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2022-07-03 13:02:00,483 - __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 13:02:08,987 - __main__ - INFO - Dataset({ |
|
features: ['id', 'words', 'ner_tags'], |
|
num_rows: 75187 |
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}) |
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2022-07-03 13:02:09,752 - __main__ - INFO - Dataset({ |
|
features: ['id', 'words', 'ner_tags'], |
|
num_rows: 9479 |
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}) |
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2022-07-03 13:02:09,755 - transformers.tokenization_utils_base - INFO - Didn't find file models/distilbert-base-uncased_1656660721.137864/checkpoint-14100/added_tokens.json. We won't load it. |
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2022-07-03 13:02:09,756 - transformers.tokenization_utils_base - INFO - loading file models/distilbert-base-uncased_1656660721.137864/checkpoint-14100/vocab.txt |
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2022-07-03 13:02:09,756 - transformers.tokenization_utils_base - INFO - loading file models/distilbert-base-uncased_1656660721.137864/checkpoint-14100/tokenizer.json |
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2022-07-03 13:02:09,756 - transformers.tokenization_utils_base - INFO - loading file None |
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2022-07-03 13:02:09,757 - transformers.tokenization_utils_base - INFO - loading file models/distilbert-base-uncased_1656660721.137864/checkpoint-14100/special_tokens_map.json |
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2022-07-03 13:02:09,757 - transformers.tokenization_utils_base - INFO - loading file models/distilbert-base-uncased_1656660721.137864/checkpoint-14100/tokenizer_config.json |
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2022-07-03 13:02:09,775 - __main__ - INFO - {'input_ids': [[101, 2054, 2785, 1997, 3638, 1029, 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], [101, 2057, 26438, 2135, 13260, 2017, 2000, 3422, 1037, 2569, 3179, 1997, 2408, 2859, 1012, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 1059, 2860, 2462, 16209, 2006, 1996, 2307, 3011, 1997, 2859, 1024, 10721, 5758, 1997, 13843, 18003, 3137, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 3061, 4206, 2006, 13843, 18003, 3137, 2003, 1996, 6104, 2000, 1996, 3634, 10435, 5805, 1012, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 2009, 2003, 3605, 1997, 1037, 3078, 26261, 2571, 1010, 3905, 26261, 4244, 1010, 1037, 4121, 2461, 6743, 1998, 14400, 3578, 1010, 1998, 1996, 2307, 2813, 1010, 2426, 2060, 2477, 1012, 102]], '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], [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, 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], [1, 1, 1, 1, 1, 1, 1, 1, 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 13:02:09,776 - __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]'] |
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2022-07-03 13:02:09,776 - __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]'] |
|
2022-07-03 13:02:09,777 - __main__ - INFO - ['[CLS]', 'w', '##w', '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]'] |
|
2022-07-03 13:02:09,777 - __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]'] |
|
2022-07-03 13:02:09,778 - __main__ - INFO - ['[CLS]', 'it', 'is', 'composed', 'of', 'a', 'primary', 'ste', '##le', ',', 'secondary', 'ste', '##les', ',', 'a', 'huge', 'round', 'sculpture', 'and', 'beacon', 'tower', ',', 'and', 'the', 'great', 'wall', ',', 'among', 'other', 'things', '.', '[SEP]'] |
|
2022-07-03 13:02:09,778 - __main__ - INFO - |
|
2022-07-03 13:02:09,778 - __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]'] |
|
2022-07-03 13:02:09,779 - __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] |
|
2022-07-03 13:02:09,785 - datasets.fingerprint - WARNING - Parameter 'function'=<function tokenize_and_align_labels at 0x7f675dfb45e0> 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 13:02:14,916 - __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, 2054, 2785, 1997, 3638, 1029, 102], [101, 2057, 26438, 2135, 13260, 2017, 2000, 3422, 1037, 2569, 3179, 1997, 2408, 2859, 1012, 102], [101, 1059, 2860, 2462, 16209, 2006, 1996, 2307, 3011, 1997, 2859, 1024, 10721, 5758, 1997, 13843, 18003, 3137, 102], [101, 3061, 4206, 2006, 13843, 18003, 3137, 2003, 1996, 6104, 2000, 1996, 3634, 10435, 5805, 1012, 102], [101, 2009, 2003, 3605, 1997, 1037, 3078, 26261, 2571, 1010, 3905, 26261, 4244, 1010, 1037, 4121, 2461, 6743, 1998, 14400, 3578, 1010, 1998, 1996, 2307, 2813, 1010, 2426, 2060, 2477, 1012, 102]], '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]], '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, 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, 32, 0, -100], [-100, 0, 0, 0, 0, 0, 0, 0, -100, 0, 0, 0, -100, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 31, 32, 32, 0, 0, 0, 0, 0, -100]]} |
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2022-07-03 13:02:16,871 - transformers.configuration_utils - INFO - loading configuration file models/distilbert-base-uncased_1656660721.137864/checkpoint-14100/config.json |
|
2022-07-03 13:02:16,873 - transformers.configuration_utils - INFO - Model config DistilBertConfig { |
|
"_name_or_path": "models/distilbert-base-uncased_1656660721.137864/checkpoint-14100", |
|
"activation": "gelu", |
|
"architectures": [ |
|
"DistilBertForTokenClassification" |
