| 2022-11-16 17:06:48,577 - __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'] | |
| 2022-11-16 17:06:55,367 - __main__ - INFO - Dataset({ | |
| features: ['id', 'words', 'ner_tags'], | |
| num_rows: 75187 | |
| }) | |
| 2022-11-16 17:06:56,175 - __main__ - INFO - Dataset({ | |
| features: ['id', 'words', 'ner_tags'], | |
| num_rows: 9479 | |
| }) | |
| 2022-11-16 17:06:56,185 - transformers.tokenization_utils_base - INFO - loading file vocab.json | |
| 2022-11-16 17:06:56,185 - transformers.tokenization_utils_base - INFO - loading file merges.txt | |
| 2022-11-16 17:06:56,185 - transformers.tokenization_utils_base - INFO - loading file tokenizer.json | |
| 2022-11-16 17:06:56,185 - transformers.tokenization_utils_base - INFO - loading file added_tokens.json | |
| 2022-11-16 17:06:56,185 - transformers.tokenization_utils_base - INFO - loading file special_tokens_map.json | |
| 2022-11-16 17:06:56,185 - transformers.tokenization_utils_base - INFO - loading file tokenizer_config.json | |
| 2022-11-16 17:06:56,250 - __main__ - INFO - {'input_ids': [[1, 653, 761, 9, 3783, 17487, 2, 0, 0, 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, 166, 32928, 9603, 47, 7, 1183, 10, 780, 5403, 9, 15581, 436, 479, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 15584, 3082, 3192, 23959, 15, 5, 2860, 3875, 9, 436, 4832, 41876, 38628, 9, 15643, 24610, 4743, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 15125, 6764, 15, 15643, 24610, 4743, 16, 5, 23001, 7, 5, 41184, 6304, 25132, 23909, 479, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 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]], '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]], '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]]} | |
| 2022-11-16 17:06:56,251 - __main__ - INFO - ['[CLS]', 'ĠWhat', 'Ġkind', 'Ġof', 'Ġmemory', 'Ġ?', '[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]'] | |
| 2022-11-16 17:06:56,251 - __main__ - INFO - ['[CLS]', 'ĠWe', 'Ġrespectfully', 'Ġinvite', 'Ġyou', 'Ġto', 'Ġwatch', 'Ġa', 'Ġspecial', 'Ġedition', 'Ġof', 'ĠAcross', 'ĠChina', 'Ġ.', '[SEP]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]'] | |
| 2022-11-16 17:06:56,251 - __main__ - INFO - ['[CLS]', 'ĠWW', 'ĠII', 'ĠLand', 'marks', 'Ġon', 'Ġthe', 'ĠGreat', 'ĠEarth', 'Ġof', 'ĠChina', 'Ġ:', 'ĠEternal', 'ĠMemories', 'Ġof', 'ĠTai', 'hang', 'ĠMountain', '[SEP]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]'] | |
| 2022-11-16 17:06:56,251 - __main__ - INFO - ['[CLS]', 'ĠStanding', 'Ġtall', 'Ġon', 'ĠTai', 'hang', 'ĠMountain', 'Ġis', 'Ġthe', 'ĠMonument', 'Ġto', 'Ġthe', 'ĠHundred', 'ĠReg', 'iments', 'ĠOffensive', 'Ġ.', '[SEP]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]'] | |
| 2022-11-16 17:06:56,252 - __main__ - INFO - ['[CLS]', '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.', '[SEP]'] | |
| 2022-11-16 17:06:56,252 - __main__ - INFO - ------------- | |
| 2022-11-16 17:06:56,252 - __main__ - INFO - ['[CLS]', 'ĠWe', 'Ġrespectfully', 'Ġinvite', 'Ġyou', 'Ġto', 'Ġwatch', 'Ġa', 'Ġspecial', 'Ġedition', 'Ġof', 'ĠAcross', 'ĠChina', 'Ġ.', '[SEP]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]'] | |
| 2022-11-16 17:06:56,252 - __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] | |
| 2022-11-16 17:07:05,682 - __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': [[1, 653, 761, 9, 3783, 17487, 2], [1, 166, 32928, 9603, 47, 7, 1183, 10, 780, 5403, 9, 15581, 436, 479, 2], [1, 15584, 3082, 3192, 23959, 15, 5, 2860, 3875, 9, 436, 4832, 41876, 38628, 9, 15643, 24610, 4743, 2], [1, 15125, 6764, 15, 15643, 24610, 4743, 16, 5, 23001, 7, 5, 41184, 6304, 25132, 23909, 479, 2], [1, 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]], '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]], '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]]} | |
| 2022-11-16 17:07:06,902 - transformers.configuration_utils - INFO - loading configuration file /content/NER-System/models/microsoft/deberta-base_1668615764.565312/checkpoint-14100/config.json | |
