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2022-07-01 15:34:05,571 - __main__ - INFO - Label List:['O', 'B-PERSON', 'I-PERSON', 'B-NORP', 'I-NORP', 'B-FAC', 'I-FAC', 'B-ORG', 'I-ORG', 'B-GPE', 'I-GPE', 'B-LOC', 'I-LOC', 'B-PRODUCT', 'I-PRODUCT', 'B-DATE', 'I-DATE', 'B-TIME', 'I-TIME', 'B-PERCENT', 'I-PERCENT', 'B-MONEY', 'I-MONEY', 'B-QUANTITY', 'I-QUANTITY', 'B-ORDINAL', 'I-ORDINAL', 'B-CARDINAL', 'I-CARDINAL', 'B-EVENT', 'I-EVENT', 'B-WORK_OF_ART', 'I-WORK_OF_ART', 'B-LAW', 'I-LAW', 'B-LANGUAGE', 'I-LANGUAGE'] |
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2022-07-01 15:34:14,083 - __main__ - INFO - Dataset({ |
|
features: ['id', 'words', 'ner_tags'], |
|
num_rows: 75187 |
|
}) |
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2022-07-01 15:34:14,812 - __main__ - INFO - Dataset({ |
|
features: ['id', 'words', 'ner_tags'], |
|
num_rows: 9479 |
|
}) |
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2022-07-01 15:34:14,815 - transformers.tokenization_utils_base - INFO - Didn't find file models/roberta-base_1656662418.0944197/checkpoint-14100/added_tokens.json. We won't load it. |
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2022-07-01 15:34:14,816 - transformers.tokenization_utils_base - INFO - loading file models/roberta-base_1656662418.0944197/checkpoint-14100/vocab.json |
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2022-07-01 15:34:14,816 - transformers.tokenization_utils_base - INFO - loading file models/roberta-base_1656662418.0944197/checkpoint-14100/merges.txt |
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2022-07-01 15:34:14,816 - transformers.tokenization_utils_base - INFO - loading file models/roberta-base_1656662418.0944197/checkpoint-14100/tokenizer.json |
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2022-07-01 15:34:14,816 - transformers.tokenization_utils_base - INFO - loading file None |
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2022-07-01 15:34:14,816 - transformers.tokenization_utils_base - INFO - loading file models/roberta-base_1656662418.0944197/checkpoint-14100/special_tokens_map.json |
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2022-07-01 15:34:14,816 - transformers.tokenization_utils_base - INFO - loading file models/roberta-base_1656662418.0944197/checkpoint-14100/tokenizer_config.json |
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2022-07-01 15:34:14,880 - __main__ - INFO - {'input_ids': [[0, 653, 761, 9, 3783, 17487, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 166, 32928, 9603, 47, 7, 1183, 10, 780, 5403, 9, 15581, 436, 479, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 15584, 3082, 3192, 23959, 15, 5, 2860, 3875, 9, 436, 4832, 41876, 38628, 9, 15643, 24610, 4743, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 15125, 6764, 15, 15643, 24610, 4743, 16, 5, 23001, 7, 5, 41184, 6304, 25132, 23909, 479, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 85, 16, 14092, 9, 10, 2270, 11235, 459, 2156, 5929, 1690, 523, 293, 2156, 10, 1307, 1062, 18185, 8, 30943, 9368, 2156, 8, 5, 2860, 2298, 2156, 566, 97, 383, 479, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} |
|
2022-07-01 15:34:14,880 - __main__ - INFO - ['<s>', 'ĠWhat', 'Ġkind', 'Ġof', 'Ġmemory', 'Ġ?', '</s>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>'] |
|
2022-07-01 15:34:14,880 - __main__ - INFO - ['<s>', 'ĠWe', 'Ġrespectfully', 'Ġinvite', 'Ġyou', 'Ġto', 'Ġwatch', 'Ġa', 'Ġspecial', 'Ġedition', 'Ġof', 'ĠAcross', 'ĠChina', 'Ġ.', '</s>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>'] |
|
2022-07-01 15:34:14,881 - __main__ - INFO - ['<s>', 'ĠWW', 'ĠII', 'ĠLand', 'marks', 'Ġon', 'Ġthe', 'ĠGreat', 'ĠEarth', 'Ġof', 'ĠChina', 'Ġ:', 'ĠEternal', 'ĠMemories', 'Ġof', 'ĠTai', 'hang', 'ĠMountain', '</s>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>'] |
|
