Assess the code and output below:
```
# Install Required Libraries
!pip install --upgrade transformers
!pip install datasets
!pip install evaluate
!pip install bert_score

# Import Libraries
import evaluate
import torch
import numpy as np
from transformers import (
    T5Tokenizer,
    T5ForConditionalGeneration,
    Seq2SeqTrainer,
    Seq2SeqTrainingArguments,
    DataCollatorForSeq2Seq
)
from datasets import load_dataset, concatenate_datasets

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

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

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

# Tokenization function without padding
def tokenize_function(examples):
    model_inputs = tokenizer(
        examples["context"],
        max_length=256,
        truncation=True,
        padding=False
    )
    with tokenizer.as_target_tokenizer():
        labels = tokenizer(
            examples["chosen"],
            max_length=256,
            truncation=True,
            padding=False
    )
    model_inputs["labels"] = labels["input_ids"]
    return model_inputs

# Tokenize both datasets
tokenized_datasets1 = dataset1.map(tokenize_function, batched=True)

# 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
tw_model.config.pad_token_id = tokenizer.pad_token_id

# 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-4,                    # Set model learning rate
    per_device_train_batch_size=8,         # Adjust batch size for your GPU/CPU
    per_device_eval_batch_size=4,
    gradient_accumulation_steps=2,         # Use gradient accumulation
    save_steps=500,                        # Save model every 500 steps
    save_total_limit=2,                    # Limit number of saved checkpoints
    logging_dir='./logs',                  # Directory for storing logs
    logging_steps=200,                     # Log every 200 steps
    num_train_epochs=6,                    # Number of training epochs
    fp16=False,                             # Disable mixed precision
    predict_with_generate=True,            # Generate predictions during evaluation
    generation_max_length=256,             # Maximum length of generated sequences
    generation_num_beams=5,                # Number of beams for beam search
)

# 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 no common tokens, F1 score is 0
        if len(common) == 0:
            f1_total += 0
        else:
            precision = len(common) / len(p_tokens) if len(p_tokens) > 0 else 0
            recall = len(common) / len(l_tokens) if len(l_tokens) > 0 else 0
            if precision + recall > 0:
                f1_total += 2 * precision * recall / (precision + recall)

    return f1_total / len(preds) if len(preds) > 0 else 0

# Load required metrics
metric_bert = evaluate.load("bertscore")
metric_meteor = evaluate.load("meteor")

# 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 preds and the labels as we can't decode them
    preds = np.where(preds != -100, preds, tokenizer.pad_token_id)
    labels = np.where(labels != -100, labels, 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)

    # Post-process predictions and labels
    decoded_preds = [pred.strip() for pred in decoded_preds]
    decoded_labels = [[label.strip()] for label in decoded_labels]  # METEOR and BERTScore expect a list of references

    # Flatten labels for F1 and METEOR calculation
    flattened_labels = [label[0] for label in decoded_labels]  # Assuming each label list contains one element

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

    # Compute METEOR
    meteor_result = metric_meteor.compute(predictions=decoded_preds, references=flattened_labels)
    meteor_score = meteor_result["meteor"]

    # Compute BERTScore (using default 'bert-base-uncased' model)
    bert_result = metric_bert.compute(predictions=decoded_preds, references=flattened_labels, lang="en")
    bert_score_f1 = np.mean(bert_result["f1"])

    # Combine the metrics
    result = {
        "f1": f1_score * 100,
        "meteor": meteor_score * 100,      # Express METEOR as percentage
        "bert_score_f1": bert_score_f1 * 100  # Express BERTScore F1 as percentage
    }

    return result

# 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
)

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

# Start training the model
print("Starting training...")
trainer.train()
print("Training completed!")
```
```
Starting training...
 [ 7070/14138 1:43:48 < 1:43:48, 1.13 it/s, Epoch 1.00/2]
Epoch	Training Loss	Validation Loss
 [ 39/1000 01:31 < 38:35, 0.41 it/s]
 [14138/14138 4:48:56, Epoch 1/2]
Epoch	Training Loss	Validation Loss	F1	Meteor	Bert Score F1
0	3.132700	2.974948	9.036977	14.245515	81.960124
1	3.084400	2.952344	9.511389	14.501229	82.234502
tokenizer_config.json: 100%
 25.0/25.0 [00:00<00:00, 2.16kB/s]
config.json: 100%
 482/482 [00:00<00:00, 39.9kB/s]
vocab.json: 100%
 899k/899k [00:00<00:00, 2.05MB/s]
merges.txt: 100%
 456k/456k [00:00<00:00, 1.90MB/s]
tokenizer.json: 100%
 1.36M/1.36M [00:00<00:00, 6.22MB/s]
model.safetensors: 100%
 1.42G/1.42G [00:02<00:00, 503MB/s]
Some weights of RobertaModel were not initialized from the model checkpoint at roberta-large and are newly initialized: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Training completed!
```