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INFO in modeling.py --> Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex .
INFO in modeling_bert.py --> Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex .
INFO in modeling_xlnet.py --> Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex .
INFO in registrable.py --> instantiating registered subclass relu of <class 'allennlp.nn.activations.Activation'>
INFO in registrable.py --> instantiating registered subclass relu of <class 'allennlp.nn.activations.Activation'>
INFO in registrable.py --> instantiating registered subclass relu of <class 'allennlp.nn.activations.Activation'>
INFO in registrable.py --> instantiating registered subclass relu of <class 'allennlp.nn.activations.Activation'>
INFO in tokenization.py --> loading vocabulary file C:\Users\smaxbehr\AppData\Local\ChemDataExtractor\ChemDataExtractor\models/scibert_cased_vocab-1.0.txt
INFO in archival.py --> loading archive file C:\Users\smaxbehr\AppData\Local\ChemDataExtractor\ChemDataExtractor\models/bert_finetuned_crf_model-1.0a
WARNING in params.py --> _jsonnet not loaded, treating C:\Users\smaxbehr\AppData\Local\ChemDataExtractor\ChemDataExtractor\models/bert_finetuned_crf_model-1.0a\config.json as json
WARNING in params.py --> _jsonnet not loaded, treating snippet as json
INFO in registrable.py --> instantiating registered subclass bert_crf_tagger of <class 'allennlp.models.model.Model'>
INFO in params.py --> type = default
INFO in registrable.py --> instantiating registered subclass default of <class 'allennlp.data.vocabulary.Vocabulary'>
INFO in vocabulary.py --> Loading token dictionary from C:\Users\smaxbehr\AppData\Local\ChemDataExtractor\ChemDataExtractor\models/bert_finetuned_crf_model-1.0a\vocabulary.
INFO in from_params.py --> instantiating class <class 'allennlp.models.model.Model'> from params {'calculate_span_f1': True, 'dropout': 0.1, 'include_start_end_transitions': False, 'label_encoding': 'BIO', 'constrain_crf_decoding': True, 'type': 'bert_crf_tagger', 'text_field_embedder': {'embedder_to_indexer_map': {'bert': ['bert', 'bert-offsets']}, 'allow_unmatched_keys': True, 'token_embedders': {'bert': {'type': 'bert-pretrained', 'requires_grad': True, 'top_layer_only': True, 'pretrained_model': 'C:\\Users\\smaxbehr\\AppData\\Local\\ChemDataExtractor\\ChemDataExtractor\\models/scibert_cased_weights-1.0.tar.gz'}}}} and extras {'vocab'}
INFO in params.py --> model.type = bert_crf_tagger
INFO in from_params.py --> instantiating class <class 'chemdataextractor.nlp.finetuned_bert_crf_wrapper._BertCrfTagger'> from params {'calculate_span_f1': True, 'dropout': 0.1, 'include_start_end_transitions': False, 'label_encoding': 'BIO', 'constrain_crf_decoding': True, 'text_field_embedder': {'embedder_to_indexer_map': {'bert': ['bert', 'bert-offsets']}, 'allow_unmatched_keys': True, 'token_embedders': {'bert': {'type': 'bert-pretrained', 'requires_grad': True, 'top_layer_only': True, 'pretrained_model': 'C:\\Users\\smaxbehr\\AppData\\Local\\ChemDataExtractor\\ChemDataExtractor\\models/scibert_cased_weights-1.0.tar.gz'}}}} and extras {'vocab'}
INFO in from_params.py --> instantiating class <class 'allennlp.modules.text_field_embedders.text_field_embedder.TextFieldEmbedder'> from params {'embedder_to_indexer_map': {'bert': ['bert', 'bert-offsets']}, 'allow_unmatched_keys': True, 'token_embedders': {'bert': {'type': 'bert-pretrained', 'requires_grad': True, 'top_layer_only': True, 'pretrained_model': 'C:\\Users\\smaxbehr\\AppData\\Local\\ChemDataExtractor\\ChemDataExtractor\\models/scibert_cased_weights-1.0.tar.gz'}}} and extras {'vocab'}
INFO in params.py --> model.text_field_embedder.type = basic
INFO in params.py --> model.text_field_embedder.allow_unmatched_keys = True
INFO in from_params.py --> instantiating class <class 'allennlp.modules.token_embedders.token_embedder.TokenEmbedder'> from params {'type': 'bert-pretrained', 'requires_grad': True, 'top_layer_only': True, 'pretrained_model': 'C:\\Users\\smaxbehr\\AppData\\Local\\ChemDataExtractor\\ChemDataExtractor\\models/scibert_cased_weights-1.0.tar.gz'} and extras {'vocab'}
INFO in params.py --> model.text_field_embedder.token_embedders.bert.type = bert-pretrained
INFO in from_params.py --> instantiating class <class 'allennlp.modules.token_embedders.bert_token_embedder.PretrainedBertEmbedder'> from params {'requires_grad': True, 'top_layer_only': True, 'pretrained_model': 'C:\\Users\\smaxbehr\\AppData\\Local\\ChemDataExtractor\\ChemDataExtractor\\models/scibert_cased_weights-1.0.tar.gz'} and extras {'vocab'}
INFO in params.py --> model.text_field_embedder.token_embedders.bert.pretrained_model = C:\Users\smaxbehr\AppData\Local\ChemDataExtractor\ChemDataExtractor\models/scibert_cased_weights-1.0.tar.gz
INFO in params.py --> model.text_field_embedder.token_embedders.bert.requires_grad = True
INFO in params.py --> model.text_field_embedder.token_embedders.bert.top_layer_only = True
INFO in params.py --> model.text_field_embedder.token_embedders.bert.scalar_mix_parameters = None
INFO in modeling.py --> loading archive file C:\Users\smaxbehr\AppData\Local\ChemDataExtractor\ChemDataExtractor\models/scibert_cased_weights-1.0.tar.gz
INFO in modeling.py --> extracting archive file C:\Users\smaxbehr\AppData\Local\ChemDataExtractor\ChemDataExtractor\models/scibert_cased_weights-1.0.tar.gz to temp dir C:\Users\smaxbehr\AppData\Local\Temp\tmpv1hx463h
INFO in modeling.py --> Model config {
"attention_probs_dropout_prob": 0.1,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"max_position_embeddings": 512,
"num_attention_heads": 12,
"num_hidden_layers": 12,
"type_vocab_size": 2,
"vocab_size": 31116
}