2022-07-01 15:34:14,881 - __main__ - INFO - ['<s>', 'ĠStanding', 'Ġtall', 'Ġon', 'ĠTai', 'hang', 'ĠMountain', 'Ġis', 'Ġthe', 'ĠMonument', 'Ġto', 'Ġthe', 'ĠHundred', 'ĠReg', 'iments', 'ĠOffensive', 'Ġ.', '</s>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>'] |
|
2022-07-01 15:34:14,881 - __main__ - INFO - ['<s>', 'ĠIt', 'Ġis', 'Ġcomposed', 'Ġof', 'Ġa', 'Ġprimary', 'Ġste', 'le', 'Ġ,', 'Ġsecondary', 'Ġst', 'el', 'es', 'Ġ,', 'Ġa', 'Ġhuge', 'Ġround', 'Ġsculpture', 'Ġand', 'Ġbeacon', 'Ġtower', 'Ġ,', 'Ġand', 'Ġthe', 'ĠGreat', 'ĠWall', 'Ġ,', 'Ġamong', 'Ġother', 'Ġthings', 'Ġ.', '</s>'] |
|
2022-07-01 15:34:14,881 - __main__ - INFO - |
|
2022-07-01 15:34:14,881 - __main__ - INFO - ['<s>', 'ĠWe', 'Ġrespectfully', 'Ġinvite', 'Ġyou', 'Ġto', 'Ġwatch', 'Ġa', 'Ġspecial', 'Ġedition', 'Ġof', 'ĠAcross', 'ĠChina', 'Ġ.', '</s>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>'] |
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2022-07-01 15:34:14,882 - __main__ - INFO - [None, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None] |
|
2022-07-01 15:34:14,885 - datasets.fingerprint - WARNING - Parameter 'function'=<function tokenize_and_align_labels at 0x7f8a97a8adc0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed. |
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2022-07-01 15:34:20,039 - __main__ - INFO - {'id': [0, 1, 2, 3, 4], 'words': [['What', 'kind', 'of', 'memory', '?'], ['We', 'respectfully', 'invite', 'you', 'to', 'watch', 'a', 'special', 'edition', 'of', 'Across', 'China', '.'], ['WW', 'II', 'Landmarks', 'on', 'the', 'Great', 'Earth', 'of', 'China', ':', 'Eternal', 'Memories', 'of', 'Taihang', 'Mountain'], ['Standing', 'tall', 'on', 'Taihang', 'Mountain', 'is', 'the', 'Monument', 'to', 'the', 'Hundred', 'Regiments', 'Offensive', '.'], ['It', 'is', 'composed', 'of', 'a', 'primary', 'stele', ',', 'secondary', 'steles', ',', 'a', 'huge', 'round', 'sculpture', 'and', 'beacon', 'tower', ',', 'and', 'the', 'Great', 'Wall', ',', 'among', 'other', 'things', '.']], 'ner_tags': [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0], [31, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32], [0, 0, 0, 11, 12, 0, 31, 32, 32, 32, 32, 32, 32, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 31, 32, 32, 0, 0, 0, 0, 0]], 'input_ids': [[0, 653, 761, 9, 3783, 17487, 2], [0, 166, 32928, 9603, 47, 7, 1183, 10, 780, 5403, 9, 15581, 436, 479, 2], [0, 15584, 3082, 3192, 23959, 15, 5, 2860, 3875, 9, 436, 4832, 41876, 38628, 9, 15643, 24610, 4743, 2], [0, 15125, 6764, 15, 15643, 24610, 4743, 16, 5, 23001, 7, 5, 41184, 6304, 25132, 23909, 479, 2], [0, 85, 16, 14092, 9, 10, 2270, 11235, 459, 2156, 5929, 1690, 523, 293, 2156, 10, 1307, 1062, 18185, 8, 30943, 9368, 2156, 8, 5, 2860, 2298, 2156, 566, 97, 383, 479, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'labels': [[-100, 0, 0, 0, 0, 0, -100], [-100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, -100], [-100, 31, 32, 32, -100, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, -100, 32, -100], [-100, 0, 0, 0, 11, -100, 12, 0, 31, 32, 32, 32, 32, 32, -100, 32, 0, -100], [-100, 0, 0, 0, 0, 0, 0, 0, -100, 0, 0, 0, -100, -100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 31, 32, 32, 0, 0, 0, 0, 0, -100]]} |
|
2022-07-01 15:34:22,155 - transformers.configuration_utils - INFO - loading configuration file models/roberta-base_1656662418.0944197/checkpoint-14100/config.json |
|
2022-07-01 15:34:22,162 - transformers.configuration_utils - INFO - Model config RobertaConfig { |
|
"_name_or_path": "models/roberta-base_1656662418.0944197/checkpoint-14100", |
|
"architectures": [ |
|
"RobertaForTokenClassification" |
|
], |
|
"attention_probs_dropout_prob": 0.1, |
|
"bos_token_id": 0, |
|
"classifier_dropout": null, |
|
"eos_token_id": 2, |
|
"hidden_act": "gelu", |
|
"hidden_dropout_prob": 0.1, |
|
"hidden_size": 768, |
|
"id2label": { |
|
"0": "O", |
|
"1": "B-PERSON", |
|
"2": "I-PERSON", |
|
"3": "B-NORP", |
|
"4": "I-NORP", |
|
"5": "B-FAC", |
|