| 2022-11-16 17:07:06,904 - transformers.configuration_utils - INFO - Model config DebertaConfig { | |
| "_name_or_path": "/content/NER-System/models/microsoft/deberta-base_1668615764.565312/checkpoint-14100", | |
| "architectures": [ | |
| "DebertaForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "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-07, | |
| "max_position_embeddings": 512, | |
| "max_relative_positions": -1, | |
| "model_type": "deberta", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "pooler_dropout": 0, | |
| "pooler_hidden_act": "gelu", | |
| "pooler_hidden_size": 768, | |
| "pos_att_type": [ | |
| "c2p", | |
| "p2c" | |
| ], | |
| "position_biased_input": false, | |
| "relative_attention": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.23.0", | |
| "type_vocab_size": 0, | |
| "vocab_size": 50265 | |
| } | |
| 2022-11-16 17:07:06,933 - transformers.modeling_utils - INFO - loading weights file /content/NER-System/models/microsoft/deberta-base_1668615764.565312/checkpoint-14100/pytorch_model.bin | |
| 2022-11-16 17:07:08,422 - transformers.modeling_utils - INFO - All model checkpoint weights were used when initializing DebertaForTokenClassification. | |
| 2022-11-16 17:07:08,422 - transformers.modeling_utils - INFO - All the weights of DebertaForTokenClassification were initialized from the model checkpoint at /content/NER-System/models/microsoft/deberta-base_1668615764.565312/checkpoint-14100. | |
| If your task is similar to the task the model of the checkpoint was trained on, you can already use DebertaForTokenClassification for predictions without further training. | |
| 2022-11-16 17:07:08,500 - __main__ - INFO - DebertaForTokenClassification( | |
| (deberta): DebertaModel( | |
| (embeddings): DebertaEmbeddings( | |
| (word_embeddings): Embedding(50265, 768, padding_idx=0) | |
| (LayerNorm): DebertaLayerNorm() | |
| (dropout): StableDropout() | |
| ) | |
| (encoder): DebertaEncoder( | |
| (layer): ModuleList( | |
| (0): DebertaLayer( | |
| (attention): DebertaAttention( | |
| (self): DisentangledSelfAttention( | |
| (in_proj): Linear(in_features=768, out_features=2304, bias=False) | |
| (pos_dropout): StableDropout() | |
| (pos_proj): Linear(in_features=768, out_features=768, bias=False) | |
| (pos_q_proj): Linear(in_features=768, out_features=768, bias=True) | |
| (dropout): StableDropout() | |
| ) | |
| (output): DebertaSelfOutput( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (LayerNorm): DebertaLayerNorm() | |
| (dropout): StableDropout() | |
| ) | |
| ) | |
| (intermediate): DebertaIntermediate( | |
| (dense): Linear(in_features=768, out_features=3072, bias=True) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (output): DebertaOutput( | |
| (dense): Linear(in_features=3072, out_features=768, bias=True) | |
| (LayerNorm): DebertaLayerNorm() | |
| (dropout): StableDropout() | |
| ) | |
| ) | |
| (1): DebertaLayer( | |
| (attention): DebertaAttention( | |
| (self): DisentangledSelfAttention( | |
| (in_proj): Linear(in_features=768, out_features=2304, bias=False) | |
| (pos_dropout): StableDropout() | |
| (pos_proj): Linear(in_features=768, out_features=768, bias=False) | |
| (pos_q_proj): Linear(in_features=768, out_features=768, bias=True) | |
| (dropout): StableDropout() | |
| ) | |
| (output): DebertaSelfOutput( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (LayerNorm): DebertaLayerNorm() | |
| (dropout): StableDropout() | |
| ) | |
| ) | |
| (intermediate): DebertaIntermediate( | |
| (dense): Linear(in_features=768, out_features=3072, bias=True) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (output): DebertaOutput( | |
| (dense): Linear(in_features=3072, out_features=768, bias=True) | |
| (LayerNorm): DebertaLayerNorm() | |
| (dropout): StableDropout() | |
| ) | |
| ) | |
| (2): DebertaLayer( | |
| (attention): DebertaAttention( | |
| (self): DisentangledSelfAttention( | |
| (in_proj): Linear(in_features=768, out_features=2304, bias=False) | |
| (pos_dropout): StableDropout() | |
| (pos_proj): Linear(in_features=768, out_features=768, bias=False) | |