INFO in params.py --> model.label_namespace = labels
INFO in params.py --> model.label_encoding = BIO
INFO in params.py --> model.include_start_end_transitions = False
INFO in params.py --> model.constrain_crf_decoding = True
INFO in params.py --> model.calculate_span_f1 = True
INFO in params.py --> model.dropout = 0.1
INFO in params.py --> model.verbose_metrics = False
INFO in initializers.py --> Initializing parameters
INFO in initializers.py --> Done initializing parameters; the following parameters are using their default initialization from their code
INFO in initializers.py --> crf._constraint_mask
INFO in initializers.py --> crf.transitions
INFO in initializers.py --> tag_projection_layer._module.bias
INFO in initializers.py --> tag_projection_layer._module.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.embeddings.LayerNorm.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.embeddings.LayerNorm.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.embeddings.position_embeddings.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.embeddings.token_type_embeddings.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.embeddings.word_embeddings.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.0.attention.output.LayerNorm.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.0.attention.output.LayerNorm.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.0.attention.output.dense.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.0.attention.output.dense.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.0.attention.self.key.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.0.attention.self.key.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.0.attention.self.query.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.0.attention.self.query.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.0.attention.self.value.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.0.attention.self.value.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.0.intermediate.dense.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.0.intermediate.dense.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.0.output.LayerNorm.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.0.output.LayerNorm.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.0.output.dense.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.0.output.dense.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.1.attention.output.LayerNorm.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.1.attention.output.LayerNorm.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.1.attention.output.dense.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.1.attention.output.dense.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.1.attention.self.key.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.1.attention.self.key.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.1.attention.self.query.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.1.attention.self.query.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.1.attention.self.value.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.1.attention.self.value.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.1.intermediate.dense.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.1.intermediate.dense.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.1.output.LayerNorm.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.1.output.LayerNorm.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.1.output.dense.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.1.output.dense.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.10.attention.output.LayerNorm.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.10.attention.output.LayerNorm.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.10.attention.output.dense.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.10.attention.output.dense.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.10.attention.self.key.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.10.attention.self.key.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.10.attention.self.query.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.10.attention.self.query.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.10.attention.self.value.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.10.attention.self.value.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.10.intermediate.dense.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.10.intermediate.dense.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.10.output.LayerNorm.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.10.output.LayerNorm.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.10.output.dense.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.10.output.dense.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.11.attention.output.LayerNorm.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.11.attention.output.LayerNorm.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.11.attention.output.dense.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.11.attention.output.dense.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.11.attention.self.key.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.11.attention.self.key.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.11.attention.self.query.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.11.attention.self.query.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.11.attention.self.value.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.11.attention.self.value.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.11.intermediate.dense.