"6": "I-FAC", |
|
"7": "B-ORG", |
|
"8": "I-ORG", |
|
"9": "B-GPE", |
|
"10": "I-GPE", |
|
"11": "B-LOC", |
|
"12": "I-LOC", |
|
"13": "B-PRODUCT", |
|
"14": "I-PRODUCT", |
|
"15": "B-DATE", |
|
"16": "I-DATE", |
|
"17": "B-TIME", |
|
"18": "I-TIME", |
|
"19": "B-PERCENT", |
|
"20": "I-PERCENT", |
|
"21": "B-MONEY", |
|
"22": "I-MONEY", |
|
"23": "B-QUANTITY", |
|
"24": "I-QUANTITY", |
|
"25": "B-ORDINAL", |
|
"26": "I-ORDINAL", |
|
"27": "B-CARDINAL", |
|
"28": "I-CARDINAL", |
|
"29": "B-EVENT", |
|
"30": "I-EVENT", |
|
"31": "B-WORK_OF_ART", |
|
"32": "I-WORK_OF_ART", |
|
"33": "B-LAW", |
|
"34": "I-LAW", |
|
"35": "B-LANGUAGE", |
|
"36": "I-LANGUAGE" |
|
}, |
|
"initializer_range": 0.02, |
|
"intermediate_size": 3072, |
|
"label2id": { |
|
"B-CARDINAL": 27, |
|
"B-DATE": 15, |
|
"B-EVENT": 29, |
|
"B-FAC": 5, |
|
"B-GPE": 9, |
|
"B-LANGUAGE": 35, |
|
"B-LAW": 33, |
|
"B-LOC": 11, |
|
"B-MONEY": 21, |
|
"B-NORP": 3, |
|
"B-ORDINAL": 25, |
|
"B-ORG": 7, |
|
"B-PERCENT": 19, |
|
"B-PERSON": 1, |
|
"B-PRODUCT": 13, |
|
"B-QUANTITY": 23, |
|
"B-TIME": 17, |
|
"B-WORK_OF_ART": 31, |
|
"I-CARDINAL": 28, |
|
"I-DATE": 16, |
|
"I-EVENT": 30, |
|
"I-FAC": 6, |
|
"I-GPE": 10, |
|
"I-LANGUAGE": 36, |
|
"I-LAW": 34, |
|
"I-LOC": 12, |
|
"I-MONEY": 22, |
|
"I-NORP": 4, |
|
"I-ORDINAL": 26, |
|
"I-ORG": 8, |
|
"I-PERCENT": 20, |
|
"I-PERSON": 2, |
|
"I-PRODUCT": 14, |
|
"I-QUANTITY": 24, |
|
"I-TIME": 18, |
|
"I-WORK_OF_ART": 32, |
|
"O": 0 |
|
}, |
|
"layer_norm_eps": 1e-05, |
|
"max_position_embeddings": 514, |
|
"model_type": "roberta", |
|
"num_attention_heads": 12, |
|
"num_hidden_layers": 12, |
|
"pad_token_id": 1, |
|
"position_embedding_type": "absolute", |
|
"torch_dtype": "float32", |
|
"transformers_version": "4.20.0", |
|
"type_vocab_size": 1, |
|
"use_cache": true, |
|
"vocab_size": 50265 |
|
} |
|
|
|
2022-07-01 15:34:22,259 - transformers.modeling_utils - INFO - loading weights file models/roberta-base_1656662418.0944197/checkpoint-14100/pytorch_model.bin |
|
2022-07-01 15:34:23,639 - transformers.modeling_utils - INFO - All model checkpoint weights were used when initializing RobertaForTokenClassification. |
|
|
|
2022-07-01 15:34:23,640 - transformers.modeling_utils - INFO - All the weights of RobertaForTokenClassification were initialized from the model checkpoint at models/roberta-base_1656662418.0944197/checkpoint-14100. |
|
If your task is similar to the task the model of the checkpoint was trained on, you can already use RobertaForTokenClassification for predictions without further training. |
|
2022-07-01 15:34:23,646 - __main__ - INFO - RobertaForTokenClassification( |
|
(roberta): RobertaModel( |
|
(embeddings): RobertaEmbeddings( |
|
(word_embeddings): Embedding(50265, 768, padding_idx=1) |
|
(position_embeddings): Embedding(514, 768, padding_idx=1) |
|
(token_type_embeddings): Embedding(1, 768) |
|
(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
(encoder): RobertaEncoder( |
|
(layer): ModuleList( |
|
(0): RobertaLayer( |
|
(attention): RobertaAttention( |
|
(self): RobertaSelfAttention( |
|
(query): Linear(in_features=768, out_features=768, bias=True) |
|
(key): Linear(in_features=768, out_features=768, bias=True) |
|
(value): Linear(in_features=768, out_features=768, bias=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
(output): RobertaSelfOutput( |
|
(dense): Linear(in_features=768, out_features=768, bias=True) |
|
(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(intermediate): RobertaIntermediate( |
|
(dense): Linear(in_features=768, out_features=3072, bias=True) |
|
(intermediate_act_fn): GELUActivation() |
|
) |
|
(output): RobertaOutput( |
|
(dense): Linear(in_features=3072, out_features=768, bias=True) |
|
(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(1): RobertaLayer( |
|
(attention): RobertaAttention( |
|
(self): RobertaSelfAttention( |
|
(query): Linear(in_features=768, out_features=768, bias=True) |
