Este es el modelo más grande de roberta FacebookAI/xlm-roberta-large-finetuned-conll03-english- Este modelo fue ajustado usando el framework Kaggle [https://www.kaggle.com/settings]. Para realizar el preentrenamiento del modelo se tuvo que crear un directorio temporal en Kaggle con el fin de almacenar de manera temoporal el modelo que pesa alrededor de 35 Gz.

The following hyperparameters were used during training:

eval_strategy="epoch",
save_strategy="epoch",
learning_rate=2e-5, # (Aprendizaje se esta cambiando)
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
num_train_epochs=5,
weight_decay=0.1,
max_grad_norm=1.0,
adam_epsilon=1e-5,
fp16=True,
save_total_limit=2,
load_best_model_at_end=True,
push_to_hub=True,
metric_for_best_model="f1",
seed=42,

Metric	Value
eval_loss	0.12918254733085632
eval_precision	0.8674463937621832
eval_recall	0.8752458555774094
eval_f1	0.8713286713286713
eval_accuracy	0.9813980358174466
eval_runtime	3.6357
eval_samples_per_second	417.526
eval_steps_per_second	26.13
epoch	5.0

Label	Precision	Recall	F1	Number
LOC	0.8867924528301887	0.8238007380073801	0.8541367766618843	1084
MISC	0.7349726775956285	0.7911764705882353	0.7620396600566574	340
ORG	0.8400272294077604	0.8814285714285715	0.8602300453119553	1400
PER	0.9599465954606141	0.9782312925170068	0.9690026954177898	735

KPOETA/BERTO-LOS-MUCHACHOS-1

xml-roberta-large-finetuned-ner

Model description

KPOETA/
BERTO-LOS-MUCHACHOS-1