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2022-07-03 15:51:20,416 - __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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2022-07-03 15:51:26,630 - __main__ - INFO - Dataset({ |
|
features: ['id', 'words', 'ner_tags'], |
|
num_rows: 75187 |
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}) |
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2022-07-03 15:51:27,367 - __main__ - INFO - Dataset({ |
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features: ['id', 'words', 'ner_tags'], |
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num_rows: 9479 |
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}) |
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2022-07-03 15:51:27,370 - transformers.tokenization_utils_base - INFO - Didn't find file models/albert-base-v2_1656839871.089586/checkpoint-14100/spiece.model. We won't load it. |
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2022-07-03 15:51:27,370 - transformers.tokenization_utils_base - INFO - Didn't find file models/albert-base-v2_1656839871.089586/checkpoint-14100/added_tokens.json. We won't load it. |
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2022-07-03 15:51:27,371 - transformers.tokenization_utils_base - INFO - loading file None |
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2022-07-03 15:51:27,371 - transformers.tokenization_utils_base - INFO - loading file models/albert-base-v2_1656839871.089586/checkpoint-14100/tokenizer.json |
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2022-07-03 15:51:27,371 - transformers.tokenization_utils_base - INFO - loading file None |
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2022-07-03 15:51:27,371 - transformers.tokenization_utils_base - INFO - loading file models/albert-base-v2_1656839871.089586/checkpoint-14100/special_tokens_map.json |
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2022-07-03 15:51:27,372 - transformers.tokenization_utils_base - INFO - loading file models/albert-base-v2_1656839871.089586/checkpoint-14100/tokenizer_config.json |
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2022-07-03 15:51:27,422 - __main__ - INFO - {'input_ids': [[2, 98, 825, 16, 1912, 13, 60, 3, 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, 0, 0, 0, 0], [2, 95, 22719, 102, 10275, 42, 20, 1455, 21, 621, 1322, 16, 464, 998, 13, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 13, 14178, 595, 19045, 27, 14, 374, 1073, 16, 998, 13, 45, 10987, 4584, 16, 5466, 7065, 1286, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 1288, 2263, 27, 5466, 7065, 1286, 25, 14, 4908, 20, 14, 1874, 12272, 4632, 13, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 32, 25, 1869, 16, 21, 1256, 13, 18, 14305, 13, 15, 2277, 6621, 1355, 13, 15, 21, 2329, 560, 5515, 17, 13339, 1710, 13, 15, 17, 14, 374, 769, 13, 15, 497, 89, 564, 13, 9, 3]], 'token_type_ids': [[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, 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, 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, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[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, 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, 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, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} |
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2022-07-03 15:51:27,422 - __main__ - INFO - ['[CLS]', '▁what', '▁kind', '▁of', '▁memory', '▁', '?', '[SEP]', '<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>', '<pad>', '<pad>', '<pad>', '<pad>'] |
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2022-07-03 15:51:27,422 - __main__ - INFO - ['[CLS]', '▁we', '▁respectful', 'ly', '▁invite', '▁you', '▁to', '▁watch', '▁a', '▁special', '▁edition', '▁of', '▁across', '▁china', '▁', '.', '[SEP]', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>'] |
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2022-07-03 15:51:27,423 - __main__ - INFO - ['[CLS]', '▁', 'ww', '▁ii', '▁landmarks', '▁on', '▁the', '▁great', '▁earth', '▁of', '▁china', '▁', ':', '▁eternal', '▁memories', '▁of', '▁tai', 'hang', '▁mountain', '[SEP]', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>'] |
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2022-07-03 15:51:27,423 - __main__ - INFO - ['[CLS]', '▁standing', '▁tall', '▁on', '▁tai', 'hang', '▁mountain', '▁is', '▁the', '▁monument', '▁to', '▁the', '▁hundred', '▁regiments', '▁offensive', '▁', '.', '[SEP]', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>'] |
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2022-07-03 15:51:27,423 - __main__ - INFO - ['[CLS]', '▁it', '▁is', '▁composed', '▁of', '▁a', '▁primary', '▁', 's', 'tele', '▁', ',', '▁secondary', '▁ste', 'les', '▁', ',', '▁a', '▁huge', '▁round', '▁sculpture', '▁and', '▁beacon', '▁tower', '▁', ',', '▁and', '▁the', '▁great', '▁wall', '▁', ',', '▁among', '▁other', '▁things', '▁', '.', '[SEP]'] |
