Assess the results of this code:
```
# Load and Prepare the Dataset
dataset1 = load_dataset("danielv835/personal_finance_v0.2")

# 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=512,
        truncation=True,
        padding="max_length"
    )
    with tokenizer.as_target_tokenizer():
        labels = tokenizer(
            examples["chosen"],
            max_length=512,
            truncation=True,
            padding="max_length"
    )
    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,
  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-5,                    # 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=5,                    # Number of training epochs
    fp16=False,                             # Disable mixed precision
    predict_with_generate=True,            # Generate predictions during evaluation
    generation_max_length=512,             # Maximum length of generated sequences
    generation_num_beams=5,                # Number of beams for beam search
)

# Load the SacreBLEU metric from the Hugging Face Evaluate library
metric = evaluate.load("sacrebleu")

# 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)

    # Decode the reference tokens into text
    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 refrences

    # Compute the metrics using the SacreBLEU metric
    result = metric.compute(predictions=decoded_preds, references=decoded_labels)

    # Extract the BLEU score from the result
    result = {"bleu": result["score"]}

    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
trainer.train()
```
```
[17675/17675 10:24:58, Epoch 5/5]
Epoch	Training Loss	Validation Loss	Bleu
1	0.536700	0.496225	0.951696
2	0.534900	0.489908	0.974208
3	0.511400	0.486586	1.063596
4	0.521500	0.485080	1.053734
5	0.500700	0.484606	1.005190
TrainOutput(global_step=17675, training_loss=0.5790776677111419, metrics={'train_runtime': 37500.9384, 'train_samples_per_second': 7.541, 'train_steps_per_second': 0.471, 'total_flos': 1.9363910791200768e+17, 'train_loss': 0.5790776677111419, 'epoch': 5.0})
```