Update README.md

README.md

@@ -40,28 +40,28 @@ La Configuración 3, con un número de épocas superior, logró la convergencia
 
 ## Argumentos de entrenamiento
 
-training_args = Seq2SeqTrainingArguments(
-    output_dir=output_dir,
-    lr_scheduler_type="linear",
-    num_train_epochs=15,
-    per_device_train_batch_size=8,
-    per_device_eval_batch_size=8,
-    gradient_accumulation_steps=2,
-    learning_rate=5e-5,
-    weight_decay=0.01,
-    warmup_steps=50,
-    predict_with_generate=True,
-    logging_dir=f"{output_dir}/logs",
-    logging_steps=10,
-    eval_steps=10,
-    save_total_limit=2,
-    dataloader_num_workers=2,
-    evaluation_strategy="steps",
-    save_strategy="steps",
-    load_best_model_at_end=True,
-    fp16=True,
-    seed=42,
-)
+| Argumento                     |                  Valor |
+| ----------------------------- | ---------------------: |
+| `output_dir`                  |           `output_dir` |
+| `lr_scheduler_type`           |             `"linear"` |
+| `num_train_epochs`            |                   `15` |
+| `per_device_train_batch_size` |                    `8` |
+| `per_device_eval_batch_size`  |                    `8` |
+| `gradient_accumulation_steps` |                    `2` |
+| `learning_rate`               |                 `5e-5` |
+| `weight_decay`                |                 `0.01` |
+| `warmup_steps`                |                   `50` |
+| `predict_with_generate`       |                 `True` |
+| `logging_dir`                 | `f"{output_dir}/logs"` |
+| `logging_steps`               |                   `10` |
+| `eval_steps`                  |                   `10` |
+| `save_total_limit`            |                    `2` |
+| `dataloader_num_workers`      |                    `2` |
+| `evaluation_strategy`         |              `"steps"` |
+| `save_strategy`               |              `"steps"` |
+| `load_best_model_at_end`      |                 `True` |
+| `fp16`                        |                 `True` |
+| `seed`                        |                   `42` |
 
 ## Dataset
 
@@ -83,9 +83,11 @@ Splits disponibles:
 
 Puedes cargar el dataset directamente desde Hugging Face:
 
-_from datasets import load_dataset
+```
+from datasets import load_dataset
 ds_train = load_dataset("AngelaGijon/TFG-DockerCommands-Dataset", split="train")
-print(ds_train[0])_
+print(ds_train[0])
+```
 
 Disponible en el repositorio: **"AngelaGijon/TFG-DockerCommands-Dataset"**