create_bert_model {aifeducation} | R Documentation |
This function creates a transformer configuration based on the BERT base architecture and a vocabulary based on WordPiece by using the python libraries 'transformers' and 'tokenizers'.
create_bert_model(
ml_framework = aifeducation_config$get_framework(),
model_dir,
vocab_raw_texts = NULL,
vocab_size = 30522,
vocab_do_lower_case = FALSE,
max_position_embeddings = 512,
hidden_size = 768,
num_hidden_layer = 12,
num_attention_heads = 12,
intermediate_size = 3072,
hidden_act = "gelu",
hidden_dropout_prob = 0.1,
attention_probs_dropout_prob = 0.1,
sustain_track = TRUE,
sustain_iso_code = NULL,
sustain_region = NULL,
sustain_interval = 15,
trace = TRUE,
pytorch_safetensors = TRUE
)
ml_framework |
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model_dir |
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vocab_raw_texts |
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vocab_size |
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vocab_do_lower_case |
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max_position_embeddings |
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num_attention_heads |
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intermediate_size |
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attention_probs_dropout_prob |
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sustain_track |
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sustain_iso_code |
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sustain_region |
Region within a country. Only available for USA and Canada See the documentation of codecarbon for more information. https://mlco2.github.io/codecarbon/parameters.html |
sustain_interval |
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trace |
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pytorch_safetensors |
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This function does not return an object. Instead the configuration and the vocabulary of the new model are saved on disk.
To train the model, pass the directory of the model to the function train_tune_bert_model.
This models uses a WordPiece Tokenizer like BERT and can be trained with whole word masking. Transformer library may show a warning which can be ignored.
Devlin, J., Chang, M.‑W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In J. Burstein, C. Doran, & T. Solorio (Eds.), Proceedings of the 2019 Conference of the North (pp. 4171–4186). Association for Computational Linguistics. doi:10.18653/v1/N19-1423
Hugging Face documentation https://huggingface.co/docs/transformers/model_doc/bert#transformers.TFBertForMaskedLM
Other Transformer:
create_deberta_v2_model()
,
create_funnel_model()
,
create_longformer_model()
,
create_roberta_model()
,
train_tune_bert_model()
,
train_tune_deberta_v2_model()
,
train_tune_funnel_model()
,
train_tune_longformer_model()
,
train_tune_roberta_model()