|
(key): Linear(in_features=768, out_features=768, bias=True) |
|
(value): Linear(in_features=768, out_features=768, bias=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
(output): RobertaSelfOutput( |
|
(dense): Linear(in_features=768, out_features=768, bias=True) |
|
(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(intermediate): RobertaIntermediate( |
|
(dense): Linear(in_features=768, out_features=3072, bias=True) |
|
(intermediate_act_fn): GELUActivation() |
|
) |
|
(output): RobertaOutput( |
|
(dense): Linear(in_features=3072, out_features=768, bias=True) |
|
(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(2): RobertaLayer( |
|
(attention): RobertaAttention( |
|
(self): RobertaSelfAttention( |
|
(query): Linear(in_features=768, out_features=768, bias=True) |
|
(key): Linear(in_features=768, out_features=768, bias=True) |
|
(value): Linear(in_features=768, out_features=768, bias=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
(output): RobertaSelfOutput( |
|
(dense): Linear(in_features=768, out_features=768, bias=True) |
|
(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(intermediate): RobertaIntermediate( |
|
(dense): Linear(in_features=768, out_features=3072, bias=True) |
|
(intermediate_act_fn): GELUActivation() |
|
) |
|
(output): RobertaOutput( |
|
(dense): Linear(in_features=3072, out_features=768, bias=True) |
|
(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(3): RobertaLayer( |
|
(attention): RobertaAttention( |
|
(self): RobertaSelfAttention( |
|
(query): Linear(in_features=768, out_features=768, bias=True) |
|
(key): Linear(in_features=768, out_features=768, bias=True) |
|
(value): Linear(in_features=768, out_features=768, bias=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
(output): RobertaSelfOutput( |
|
(dense): Linear(in_features=768, out_features=768, bias=True) |
|
(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(intermediate): RobertaIntermediate( |
|
(dense): Linear(in_features=768, out_features=3072, bias=True) |
|
(intermediate_act_fn): GELUActivation() |
|
) |
|
(output): RobertaOutput( |
|
(dense): Linear(in_features=3072, out_features=768, bias=True) |
|
(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(4): RobertaLayer( |
|
(attention): RobertaAttention( |
|
(self): RobertaSelfAttention( |
|
(query): Linear(in_features=768, out_features=768, bias=True) |
|
(key): Linear(in_features=768, out_features=768, bias=True) |
|
(value): Linear(in_features=768, out_features=768, bias=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
(output): RobertaSelfOutput( |
|
(dense): Linear(in_features=768, out_features=768, bias=True) |
|
(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(intermediate): RobertaIntermediate( |
|
(dense): Linear(in_features=768, out_features=3072, bias=True) |
|
(intermediate_act_fn): GELUActivation() |
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) |
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(output): RobertaOutput( |
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(dense): Linear(in_features=3072, out_features=768, bias=True) |
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(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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) |
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(5): RobertaLayer( |
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(attention): RobertaAttention( |
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(self): RobertaSelfAttention( |
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(query): Linear(in_features=768, out_features=768, bias=True) |
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(key): Linear(in_features=768, out_features=768, bias=True) |
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(value): Linear(in_features=768, out_features=768, bias=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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(output): RobertaSelfOutput( |