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2022-07-03 15:51:27,423 - __main__ - INFO - |
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2022-07-03 15:51:27,423 - __main__ - INFO - ['[CLS]', '▁we', '▁respectful', 'ly', '▁invite', '▁you', '▁to', '▁watch', '▁a', '▁special', '▁edition', '▁of', '▁across', '▁china', '▁', '.', '[SEP]', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>', '<pad>'] |
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2022-07-03 15:51:27,423 - __main__ - INFO - [None, 0, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 12, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None] |
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2022-07-03 15:51:27,427 - datasets.fingerprint - WARNING - Parameter 'function'=<function tokenize_and_align_labels at 0x7f8c9a20af70> 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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2022-07-03 15:51:32,943 - __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': [[2, 98, 825, 16, 1912, 13, 60, 3], [2, 95, 22719, 102, 10275, 42, 20, 1455, 21, 621, 1322, 16, 464, 998, 13, 9, 3], [2, 13, 14178, 595, 19045, 27, 14, 374, 1073, 16, 998, 13, 45, 10987, 4584, 16, 5466, 7065, 1286, 3], [2, 1288, 2263, 27, 5466, 7065, 1286, 25, 14, 4908, 20, 14, 1874, 12272, 4632, 13, 9, 3], [2, 32, 25, 1869, 16, 21, 1256, 13, 18, 14305, 13, 15, 2277, 6621, 1355, 13, 15, 21, 2329, 560, 5515, 17, 13339, 1710, 13, 15, 17, 14, 374, 769, 13, 15, 497, 89, 564, 13, 9, 3]], 'token_type_ids': [[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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '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, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'labels': [[-100, 0, 0, 0, 0, 0, -100, -100], [-100, 0, 0, -100, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, -100, -100], [-100, 31, -100, 32, 32, 32, 32, 32, 32, 32, 32, 32, -100, 32, 32, 32, 32, -100, 32, -100], [-100, 0, 0, 0, 11, -100, 12, 0, 31, 32, 32, 32, 32, 32, 32, 0, -100, -100], [-100, 0, 0, 0, 0, 0, 0, 0, -100, -100, 0, -100, 0, 0, -100, 0, -100, 0, 0, 0, 0, 0, 0, 0, 0, -100, 0, 31, 32, 32, 0, -100, 0, 0, 0, 0, -100, -100]]} |
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2022-07-03 15:51:35,822 - transformers.configuration_utils - INFO - loading configuration file models/albert-base-v2_1656839871.089586/checkpoint-14100/config.json |
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2022-07-03 15:51:35,828 - transformers.configuration_utils - INFO - Model config AlbertConfig { |
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"_name_or_path": "models/albert-base-v2_1656839871.089586/checkpoint-14100", |
|
"architectures": [ |
|
"AlbertForTokenClassification" |
|
], |
|
"attention_probs_dropout_prob": 0, |
|
"bos_token_id": 2, |
|
"classifier_dropout_prob": 0.1, |
|
"down_scale_factor": 1, |
|
"embedding_size": 128, |
|
"eos_token_id": 3, |
|
"gap_size": 0, |
|
"hidden_act": "gelu_new", |
|
"hidden_dropout_prob": 0, |
|
"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, |
|
"inner_group_num": 1, |
|
"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-12, |
|
"max_position_embeddings": 512, |
|
"model_type": "albert", |
|
"net_structure_type": 0, |
|
"num_attention_heads": 12, |
|
"num_hidden_groups": 1, |
|
"num_hidden_layers": 12, |
|
"num_memory_blocks": 0, |
|
"pad_token_id": 0, |
|
"position_embedding_type": "absolute", |
|
"torch_dtype": "float32", |
|
"transformers_version": "4.20.0", |
|
"type_vocab_size": 2, |
|
"vocab_size": 30000 |
|
} |
|
|
|
2022-07-03 15:51:35,912 - transformers.modeling_utils - INFO - loading weights file models/albert-base-v2_1656839871.089586/checkpoint-14100/pytorch_model.bin |
|
2022-07-03 15:51:36,021 - transformers.modeling_utils - INFO - All model checkpoint weights were used when initializing AlbertForTokenClassification. |
|
|
|
2022-07-03 15:51:36,022 - transformers.modeling_utils - INFO - All the weights of AlbertForTokenClassification were initialized from the model checkpoint at models/albert-base-v2_1656839871.089586/checkpoint-14100. |
|
If your task is similar to the task the model of the checkpoint was trained on, you can already use AlbertForTokenClassification for predictions without further training. |
|
2022-07-03 15:51:36,022 - __main__ - INFO - AlbertForTokenClassification( |
|
(albert): AlbertModel( |
|
(embeddings): AlbertEmbeddings( |
|
(word_embeddings): Embedding(30000, 128, padding_idx=0) |
|
(position_embeddings): Embedding(512, 128) |
|
(token_type_embeddings): Embedding(2, 128) |
|
(LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True) |
|
(dropout): Dropout(p=0, inplace=False) |
|
) |
|
(encoder): AlbertTransformer( |
|
(embedding_hidden_mapping_in): Linear(in_features=128, out_features=768, bias=True) |
|
(albert_layer_groups): ModuleList( |
|
(0): AlbertLayerGroup( |
|
(albert_layers): ModuleList( |
|
(0): AlbertLayer( |
|
