2022-07-03 16:16:30,561 - __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'] 2022-07-03 16:16:36,880 - __main__ - INFO - Dataset({ features: ['id', 'words', 'ner_tags'], num_rows: 75187 }) 2022-07-03 16:16:37,627 - __main__ - INFO - Dataset({ features: ['id', 'words', 'ner_tags'], num_rows: 9479 }) 2022-07-03 16:16:37,629 - transformers.tokenization_utils_base - INFO - Didn't find file models/bert-base-cased_1656837168.84538/checkpoint-14100/added_tokens.json. We won't load it. 2022-07-03 16:16:37,631 - transformers.tokenization_utils_base - INFO - loading file models/bert-base-cased_1656837168.84538/checkpoint-14100/vocab.txt 2022-07-03 16:16:37,631 - transformers.tokenization_utils_base - INFO - loading file models/bert-base-cased_1656837168.84538/checkpoint-14100/tokenizer.json 2022-07-03 16:16:37,631 - transformers.tokenization_utils_base - INFO - loading file None 2022-07-03 16:16:37,631 - transformers.tokenization_utils_base - INFO - loading file models/bert-base-cased_1656837168.84538/checkpoint-14100/special_tokens_map.json 2022-07-03 16:16:37,632 - transformers.tokenization_utils_base - INFO - loading file models/bert-base-cased_1656837168.84538/checkpoint-14100/tokenizer_config.json 2022-07-03 16:16:37,649 - __main__ - INFO - {'input_ids': [[101, 1327, 1912, 1104, 2962, 136, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 1284, 4161, 5834, 13967, 1128, 1106, 2824, 170, 1957, 2596, 1104, 14754, 1975, 119, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 160, 2924, 1563, 18405, 1116, 1113, 1103, 2038, 2746, 1104, 1975, 131, 21342, 19917, 1104, 16191, 17204, 3757, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 9996, 3543, 1113, 16191, 17204, 3757, 1110, 1103, 12267, 1106, 1103, 15090, 3391, 1116, 17354, 119, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 1135, 1110, 2766, 1104, 170, 2425, 188, 7854, 1162, 117, 3718, 188, 7854, 1279, 117, 170, 3321, 1668, 7115, 1105, 25973, 3590, 117, 1105, 1103, 2038, 6250, 117, 1621, 1168, 1614, 119, 102]], '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]], '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, 0], [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, 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, 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]]} 2022-07-03 16:16:37,649 - __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]'] 2022-07-03 16:16:37,651 - __main__ - INFO - ['[CLS]', 'We', 'respect', '##fully', '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]'] 2022-07-03 16:16:37,651 - __main__ - INFO - ['[CLS]', 'W', '##W', 'II', 'Landmark', '##s', '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]'] 2022-07-03 16:16:37,652 - __main__ - INFO - ['[CLS]', 'Standing', 'tall', 'on', 'Tai', '##hang', 'Mountain', 'is', 'the', 'Monument', 'to', 'the', 'Hundred', 'Regiment', '##s', 'Offensive', '.', '[SEP]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]'] 2022-07-03 16:16:37,652 - __main__ - INFO - ['[CLS]', 'It', 'is', 'composed', 'of', 'a', 'primary', 's', '##tel', '##e', ',', 'secondary', 's', '##tel', '##es', ',', 'a', 'huge', 'round', 'sculpture', 'and', 'beacon', 'tower', ',', 'and', 'the', 'Great', 'Wall', ',', 'among', 'other', 'things', '.', '[SEP]'] 2022-07-03 16:16:37,652 - __main__ - INFO - ------------- 2022-07-03 16:16:37,653 - __main__ - INFO - ['[CLS]', 'We', 'respect', '##fully', '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]'] 2022-07-03 16:16:37,653 - __main__ - INFO - [None, 0, 1, 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] 2022-07-03 16:16:37,656 - datasets.fingerprint - WARNING - Parameter 'function'= 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. 2022-07-03 16:16:42,938 - __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': [[101, 1327, 1912, 1104, 2962, 136, 102], [101, 1284, 4161, 5834, 13967, 1128, 1106, 2824, 170, 1957, 2596, 1104, 14754, 1975, 119, 102], [101, 160, 2924, 1563, 18405, 1116, 1113, 1103, 2038, 2746, 1104, 1975, 131, 21342, 19917, 1104, 16191, 17204, 3757, 102], [101, 9996, 3543, 1113, 16191, 17204, 3757, 1110, 1103, 12267, 1106, 1103, 15090, 3391, 1116, 17354, 119, 102], [101, 1135, 1110, 2766, 1104, 170, 2425, 188, 7854, 1162, 117, 3718, 188, 7854, 1279, 117, 170, 3321, 1668, 7115, 1105, 25973, 3590, 117, 1105, 1103, 2038, 6250, 117, 1621, 1168, 1614, 119, 102]], '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]], '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]], 'labels': [[-100, 0, 0, 0, 0, 0, -100], [-100, 0, 0, -100, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 0, -100], [-100, 31, -100, 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, -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]]} 2022-07-03 16:16:45,238 - transformers.configuration_utils - INFO - loading configuration file models/bert-base-cased_1656837168.84538/checkpoint-14100/config.json 2022-07-03 16:16:45,241 - transformers.configuration_utils - INFO - Model config BertConfig { "_name_or_path": "models/bert-base-cased_1656837168.84538/checkpoint-14100", "architectures": [ "BertForTokenClassification" ], "attention_probs_dropout_prob": 0.1, "classifier_dropout": null, "gradient_checkpointing": false, "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-12, "max_position_embeddings": 512, "model_type": "bert", "num_attention_heads": 12, "num_hidden_layers": 12, "pad_token_id": 0, "position_embedding_type": "absolute", "torch_dtype": "float32", "transformers_version": "4.20.0", "type_vocab_size": 2, "use_cache": true, "vocab_size": 28996 } 2022-07-03 16:16:45,304 - transformers.modeling_utils - INFO - loading weights file models/bert-base-cased_1656837168.84538/checkpoint-14100/pytorch_model.bin 2022-07-03 16:16:46,439 - transformers.modeling_utils - INFO - All model checkpoint weights were used when initializing BertForTokenClassification. 