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(dense): Linear(in_features=768, out_features=768, bias=True) |
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(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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) |
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(intermediate): RobertaIntermediate( |
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(dense): Linear(in_features=768, out_features=3072, bias=True) |
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(intermediate_act_fn): GELUActivation() |
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) |
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(output): RobertaOutput( |
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(dense): Linear(in_features=3072, out_features=768, bias=True) |
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(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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) |
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(6): RobertaLayer( |
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(attention): RobertaAttention( |
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(self): RobertaSelfAttention( |
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(query): Linear(in_features=768, out_features=768, bias=True) |
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(key): Linear(in_features=768, out_features=768, bias=True) |
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(value): Linear(in_features=768, out_features=768, bias=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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(output): RobertaSelfOutput( |
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(dense): Linear(in_features=768, out_features=768, bias=True) |
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(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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) |
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(intermediate): RobertaIntermediate( |
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(dense): Linear(in_features=768, out_features=3072, bias=True) |
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(intermediate_act_fn): GELUActivation() |
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) |
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(output): RobertaOutput( |
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(dense): Linear(in_features=3072, out_features=768, bias=True) |
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(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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) |
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(7): RobertaLayer( |
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(attention): RobertaAttention( |
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(self): RobertaSelfAttention( |
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(query): Linear(in_features=768, out_features=768, bias=True) |
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(key): Linear(in_features=768, out_features=768, bias=True) |
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(value): Linear(in_features=768, out_features=768, bias=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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(output): RobertaSelfOutput( |
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(dense): Linear(in_features=768, out_features=768, bias=True) |
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(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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) |
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(intermediate): RobertaIntermediate( |
