ef24657bab3db9373888da858308c0cde5295c6d,finetune/base_models/bert/model.py,DocRep,DocRep_1,#,212

Before Change

class DocRep(_BaseBert):
    encoder = RoBERTaEncoderV2
    featurizer = bert_featurizer
    settings = {
	"lm_type": "mlm",
        "n_embed": 768,
        "n_epochs": 32,
        "batch_size": 8,
        "n_heads": 12,
	"n_layer": 12,
        "act_fn": "gelu",
        "lr_warmup": 0.1,
	"lr": 1e-4,
        "l2_reg": 0.1,
	"epsilon": 1e-8,
        "bert_intermediate_size": 3072,
        "bert_use_pooler": False,
        "context_injection": True,
        "reading_order_removed": True,
        "context_channels": 192,
        "base_model_path": os.path.join("bert", "doc_rep_v1.jl"),
        "crf_sequence_labeling": False,
        "context_dim": 4,
        "default_context":{
            "left": 0,
            "right": 0,
            "top": 0,
            "bottom": 0,
        },
        "use_auxiliary_info": True

    }
    required_files = [
        {
            "file": os.path.join(FINETUNE_BASE_FOLDER, "model", "bert", "doc_rep_v1.jl"),
            "url": urljoin(BERT_BASE_URL, "doc_rep_v1.jl"),

After Change

class DocRep(_BaseBert):
    encoder = RoBERTaEncoderV2
    featurizer = bert_featurizer
    settings = dict(RoBERTa.settings)
    settings.update(
        {
	    "lr": 1e-4,
            "context_injection": True,
            "reading_order_removed": True,
            "context_channels": 192,
            "crf_sequence_labeling": False,
            "context_dim": 4,
            "default_context":{
                "left": 0,
                "right": 0,
                "top": 0,
                "bottom": 0,
            },
            "use_auxiliary_info": True,
            "low_memory_mode": True,
            "base_model_path": os.path.join("bert", "doc_rep_v1.jl"),
        }
    )
    required_files = [
        {
            "file": os.path.join(FINETUNE_BASE_FOLDER, "model", "bert", "doc_rep_v1.jl"),
            "url": urljoin(BERT_BASE_URL, "doc_rep_v1.jl"),

Instances