create_roberta_model {aifeducation} | R Documentation |
This function creates a transformer configuration based on the RoBERTa base architecture and a vocabulary based on Byte-Pair Encoding (BPE) tokenizer by using the python libraries 'transformers' and 'tokenizers'.
create_roberta_model(
ml_framework = aifeducation_config$get_framework(),
model_dir,
vocab_raw_texts = NULL,
vocab_size = 30522,
add_prefix_space = FALSE,
trim_offsets = TRUE,
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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add_prefix_space |
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trim_offsets |
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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_roberta_model.
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. doi:10.48550/arXiv.1907.11692
Hugging Face Documentation https://huggingface.co/docs/transformers/model_doc/roberta#transformers.RobertaConfig
Other Transformer:
create_bert_model()
,
create_deberta_v2_model()
,
create_funnel_model()
,
create_longformer_model()
,
train_tune_bert_model()
,
train_tune_deberta_v2_model()
,
train_tune_funnel_model()
,
train_tune_longformer_model()
,
train_tune_roberta_model()