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(dense): Linear(in_features=768, out_features=3072, bias=True) |
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(intermediate_act_fn): GELUActivation() |
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) |
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(output): RobertaOutput( |
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(dense): Linear(in_features=3072, out_features=768, bias=True) |
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(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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) |
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(8): RobertaLayer( |
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(attention): RobertaAttention( |
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(self): RobertaSelfAttention( |
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(query): Linear(in_features=768, out_features=768, bias=True) |
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(key): Linear(in_features=768, out_features=768, bias=True) |
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(value): Linear(in_features=768, out_features=768, bias=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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(output): RobertaSelfOutput( |
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(dense): Linear(in_features=768, out_features=768, bias=True) |
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(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
|
) |
|
(intermediate): RobertaIntermediate( |
|
(dense): Linear(in_features=768, out_features=3072, bias=True) |
|
(intermediate_act_fn): GELUActivation() |
|
) |
|
(output): RobertaOutput( |
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(dense): Linear(in_features=3072, out_features=768, bias=True) |
|
(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
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(dropout): Dropout(p=0.1, inplace=False) |
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) |
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) |
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(9): RobertaLayer( |
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(attention): RobertaAttention( |
|
(self): RobertaSelfAttention( |
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(query): Linear(in_features=768, out_features=768, bias=True) |
|
(key): Linear(in_features=768, out_features=768, bias=True) |
|
(value): Linear(in_features=768, out_features=768, bias=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
(output): RobertaSelfOutput( |
|
(dense): Linear(in_features=768, out_features=768, bias=True) |
|
(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(intermediate): RobertaIntermediate( |
|
(dense): Linear(in_features=768, out_features=3072, bias=True) |
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(intermediate_act_fn): GELUActivation() |
|
) |
|
(output): RobertaOutput( |
|
(dense): Linear(in_features=3072, out_features=768, bias=True) |
|
(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
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(10): RobertaLayer( |
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(attention): RobertaAttention( |
|
(self): RobertaSelfAttention( |
|
(query): Linear(in_features=768, out_features=768, bias=True) |
|
(key): Linear(in_features=768, out_features=768, bias=True) |
|
(value): Linear(in_features=768, out_features=768, bias=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
(output): RobertaSelfOutput( |
|
(dense): Linear(in_features=768, out_features=768, bias=True) |
|
(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(intermediate): RobertaIntermediate( |
|
(dense): Linear(in_features=768, out_features=3072, bias=True) |
|
(intermediate_act_fn): GELUActivation() |