(full_layer_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
|
(attention): AlbertAttention( |
|
(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) |
|
(attention_dropout): Dropout(p=0, inplace=False) |
|
(output_dropout): Dropout(p=0, inplace=False) |
|
(dense): Linear(in_features=768, out_features=768, bias=True) |
|
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
|
) |
|
(ffn): Linear(in_features=768, out_features=3072, bias=True) |
|
(ffn_output): Linear(in_features=3072, out_features=768, bias=True) |
|
(activation): NewGELUActivation() |
|
(dropout): Dropout(p=0, inplace=False) |
|
) |
|
) |
|
) |
|
) |
|
) |
|
) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
(classifier): Linear(in_features=768, out_features=37, bias=True) |
|
) |
|
2022-07-03 15:51:36,022 - __main__ - INFO - CONFIGS:{ |
|
"output_dir": "./models/finetuned-base-uncased_1656843680.4141676", |
|
"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 |
|
} |
|
2022-07-03 15:51:36,023 - transformers.training_args - INFO - PyTorch: setting up devices |
|
2022-07-03 15:51:36,070 - transformers.training_args - INFO - The default value for the training argument ` |
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2022-07-03 15:51:36,075 - __main__ - INFO - [[ MODEL EVALUATION ]] |
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2022-07-03 15:51:36,075 - transformers.trainer - INFO - The following columns in the evaluation set don't have a corresponding argument in `AlbertForTokenClassification.forward` and have been ignored: id, words, ner_tags. If id, words, ner_tags are not expected by `AlbertForTokenClassification.forward`, you can safely ignore this message. |
|
2022-07-03 15:51:36,077 - transformers.trainer - INFO - ***** Running Evaluation ***** |
|
2022-07-03 15:51:36,077 - transformers.trainer - INFO - Num examples = 9479 |
|
2022-07-03 15:51:36,078 - transformers.trainer - INFO - Batch size = 16 |
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2022-07-03 16:02:02,467 - __main__ - INFO - {'eval_loss': 0.08666322380304337, 'eval_precision': 0.8620168813860506, 'eval_recall': 0.8618637292351425, 'eval_f1': 0.8619402985074628, 'eval_accuracy': 0.9780515276066022, 'eval_runtime': 626.3804, 'eval_samples_per_second': 15.133, 'eval_steps_per_second': 0.947, 'step': 0} |
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2022-07-03 16:02:02,468 - transformers.trainer - INFO - The following columns in the test set don't have a corresponding argument in `AlbertForTokenClassification.forward` and have been ignored: id, words, ner_tags. If id, words, ner_tags are not expected by `AlbertForTokenClassification.forward`, you can safely ignore this message. |
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2022-07-03 16:02:02,471 - transformers.trainer - INFO - ***** Running Prediction ***** |
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2022-07-03 16:02:02,471 - transformers.trainer - INFO - Num examples = 9479 |
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2022-07-03 16:02:02,471 - transformers.trainer - INFO - Batch size = 16 |
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2022-07-03 16:12:35,933 - __main__ - INFO - precision recall f1-score support |
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CARDINAL 0.84 0.83 0.83 935 |
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DATE 0.84 0.87 0.86 1602 |
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EVENT 0.61 0.52 0.56 63 |
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FAC 0.54 0.59 0.56 135 |
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GPE 0.95 0.94 0.95 2240 |
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LANGUAGE 0.85 0.50 0.63 22 |
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LAW 0.56 0.57 0.57 40 |
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LOC 0.61 0.65 0.63 179 |
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MONEY 0.85 0.88 0.86 314 |
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NORP 0.88 0.92 0.90 841 |
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ORDINAL 0.78 0.86 0.81 195 |
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ORG 0.84 0.81 0.82 1795 |
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PERCENT 0.88 0.87 0.88 349 |
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PERSON 0.94 0.92 0.93 1988 |
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PRODUCT 0.57 0.53 0.55 76 |
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QUANTITY 0.77 0.81 0.79 105 |
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TIME 0.59 0.66 0.62 212 |
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WORK_OF_ART 0.60 0.52 0.56 166 |
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micro avg 0.86 0.86 0.86 11257 |
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macro avg 0.75 0.74 0.74 11257 |
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weighted avg 0.86 0.86 0.86 11257 |
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