|
], |
|
"attention_dropout": 0.1, |
|
"dim": 768, |
|
"dropout": 0.1, |
|
"hidden_dim": 3072, |
|
"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, |
|
"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 |
|
}, |
|
"max_position_embeddings": 512, |
|
"model_type": "distilbert", |
|
"n_heads": 12, |
|
"n_layers": 6, |
|
"pad_token_id": 0, |
|
"qa_dropout": 0.1, |
|
"seq_classif_dropout": 0.2, |
|
"sinusoidal_pos_embds": false, |
|
"tie_weights_": true, |
|
"torch_dtype": "float32", |
|
"transformers_version": "4.20.0", |
|
"vocab_size": 30522 |
|
} |
|
|
|
2022-07-03 13:02:17,083 - transformers.modeling_utils - INFO - loading weights file models/distilbert-base-uncased_1656660721.137864/checkpoint-14100/pytorch_model.bin |
|
2022-07-03 13:02:18,221 - transformers.modeling_utils - INFO - All model checkpoint weights were used when initializing DistilBertForTokenClassification. |
|
|
|
2022-07-03 13:02:18,223 - transformers.modeling_utils - INFO - All the weights of DistilBertForTokenClassification were initialized from the model checkpoint at models/distilbert-base-uncased_1656660721.137864/checkpoint-14100. |
|
If your task is similar to the task the model of the checkpoint was trained on, you can already use DistilBertForTokenClassification for predictions without further training. |
|
2022-07-03 13:02:18,226 - __main__ - INFO - DistilBertForTokenClassification( |
|
(distilbert): DistilBertModel( |
|
(embeddings): Embeddings( |
|
(word_embeddings): Embedding(30522, 768, padding_idx=0) |
|
(position_embeddings): Embedding(512, 768) |
|
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
) |
|
(transformer): Transformer( |
|
(layer): ModuleList( |
|
(0): TransformerBlock( |
|
(attention): MultiHeadSelfAttention( |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
(q_lin): Linear(in_features=768, out_features=768, bias=True) |
|
(k_lin): Linear(in_features=768, out_features=768, bias=True) |
|
(v_lin): Linear(in_features=768, out_features=768, bias=True) |
|
(out_lin): Linear(in_features=768, out_features=768, bias=True) |
|
) |
|
(sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
|
(ffn): FFN( |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
(lin1): Linear(in_features=768, out_features=3072, bias=True) |
|
(lin2): Linear(in_features=3072, out_features=768, bias=True) |
|
(activation): GELUActivation() |
|
) |
|
(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
|
) |
|
(1): TransformerBlock( |
|
(attention): MultiHeadSelfAttention( |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
(q_lin): Linear(in_features=768, out_features=768, bias=True) |
|
(k_lin): Linear(in_features=768, out_features=768, bias=True) |
|
(v_lin): Linear(in_features=768, out_features=768, bias=True) |
|
(out_lin): Linear(in_features=768, out_features=768, bias=True) |
|
) |
|
(sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
|
(ffn): FFN( |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
(lin1): Linear(in_features=768, out_features=3072, bias=True) |
|
(lin2): Linear(in_features=3072, out_features=768, bias=True) |
|
(activation): GELUActivation() |
|
) |
|
(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
|
) |
|
(2): TransformerBlock( |
|
(attention): MultiHeadSelfAttention( |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
(q_lin): Linear(in_features=768, out_features=768, bias=True) |
|
(k_lin): Linear(in_features=768, out_features=768, bias=True) |
|
(v_lin): Linear(in_features=768, out_features=768, bias=True) |
|
(out_lin): Linear(in_features=768, out_features=768, bias=True) |
|
) |
|
(sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
|
(ffn): FFN( |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
(lin1): Linear(in_features=768, out_features=3072, bias=True) |
|
(lin2): Linear(in_features=3072, out_features=768, bias=True) |
|
(activation): GELUActivation() |
|
) |
|
(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
|
) |
|
(3): TransformerBlock( |
|
(attention): MultiHeadSelfAttention( |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
(q_lin): Linear(in_features=768, out_features=768, bias=True) |
|
(k_lin): Linear(in_features=768, out_features=768, bias=True) |
|
(v_lin): Linear(in_features=768, out_features=768, bias=True) |
|
(out_lin): Linear(in_features=768, out_features=768, bias=True) |
|
) |
|
(sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
|
(ffn): FFN( |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
(lin1): Linear(in_features=768, out_features=3072, bias=True) |
|
(lin2): Linear(in_features=3072, out_features=768, bias=True) |
|
(activation): GELUActivation() |
|
) |
|
(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
|
) |
|
(4): TransformerBlock( |
|
(attention): MultiHeadSelfAttention( |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
(q_lin): Linear(in_features=768, out_features=768, bias=True) |
|
(k_lin): Linear(in_features=768, out_features=768, bias=True) |
|
(v_lin): Linear(in_features=768, out_features=768, bias=True) |
|
(out_lin): Linear(in_features=768, out_features=768, bias=True) |
|
) |
|
(sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
|
(ffn): FFN( |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
(lin1): Linear(in_features=768, out_features=3072, bias=True) |
|
(lin2): Linear(in_features=3072, out_features=768, bias=True) |
|
(activation): GELUActivation() |
|
) |
|
(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
|
) |
|
(5): TransformerBlock( |
|