|
) |
|
(output): RobertaOutput( |
|
(dense): Linear(in_features=3072, out_features=768, bias=True) |
|
(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(11): RobertaLayer( |
|
(attention): RobertaAttention( |
|
(self): RobertaSelfAttention( |
|
(query): Linear(in_features=768, out_features=768, bias=True) |
|
(key): Linear(in_features=768, out_features=768, bias=True) |
|
(value): Linear(in_features=768, out_features=768, bias=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
(output): RobertaSelfOutput( |
|
(dense): Linear(in_features=768, out_features=768, bias=True) |
|
(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
(intermediate): RobertaIntermediate( |
|
(dense): Linear(in_features=768, out_features=3072, bias=True) |
|
(intermediate_act_fn): GELUActivation() |
|
) |
|
(output): RobertaOutput( |
|
(dense): Linear(in_features=3072, out_features=768, bias=True) |
|
(LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
) |
|
) |
|
) |
|
) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
(classifier): Linear(in_features=768, out_features=37, bias=True) |
|
) |
|
2022-07-01 15:34:23,647 - __main__ - INFO - CONFIGS:{ |
|
"output_dir": "./models/fine-tunned-roberta-14100_1656669845.567399", |
|
"per_device_train_batch_size": 16, |
|
"per_device_eval_batch_size": 16, |
|
"save_total_limit": 2, |
|
"num_train_epochs": 3, |
|
"seed": 1, |
|
"load_best_model_at_end": true, |
|
"evaluation_strategy": "epoch", |
|
"save_strategy": "epoch", |
|
"learning_rate": 2e-05, |
|
"weight_decay": 0.01, |
|
"logging_steps": 469.0 |
|
} |
|
2022-07-01 15:34:23,647 - transformers.training_args - INFO - PyTorch: setting up devices |
|
2022-07-01 15:34:23,702 - transformers.training_args - INFO - The default value for the training argument ` |
|
2022-07-01 15:34:27,143 - __main__ - INFO - [[ MODEL EVALUATION ]] |
|
2022-07-01 15:34:27,143 - transformers.trainer - INFO - The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: id, ner_tags, words. If id, ner_tags, words are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message. |
|
2022-07-01 15:34:27,146 - transformers.trainer - INFO - ***** Running Evaluation ***** |
|
2022-07-01 15:34:27,146 - transformers.trainer - INFO - Num examples = 9479 |
|
2022-07-01 15:34:27,146 - transformers.trainer - INFO - Batch size = 16 |
|
2022-07-01 15:35:42,947 - __main__ - INFO - {'eval_loss': 0.06502678245306015, 'eval_precision': 0.888830852267781, 'eval_recall': 0.9069912054721506, 'eval_f1': 0.8978192050650721, 'eval_accuracy': 0.9842020533202814, 'eval_runtime': 75.7916, 'eval_samples_per_second': 125.067, 'eval_steps_per_second': 7.824, 'step': 0} |
|
2022-07-01 15:35:42,947 - transformers.trainer - INFO - The following columns in the test set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: id, ner_tags, words. If id, ner_tags, words are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message. |
|
2022-07-01 15:35:42,949 - transformers.trainer - INFO - ***** Running Prediction ***** |
|
2022-07-01 15:35:42,949 - transformers.trainer - INFO - Num examples = 9479 |
|
2022-07-01 15:35:42,950 - transformers.trainer - INFO - Batch size = 16 |
|
2022-07-01 15:37:01,513 - __main__ - INFO - precision recall f1-score support |
|
|
|
CARDINAL 0.84 0.85 0.85 935 |
|
DATE 0.85 0.90 0.87 1602 |
|
EVENT 0.67 0.76 0.71 63 |
|
FAC 0.74 0.72 0.73 135 |
|
GPE 0.97 0.96 0.96 2240 |
|
LANGUAGE 0.83 0.68 0.75 22 |
|
LAW 0.66 0.62 0.64 40 |
|
LOC 0.74 0.80 0.77 179 |
|
MONEY 0.85 0.89 0.87 314 |
|
NORP 0.93 0.96 0.95 841 |
|
ORDINAL 0.81 0.89 0.85 195 |
|
ORG 0.90 0.91 0.91 1795 |
|
PERCENT 0.90 0.92 0.91 349 |
|
PERSON 0.95 0.95 0.95 1988 |
|
PRODUCT 0.74 0.83 0.78 76 |
|
QUANTITY 0.76 0.80 0.78 105 |
|
TIME 0.62 0.67 0.65 212 |
|
WORK_OF_ART 0.58 0.69 0.63 166 |
|
|
|
micro avg 0.89 0.91 0.90 11257 |
|
macro avg 0.80 0.82 0.81 11257 |
|
weighted avg 0.89 0.91 0.90 11257 |
|
|
|
|