2022-07-03 16:16:46,439 - transformers.modeling_utils - INFO - All the weights of BertForTokenClassification were initialized from the model checkpoint at models/bert-base-cased_1656837168.84538/checkpoint-14100. If your task is similar to the task the model of the checkpoint was trained on, you can already use BertForTokenClassification for predictions without further training. 2022-07-03 16:16:46,442 - __main__ - INFO - BertForTokenClassification( (bert): BertModel( (embeddings): BertEmbeddings( (word_embeddings): Embedding(28996, 768, padding_idx=0) (position_embeddings): Embedding(512, 768) (token_type_embeddings): Embedding(2, 768) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) (encoder): BertEncoder( (layer): ModuleList( (0): BertLayer( (attention): BertAttention( (self): BertSelfAttention( (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): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (1): BertLayer( (attention): BertAttention( (self): BertSelfAttention( (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): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (2): BertLayer( (attention): BertAttention( (self): BertSelfAttention( (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): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (3): BertLayer( (attention): BertAttention( (self): BertSelfAttention( (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): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (4): BertLayer( (attention): BertAttention( (self): BertSelfAttention( (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): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (5): BertLayer( (attention): BertAttention( (self): BertSelfAttention( (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): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (6): BertLayer( (attention): BertAttention( (self): BertSelfAttention( (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): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (7): BertLayer( (attention): BertAttention( (self): BertSelfAttention( (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): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (8): BertLayer( (attention): BertAttention( (self): BertSelfAttention( (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): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (9): BertLayer( (attention): BertAttention( (self): BertSelfAttention( (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): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (10): BertLayer( (attention): BertAttention( (self): BertSelfAttention( (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): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (11): BertLayer( (attention): BertAttention( (self): BertSelfAttention( (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): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.1, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, 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) ) 2022-07-03 16:16:46,443 - __main__ - INFO - CONFIGS:{ "output_dir": "./models/finetuned-base-uncased_1656845190.560204", "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 16:16:46,444 - transformers.training_args - INFO - PyTorch: setting up devices 2022-07-03 16:16:46,488 - transformers.training_args - INFO - The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-). 2022-07-03 16:16:46,494 - __main__ - INFO - [[ MODEL EVALUATION ]] 2022-07-03 16:16:46,494 - transformers.trainer - INFO - The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: ner_tags, id, words. If ner_tags, id, words are not expected by `BertForTokenClassification.forward`, you can safely ignore this message. 2022-07-03 16:16:46,497 - transformers.trainer - INFO - ***** Running Evaluation ***** 2022-07-03 16:16:46,497 - transformers.trainer - INFO - Num examples = 9479 2022-07-03 16:16:46,498 - transformers.trainer - INFO - Batch size = 16 2022-07-03 16:25:59,032 - __main__ - INFO - {'eval_loss': 0.06829366087913513, 'eval_precision': 0.8785372224640836, 'eval_recall': 0.8963311717153771, 'eval_f1': 0.8873450004397152, 'eval_accuracy': 0.9835533880964035, 'eval_runtime': 552.5236, 'eval_samples_per_second': 17.156, 'eval_steps_per_second': 1.073, 'step': 0} 2022-07-03 16:25:59,032 - transformers.trainer - INFO - The following columns in the test set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: ner_tags, id, words. If ner_tags, id, words are not expected by `BertForTokenClassification.forward`, you can safely ignore this message. 2022-07-03 16:25:59,034 - transformers.trainer - INFO - ***** Running Prediction ***** 2022-07-03 16:25:59,035 - transformers.trainer - INFO - Num examples = 9479 2022-07-03 16:25:59,035 - transformers.trainer - INFO - Batch size = 16 2022-07-03 16:34:58,579 - __main__ - INFO - precision recall f1-score support CARDINAL 0.86 0.87 0.86 935 DATE 0.84 0.88 0.86 1602 EVENT 0.65 0.67 0.66 63 FAC 0.69 0.71 0.70 135 GPE 0.97 0.93 0.95 2240 LANGUAGE 0.76 0.73 0.74 22 LAW 0.54 0.55 0.54 40 LOC 0.73 0.80 0.76 179 MONEY 0.87 0.90 0.88 314 NORP 0.93 0.96 0.94 841 ORDINAL 0.80 0.87 0.83 195 ORG 0.88 0.90 0.89 1795 PERCENT 0.88 0.90 0.89 349 PERSON 0.94 0.95 0.94 1988 PRODUCT 0.62 0.76 0.69 76 QUANTITY 0.74 0.81 0.77 105 TIME 0.61 0.67 0.64 212 WORK_OF_ART 0.56 0.66 0.61 166 micro avg 0.88 0.90 0.89 11257 macro avg 0.77 0.81 0.79 11257 weighted avg 0.88 0.90 0.89 11257