokens) & set(l_tokens)
        if not common:
            f1_total += 0
        else:
            precision = len(common) / len(p_tokens)
            recall = len(common) / len(l_tokens)
            f1_total += 2 * precision * recall / (precision + recall)
    return f1_total / len(preds) if len(preds) > 0 else 0

# Define the compute_metrics function
def compute_metrics(eval_pred):
    preds, labels = eval_pred

    # If logits are returned as a tuple, extract the first element
    if isinstance(preds, tuple):
        preds = preds[0]

    # Replace -100 in labels to pad_token_id for decoding
    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)

    # Check for invalid token IDs
    preds = np.where(preds < len(tokenizer), preds, tokenizer.pad_token_id)

    # Decode the predicted tokens into text
    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)

    # Some simple post-processing
    decoded_preds = [pred.strip() for pred in decoded_preds]
    decoded_labels = [[label.strip()] for label in decoded_labels]  # SacreBLEU expects a list of references

    # Debugging: Print samples
    if random.random() < 1e-4:  # Print for a small subset to avoid flooding logs
        for i in range(min(3, len(decoded_preds))):
            print(f"Sample {i + 1}:")
            print(f"  Prediction: {decoded_preds[i]}")
            print(f"  Label: {decoded_labels[i][0]}\n")

    # Compute BLEU score using SacreBLEU metric
    bleu_result = sacrebleu_metric.compute(predictions=decoded_preds, references=decoded_labels)
    bleu_score = bleu_result["score"]

    # Flatten the decoded_labels for F1 and Exact Match computation
    flattened_labels = [label[0] for label in decoded_labels]

    # Compute Exact Match (EM) score
    em_score = exact_match_score(decoded_preds, flattened_labels)

    # Compute F1 Score
    f1_score = f1_score_custom(decoded_preds, flattened_labels)

    # Compute BERTScore (using pre-trained BERT embeddings)
    P, R, F1_bert = bert_score_func(decoded_preds, flattened_labels, lang="en", rescale_with_baseline=True)
    bert_f1_score = F1_bert.mean().item()  # Get the mean of the F1 BERTScore

    # Combine all metrics into a single dictionary
    result = {
        "bleu": bleu_score,
        "exact_match": em_score,
        "f1": f1_score,
        "bert_score_f1": bert_f1_score
    }

    return result

# Initialize Callbacks
callbacks = [EarlyStoppingCallback(early_stopping_patience=2)]

# Initialize the Trainer instance
trainer = Seq2SeqTrainer(
    model=tw_model,                        # Model to be trained
    args=training_args,                    # Training arguments
    train_dataset=tokenized_datasets1["train"],           # Training dataset
    eval_dataset=tokenized_datasets1["test"],             # Evaluation dataset
    data_collator=data_collator,           # Data collator for batching
    tokenizer=tokenizer,                   # Tokenizer for encoding/decoding
    compute_metrics=compute_metrics,       # Function to compute evaluation metrics
    callbacks=callbacks,                   # Callbacks for training
)

# Generate Predictions Before Training (Diagnostics)
def generate_initial_predictions():
    print("\nGenerating Initial Predictions Before Training:")
    for i in range(3):
        sample = tokenized_datasets1['test'][i]
        input_ids = torch.tensor(sample['input_ids']).unsqueeze(0)  # Batch size 1
        input_ids = input_ids.to(trainer.args.device)
        outputs = tw_model.generate(
            input_ids,
            max_length=50,
            num_beams=3,
            early_stopping=True
        )
        decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
        print(f"Sample {i + 1}:")
        print(f"  Prediction: {decoded_output}")
        print(f"  Label: {dataset1['test'][i]['chosen']}\n")

# Empty the cache before starting the training process
torch.cuda.empty_cache()

# Generate initial predictions before training
generate_initial_predictions()

# Start training the model
print("Starting training...")
trainer.train()
print("Training completed!")
```
```
IndexError                                Traceback (most recent call last)
<ipython-input-11-cf37cbb221e6> in <cell line: 3>()
      1 # Start training the model
      2 print("Starting training...")
----> 3 trainer.train()
      4 print("Training completed!")