| (pos_q_proj): Linear(in_features=768, out_features=768, bias=True) | |
| (dropout): StableDropout() | |
| ) | |
| (output): DebertaSelfOutput( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (LayerNorm): DebertaLayerNorm() | |
| (dropout): StableDropout() | |
| ) | |
| ) | |
| (intermediate): DebertaIntermediate( | |
| (dense): Linear(in_features=768, out_features=3072, bias=True) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (output): DebertaOutput( | |
| (dense): Linear(in_features=3072, out_features=768, bias=True) | |
| (LayerNorm): DebertaLayerNorm() | |
| (dropout): StableDropout() | |
| ) | |
| ) | |
| (3): DebertaLayer( | |
| (attention): DebertaAttention( | |
| (self): DisentangledSelfAttention( | |
| (in_proj): Linear(in_features=768, out_features=2304, bias=False) | |
| (pos_dropout): StableDropout() | |
| (pos_proj): Linear(in_features=768, out_features=768, bias=False) | |
| (pos_q_proj): Linear(in_features=768, out_features=768, bias=True) | |
| (dropout): StableDropout() | |
| ) | |
| (output): DebertaSelfOutput( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (LayerNorm): DebertaLayerNorm() | |
| (dropout): StableDropout() | |
| ) | |
| ) | |
| (intermediate): DebertaIntermediate( | |
| (dense): Linear(in_features=768, out_features=3072, bias=True) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (output): DebertaOutput( | |
| (dense): Linear(in_features=3072, out_features=768, bias=True) | |
| (LayerNorm): DebertaLayerNorm() | |
| (dropout): StableDropout() | |
| ) | |
| ) | |
| (4): DebertaLayer( | |
| (attention): DebertaAttention( | |
| (self): DisentangledSelfAttention( | |
| (in_proj): Linear(in_features=768, out_features=2304, bias=False) | |
| (pos_dropout): StableDropout() | |
| (pos_proj): Linear(in_features=768, out_features=768, bias=False) | |
| (pos_q_proj): Linear(in_features=768, out_features=768, bias=True) | |
| (dropout): StableDropout() | |
| ) | |
| (output): DebertaSelfOutput( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (LayerNorm): DebertaLayerNorm() | |
| (dropout): StableDropout() | |
| ) | |
| ) | |
| (intermediate): DebertaIntermediate( | |
| (dense): Linear(in_features=768, out_features=3072, bias=True) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (output): DebertaOutput( | |
| (dense): Linear(in_features=3072, out_features=768, bias=True) | |
| (LayerNorm): DebertaLayerNorm() | |
| (dropout): StableDropout() | |
| ) | |
| ) | |
| (5): DebertaLayer( | |
| (attention): DebertaAttention( | |
| (self): DisentangledSelfAttention( | |
| (in_proj): Linear(in_features=768, out_features=2304, bias=False) | |
| (pos_dropout): StableDropout() | |
| (pos_proj): Linear(in_features=768, out_features=768, bias=False) | |
| (pos_q_proj): Linear(in_features=768, out_features=768, bias=True) | |
| (dropout): StableDropout() | |
| ) | |
| (output): DebertaSelfOutput( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (LayerNorm): DebertaLayerNorm() | |
| (dropout): StableDropout() | |
| ) | |
| ) | |
| (intermediate): DebertaIntermediate( | |
| (dense): Linear(in_features=768, out_features=3072, bias=True) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (output): DebertaOutput( | |
| (dense): Linear(in_features=3072, out_features=768, bias=True) | |
| (LayerNorm): DebertaLayerNorm() | |
| (dropout): StableDropout() | |
| ) | |
| ) | |
| (6): DebertaLayer( | |
| (attention): DebertaAttention( | |
| (self): DisentangledSelfAttention( | |
| (in_proj): Linear(in_features=768, out_features=2304, bias=False) | |
| (pos_dropout): StableDropout() | |
| (pos_proj): Linear(in_features=768, out_features=768, bias=False) | |
| (pos_q_proj): Linear(in_features=768, out_features=768, bias=True) | |
| (dropout): StableDropout() | |
| ) | |
| (output): DebertaSelfOutput( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (LayerNorm): DebertaLayerNorm() | |
| (dropout): StableDropout() | |
| ) | |
| ) | |
| (intermediate): DebertaIntermediate( | |
