model added.

config.json

@@ -0,0 +1,104 @@
+{
+  "_name_or_path": "bert-base-cased",
+  "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
+}

eval.log

@@ -0,0 +1,493 @@
+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'=<function tokenize_and_align_labels at 0x7f4e0440bee0> 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
+

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