15 frames
/usr/local/lib/python3.10/dist-packages/transformers/trainer.py in train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)
   2050                 hf_hub_utils.enable_progress_bars()
   2051         else:
-> 2052             return inner_training_loop(
   2053                 args=args,
   2054                 resume_from_checkpoint=resume_from_checkpoint,

/usr/local/lib/python3.10/dist-packages/transformers/trainer.py in _inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)
   2485 
   2486             self.control = self.callback_handler.on_epoch_end(args, self.state, self.control)
-> 2487             self._maybe_log_save_evaluate(tr_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval)
   2488 
   2489             if DebugOption.TPU_METRICS_DEBUG in self.args.debug:

/usr/local/lib/python3.10/dist-packages/transformers/trainer.py in _maybe_log_save_evaluate(self, tr_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval)
   2913         metrics = None
   2914         if self.control.should_evaluate:
-> 2915             metrics = self._evaluate(trial, ignore_keys_for_eval)
   2916 
   2917         if self.control.should_save:

/usr/local/lib/python3.10/dist-packages/transformers/trainer.py in _evaluate(self, trial, ignore_keys_for_eval, skip_scheduler)
   2870 
   2871     def _evaluate(self, trial, ignore_keys_for_eval, skip_scheduler=False):
-> 2872         metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)
   2873         self._report_to_hp_search(trial, self.state.global_step, metrics)
   2874 

/usr/local/lib/python3.10/dist-packages/transformers/trainer_seq2seq.py in evaluate(self, eval_dataset, ignore_keys, metric_key_prefix, **gen_kwargs)
    178         self.gather_function = self.accelerator.gather
    179         self._gen_kwargs = gen_kwargs
--> 180         return super().evaluate(eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)
    181 
    182     def predict(

/usr/local/lib/python3.10/dist-packages/transformers/trainer.py in evaluate(self, eval_dataset, ignore_keys, metric_key_prefix)
   3866 
   3867         eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
-> 3868         output = eval_loop(
   3869             eval_dataloader,
   3870             description="Evaluation",

/usr/local/lib/python3.10/dist-packages/transformers/trainer.py in evaluation_loop(self, dataloader, description, prediction_loss_only, ignore_keys, metric_key_prefix)
   4158                 )
   4159             else:
-> 4160                 metrics = self.compute_metrics(EvalPrediction(predictions=all_preds, label_ids=all_labels))
   4161         elif metrics is None:
   4162             metrics = {}

<ipython-input-8-bfb1379b1e84> in compute_metrics(eval_pred)
     73 
     74     # Decode the predicted tokens into text
---> 75     decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
     76     decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
     77 

/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py in batch_decode(self, sequences, skip_special_tokens, clean_up_tokenization_spaces, **kwargs)
   3965             `List[str]`: The list of decoded sentences.
   3966         """
-> 3967         return [
   3968             self.decode(
   3969                 seq,

/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py in <listcomp>(.0)
   3966         """
   3967         return [
-> 3968             self.decode(
   3969                 seq,
   3970                 skip_special_tokens=skip_special_tokens,

/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py in decode(self, token_ids, skip_special_tokens, clean_up_tokenization_spaces, **kwargs)
   4005         token_ids = to_py_obj(token_ids)
   4006 
-> 4007         return self._decode(
   4008             token_ids=token_ids,
   4009             skip_special_tokens=skip_special_tokens,

/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils.py in _decode(self, token_ids, skip_special_tokens, clean_up_tokenization_spaces, spaces_between_special_tokens, **kwargs)
   1086         self._decode_use_source_tokenizer = kwargs.pop("use_source_tokenizer", False)
   1087 
-> 1088         filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens)
   1089         # If given is a single id, prevents splitting the string in upcoming loop
   1090         if isinstance(filtered_tokens, str):

/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils.py in convert_ids_to_tokens(self, ids, skip_special_tokens)
   1067                 tokens.append(self._added_tokens_decoder[index].content)
   1068             else:
-> 1069                 tokens.append(self._convert_id_to_token(index))
   1070         return tokens
   1071 

/usr/local/lib/python3.10/dist-packages/transformers/models/t5/tokenization_t5.py in _convert_id_to_token(self, index)
    404     def _convert_id_to_token(self, index):
    405         """Converts an index (integer) in a token (str) using the vocab."""
--> 406         token = self.sp_model.IdToPiece(index)
    407         return token
    408 

/usr/local/lib/python3.10/dist-packages/sentencepiece/__init__.py in _batched_func(self, arg)
   1177       return [_func(self, n) for n in arg]
   1178     else:
-> 1179       return _func(self, arg)
   1180 
   1181   setattr(classname, name, _batched_func)

/usr/local/lib/python3.10/dist-packages/sentencepiece/__init__.py in _func(v, n)
   1170   def _func(v, n):
   1171     if type(n) is int and (n < 0 or n >= v.piece_size()):
-> 1172       raise IndexError('piece id is out of range.')
   1173     return func(v, n)
   1174 

IndexError: piece id is out of range.
```