| (dense): Linear(in_features=768, out_features=3072, bias=True) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (output): DebertaOutput( | |
| (dense): Linear(in_features=3072, out_features=768, bias=True) | |
| (LayerNorm): DebertaLayerNorm() | |
| (dropout): StableDropout() | |
| ) | |
| ) | |
| (7): DebertaLayer( | |
| (attention): DebertaAttention( | |
| (self): DisentangledSelfAttention( | |
| (in_proj): Linear(in_features=768, out_features=2304, bias=False) | |
| (pos_dropout): StableDropout() | |
| (pos_proj): Linear(in_features=768, out_features=768, bias=False) | |
| (pos_q_proj): Linear(in_features=768, out_features=768, bias=True) | |
| (dropout): StableDropout() | |
| ) | |
| (output): DebertaSelfOutput( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (LayerNorm): DebertaLayerNorm() | |
| (dropout): StableDropout() | |
| ) | |
| ) | |
| (intermediate): DebertaIntermediate( | |
| (dense): Linear(in_features=768, out_features=3072, bias=True) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (output): DebertaOutput( | |
| (dense): Linear(in_features=3072, out_features=768, bias=True) | |
| (LayerNorm): DebertaLayerNorm() | |
| (dropout): StableDropout() | |
| ) | |
| ) | |
| (8): DebertaLayer( | |
| (attention): DebertaAttention( | |
| (self): DisentangledSelfAttention( | |
| (in_proj): Linear(in_features=768, out_features=2304, bias=False) | |
| (pos_dropout): StableDropout() | |
| (pos_proj): Linear(in_features=768, out_features=768, bias=False) | |
| (pos_q_proj): Linear(in_features=768, out_features=768, bias=True) | |
| (dropout): StableDropout() | |
| ) | |
| (output): DebertaSelfOutput( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (LayerNorm): DebertaLayerNorm() | |
| (dropout): StableDropout() | |
| ) | |
| ) | |
| (intermediate): DebertaIntermediate( | |
| (dense): Linear(in_features=768, out_features=3072, bias=True) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (output): DebertaOutput( | |
| (dense): Linear(in_features=3072, out_features=768, bias=True) | |
| (LayerNorm): DebertaLayerNorm() | |
| (dropout): StableDropout() | |
| ) | |
| ) | |
| (9): DebertaLayer( | |
| (attention): DebertaAttention( | |
| (self): DisentangledSelfAttention( | |
| (in_proj): Linear(in_features=768, out_features=2304, bias=False) | |
| (pos_dropout): StableDropout() | |
| (pos_proj): Linear(in_features=768, out_features=768, bias=False) | |
| (pos_q_proj): Linear(in_features=768, out_features=768, bias=True) | |
| (dropout): StableDropout() | |
| ) | |
| (output): DebertaSelfOutput( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (LayerNorm): DebertaLayerNorm() | |
| (dropout): StableDropout() | |
| ) | |
| ) | |
| (intermediate): DebertaIntermediate( | |
| (dense): Linear(in_features=768, out_features=3072, bias=True) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (output): DebertaOutput( | |
| (dense): Linear(in_features=3072, out_features=768, bias=True) | |
| (LayerNorm): DebertaLayerNorm() | |
| (dropout): StableDropout() | |
| ) | |
| ) | |
| (10): DebertaLayer( | |
| (attention): DebertaAttention( | |
| (self): DisentangledSelfAttention( | |
| (in_proj): Linear(in_features=768, out_features=2304, bias=False) | |
| (pos_dropout): StableDropout() | |
| (pos_proj): Linear(in_features=768, out_features=768, bias=False) | |
| (pos_q_proj): Linear(in_features=768, out_features=768, bias=True) | |
| (dropout): StableDropout() | |
| ) | |
| (output): DebertaSelfOutput( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (LayerNorm): DebertaLayerNorm() | |
| (dropout): StableDropout() | |
| ) | |
| ) | |
| (intermediate): DebertaIntermediate( | |
| (dense): Linear(in_features=768, out_features=3072, bias=True) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (output): DebertaOutput( | |
| (dense): Linear(in_features=3072, out_features=768, bias=True) | |
| (LayerNorm): DebertaLayerNorm() | |
| (dropout): StableDropout() | |
| ) | |
| ) | |
| (11): DebertaLayer( | |
| (attention): DebertaAttention( | |
| (self): DisentangledSelfAttention( | |