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.11.intermediate.dense.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.11.output.LayerNorm.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.11.output.LayerNorm.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.11.output.dense.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.11.output.dense.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.2.attention.output.LayerNorm.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.2.attention.output.LayerNorm.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.2.attention.output.dense.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.2.attention.output.dense.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.2.attention.self.key.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.2.attention.self.key.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.2.attention.self.query.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.2.attention.self.query.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.2.attention.self.value.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.2.attention.self.value.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.2.intermediate.dense.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.2.intermediate.dense.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.2.output.LayerNorm.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.2.output.LayerNorm.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.2.output.dense.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.2.output.dense.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.3.attention.output.LayerNorm.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.3.attention.output.LayerNorm.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.3.attention.output.dense.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.3.attention.output.dense.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.3.attention.self.key.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.3.attention.self.key.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.3.attention.self.query.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.3.attention.self.query.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.3.attention.self.value.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.3.attention.self.value.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.3.intermediate.dense.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.3.intermediate.dense.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.3.output.LayerNorm.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.3.output.LayerNorm.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.3.output.dense.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.3.output.dense.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.4.attention.output.LayerNorm.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.4.attention.output.LayerNorm.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.4.attention.output.dense.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.4.attention.output.dense.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.4.attention.self.key.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.4.attention.self.key.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.4.attention.self.query.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.4.attention.self.query.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.4.attention.self.value.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.4.attention.self.value.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.4.intermediate.dense.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.4.intermediate.dense.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.4.output.LayerNorm.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.4.output.LayerNorm.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.4.output.dense.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.4.output.dense.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.5.attention.output.LayerNorm.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.5.attention.output.LayerNorm.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.5.attention.output.dense.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.5.attention.output.dense.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.5.attention.self.key.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.5.attention.self.key.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.5.attention.self.query.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.5.attention.self.query.weight
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.5.attention.self.value.bias
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INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.5.intermediate.dense.bias
INFO in initializers.py --> text_field_embedder.token_embedder_bert.bert_model.encoder.layer.5.intermediate.dense.weight
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INFO in core.py --> Checking for updates to resources.json in case models have been updated. Note: this behavior can be turned off with download_method=None or download_method=DownloadMethod.REUSE_RESOURCES
INFO in core.py --> Loading these models for language: en (English):
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| Processor | Package |
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| tokenize | craft |
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INFO in core.py --> Using device: cpu
INFO in core.py --> Loading: tokenize
INFO in core.py --> Done loading processors!