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sara-m98/ECO_BETO_2_DESCARTADO
ECO_BETO_2_DESCARTADO is a token classification model from sara-m98. Use it when you need labels on individual words, such as names. It is set up for transformers.
modelid = 'dccuchile/bert-base-spanish-wwm-uncased'
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.safetensors437 MB · 100%
From the Hugging Face model README
model_id = 'dccuchile/bert-base-spanish-wwm-uncased'
training_args = TrainingArguments( output_dir='ECO_BETO_UNCASED_FINAL_2', evaluation_strategy="epoch", learning_rate=2e-5, per_device_train_batch_size=8, # SE HA DISMINUIDO per_device_eval_batch_size=8, num_train_epochs=32, logging_steps=10, warmup_steps=500, # AÑADIDO NUEVO PARA EVITAR SOBREAJUSTE weight_decay=0.01, save_strategy="epoch", load_best_model_at_end=True, push_to_hub=True )
Epoch Training Loss Validation Loss Precision Recall F1 Accuracy 1 0.077700 0.062895 0.407967 0.340118 0.370965 0.984857 2 0.026600 0.046988 0.430195 0.466256 0.447500 0.987083 3 0.016600 0.057439 0.445101 0.484199 0.463828 0.986488 4 0.009400 0.063273 0.419288 0.489020 0.451477 0.986186 5 0.006000 0.073604 0.452041 0.483396 0.467193 0.986783 6 0.003500 0.075105 0.443732 0.500536 0.470425 0.986838 7 0.002800 0.087318 0.459719 0.499732 0.478891 0.986587 8 0.002700 0.086024 0.447406 0.503482 0.473790 0.986307 9 0.002500 0.097406 0.471528 0.505624 0.487981 0.986492 10 0.001800 0.083656 0.481994 0.512587 0.496820 0.986886 11 0.000800 0.097788 0.440783 0.476433 0.457915 0.986552 12 0.001000 0.097364 0.466699 0.508570 0.486736 0.986814 13 0.001800 0.092892 0.457457 0.501071 0.478272 0.986906 14 0.001100 0.098654 0.462056 0.515265 0.487212 0.986848 15 0.000400 0.098693 0.479911 0.518211 0.498326 0.987071 16 0.000600 0.102500 0.485228 0.519014 0.501553 0.986984 17 0.000700 0.092908 0.483439 0.531601 0.506378 0.987309 18 0.000400 0.108735 0.469491 0.498661 0.483636 0.986931 19 0.000500 0.108452 0.482377 0.513123 0.497275 0.987038 20 0.000400 0.110222 0.477419 0.515265 0.495621 0.986849 21 0.000100 0.111396 0.474851 0.510712 0.492129 0.986813 22 0.000200 0.116713 0.489478 0.504553 0.496901 0.986853 23 0.000100 0.120678 0.471004 0.517675 0.493238 0.986511 24 0.000200 0.105337 0.479627 0.523299 0.500512 0.986750 25 0.000300 0.115356 0.490518 0.519550 0.504617 0.986805 26 0.000200 0.113237 0.481186 0.517140 0.498516 0.986855 27 0.000400 0.117845 0.487414 0.513390 0.500065 0.986799 28 0.000100 0.114433 0.502346 0.516069 0.509115 0.987371 29 0.000200 0.112246 0.494425 0.522496 0.508073 0.987242 30 0.000300 0.117259 0.495396 0.518747 0.506803 0.987060 31 0.000100 0.119009 0.488776 0.513123 0.500653 0.987001 32 0.000100 0.119304 0.490441 0.515265 0.502547 0.987036