(attention): MultiHeadSelfAttention( |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
(q_lin): Linear(in_features=768, out_features=768, bias=True) |
|
(k_lin): Linear(in_features=768, out_features=768, bias=True) |
|
(v_lin): Linear(in_features=768, out_features=768, bias=True) |
|
(out_lin): Linear(in_features=768, out_features=768, bias=True) |
|
) |
|
(sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
|
(ffn): FFN( |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
(lin1): Linear(in_features=768, out_features=3072, bias=True) |
|
(lin2): Linear(in_features=3072, out_features=768, bias=True) |
|
(activation): GELUActivation() |
|
) |
|
(output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) |
|
) |
|
) |
|
) |
|
) |
|
(dropout): Dropout(p=0.1, inplace=False) |
|
(classifier): Linear(in_features=768, out_features=37, bias=True) |
|
) |
|
2022-07-03 13:02:18,228 - __main__ - INFO - CONFIGS:{ |
|
"output_dir": "./models/distilbert-base-uncased_1656833520.4812543", |
|
"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, |
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"logging_steps": 469.0 |
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} |
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2022-07-03 13:02:18,228 - transformers.training_args - INFO - PyTorch: setting up devices |
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2022-07-03 13:02:18,318 - transformers.training_args - INFO - The default value for the training argument ` |
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2022-07-03 13:02:23,736 - __main__ - INFO - [[ MODEL EVALUATION ]] |
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2022-07-03 13:02:23,736 - transformers.trainer - INFO - The following columns in the evaluation set don't have a corresponding argument in `DistilBertForTokenClassification.forward` and have been ignored: id, ner_tags, words. If id, ner_tags, words are not expected by `DistilBertForTokenClassification.forward`, you can safely ignore this message. |
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2022-07-03 13:02:23,752 - transformers.trainer - INFO - ***** Running Evaluation ***** |
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2022-07-03 13:02:23,752 - transformers.trainer - INFO - Num examples = 9479 |
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2022-07-03 13:02:23,752 - transformers.trainer - INFO - Batch size = 16 |
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2022-07-03 13:03:05,412 - __main__ - INFO - {'eval_loss': 0.08268037438392639, 'eval_precision': 0.8460803059273423, 'eval_recall': 0.8647952385182553, 'eval_f1': 0.8553354127311866, 'eval_accuracy': 0.9779158976052459, 'eval_runtime': 41.6535, 'eval_samples_per_second': 227.568, 'eval_steps_per_second': 14.236, 'step': 0} |
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2022-07-03 13:03:05,413 - transformers.trainer - INFO - The following columns in the test set don't have a corresponding argument in `DistilBertForTokenClassification.forward` and have been ignored: id, ner_tags, words. If id, ner_tags, words are not expected by `DistilBertForTokenClassification.forward`, you can safely ignore this message. |
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2022-07-03 13:03:05,415 - transformers.trainer - INFO - ***** Running Prediction ***** |
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2022-07-03 13:03:05,415 - transformers.trainer - INFO - Num examples = 9479 |
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2022-07-03 13:03:05,415 - transformers.trainer - INFO - Batch size = 16 |
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2022-07-03 13:03:49,560 - __main__ - INFO - precision recall f1-score support |
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CARDINAL 0.84 0.86 0.85 935 |
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DATE 0.83 0.88 0.85 1602 |
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EVENT 0.57 0.57 0.57 63 |
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FAC 0.55 0.62 0.58 135 |
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GPE 0.95 0.92 0.94 2240 |
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LANGUAGE 0.82 0.64 0.72 22 |
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LAW 0.50 0.50 0.50 40 |
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LOC 0.55 0.72 0.62 179 |
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MONEY 0.87 0.89 0.88 314 |
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NORP 0.85 0.89 0.87 841 |
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ORDINAL 0.81 0.88 0.84 195 |
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ORG 0.81 0.83 0.82 1795 |
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PERCENT 0.87 0.89 0.88 349 |
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PERSON 0.93 0.93 0.93 1988 |
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PRODUCT 0.55 0.55 0.55 76 |
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QUANTITY 0.71 0.80 0.75 105 |
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TIME 0.59 0.66 0.62 212 |
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WORK_OF_ART 0.42 0.44 0.43 166 |
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micro avg 0.85 0.86 0.86 11257 |
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macro avg 0.72 0.75 0.73 11257 |
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weighted avg 0.85 0.86 0.86 11257 |
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