| (in_proj): Linear(in_features=768, out_features=2304, bias=False) | |
| (pos_dropout): StableDropout() | |
| (pos_proj): Linear(in_features=768, out_features=768, bias=False) | |
| (pos_q_proj): Linear(in_features=768, out_features=768, bias=True) | |
| (dropout): StableDropout() | |
| ) | |
| (output): DebertaSelfOutput( | |
| (dense): Linear(in_features=768, out_features=768, bias=True) | |
| (LayerNorm): DebertaLayerNorm() | |
| (dropout): StableDropout() | |
| ) | |
| ) | |
| (intermediate): DebertaIntermediate( | |
| (dense): Linear(in_features=768, out_features=3072, bias=True) | |
| (intermediate_act_fn): GELUActivation() | |
| ) | |
| (output): DebertaOutput( | |
| (dense): Linear(in_features=3072, out_features=768, bias=True) | |
| (LayerNorm): DebertaLayerNorm() | |
| (dropout): StableDropout() | |
| ) | |
| ) | |
| ) | |
| (rel_embeddings): Embedding(1024, 768) | |
| ) | |
| ) | |
| (dropout): Dropout(p=0.1, inplace=False) | |
| (classifier): Linear(in_features=768, out_features=37, bias=True) | |
| ) | |
| 2022-11-16 17:07:08,521 - __main__ - INFO - CONFIGS:{ | |
| "output_dir": "./eval_test1", | |
| "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, | |
| "fp16": true, | |
| "logging_steps": 469.0 | |
| } | |
| 2022-11-16 17:07:08,522 - transformers.training_args - INFO - PyTorch: setting up devices | |
| 2022-11-16 17:07:08,557 - 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 :-). | |
| 2022-11-16 17:07:11,000 - transformers.trainer - INFO - Using cuda_amp half precision backend | |
| 2022-11-16 17:07:11,001 - __main__ - INFO - [[ MODEL EVALUATION ]] | |
| 2022-11-16 17:07:11,001 - transformers.trainer - INFO - The following columns in the evaluation set don't have a corresponding argument in `DebertaForTokenClassification.forward` and have been ignored: words, ner_tags, id. If words, ner_tags, id are not expected by `DebertaForTokenClassification.forward`, you can safely ignore this message. | |
| 2022-11-16 17:07:11,004 - transformers.trainer - INFO - ***** Running Evaluation ***** | |
| 2022-11-16 17:07:11,004 - transformers.trainer - INFO - Num examples = 9479 | |
| 2022-11-16 17:07:11,004 - transformers.trainer - INFO - Batch size = 16 | |
| 2022-11-16 17:07:11,007 - transformers.tokenization_utils_base - WARNING - You're using a DebertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding. | |
| 2022-11-16 17:07:33,916 - __main__ - INFO - {'eval_loss': 0.06115942820906639, 'eval_precision': 0.8953762782973517, 'eval_recall': 0.9100115483699032, 'eval_f1': 0.902634593356243, 'eval_accuracy': 0.9848035428915137, 'eval_runtime': 22.906, 'eval_samples_per_second': 413.822, 'eval_steps_per_second': 25.888, 'step': 0} | |
| 2022-11-16 17:07:33,916 - transformers.trainer - INFO - The following columns in the test set don't have a corresponding argument in `DebertaForTokenClassification.forward` and have been ignored: words, ner_tags, id. If words, ner_tags, id are not expected by `DebertaForTokenClassification.forward`, you can safely ignore this message. | |
| 2022-11-16 17:07:33,918 - transformers.trainer - INFO - ***** Running Prediction ***** | |
| 2022-11-16 17:07:33,918 - transformers.trainer - INFO - Num examples = 9479 | |
| 2022-11-16 17:07:33,918 - transformers.trainer - INFO - Batch size = 16 | |
| 2022-11-16 17:07:59,630 - __main__ - INFO - precision recall f1-score support | |
| CARDINAL 0.86 0.87 0.86 935 | |
| DATE 0.85 0.89 0.87 1602 | |
| EVENT 0.65 0.78 0.71 63 | |
| FAC 0.74 0.80 0.77 135 | |
| GPE 0.97 0.96 0.96 2240 | |
| LANGUAGE 0.83 0.68 0.75 22 | |
| LAW 0.71 0.68 0.69 40 | |
| LOC 0.74 0.77 0.76 179 | |
| MONEY 0.88 0.90 0.89 314 | |
| NORP 0.94 0.97 0.95 841 | |
| ORDINAL 0.79 0.87 0.83 195 | |
| ORG 0.92 0.92 0.92 1795 | |
| PERCENT 0.92 0.92 0.92 349 | |
| PERSON 0.95 0.95 0.95 1988 | |
| PRODUCT 0.65 0.76 0.70 76 | |
| QUANTITY 0.77 0.82 0.80 105 | |
| TIME 0.62 0.65 0.63 212 | |
| WORK_OF_ART 0.64 0.69 0.66 166 | |
| micro avg 0.90 0.91 0.90 11257 | |
| macro avg 0.80 0.83 0.81 11257 | |
| weighted avg 0.90 0.91 0.90 11257 | |