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ner-distilbert-base-uncased-ontonotesv5-englishv4

Token Classification
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distilbert
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ner-distilbert-base-uncased-ontonotesv5-englishv4 / eval.log
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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']
2022-07-03 13:02:08,987 - __main__ - INFO - Dataset({
features: ['id', 'words', 'ner_tags'],
num_rows: 75187
})
2022-07-03 13:02:09,752 - __main__ - INFO - Dataset({
features: ['id', 'words', 'ner_tags'],
num_rows: 9479
})
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.
2022-07-03 13:02:09,756 - transformers.tokenization_utils_base - INFO - loading file models/distilbert-base-uncased_1656660721.137864/checkpoint-14100/vocab.txt
2022-07-03 13:02:09,756 - transformers.tokenization_utils_base - INFO - loading file models/distilbert-base-uncased_1656660721.137864/checkpoint-14100/tokenizer.json
2022-07-03 13:02:09,756 - transformers.tokenization_utils_base - INFO - loading file None
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
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
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]']
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.
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]]}
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,
"logging_steps": 469.0
}
2022-07-03 13:02:18,228 - transformers.training_args - INFO - PyTorch: setting up devices
2022-07-03 13:02:18,318 - 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 13:02:23,736 - __main__ - INFO - [[ MODEL EVALUATION ]]
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.
2022-07-03 13:02:23,752 - transformers.trainer - INFO - ***** Running Evaluation *****
2022-07-03 13:02:23,752 - transformers.trainer - INFO - Num examples = 9479
2022-07-03 13:02:23,752 - transformers.trainer - INFO - Batch size = 16
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}
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.
2022-07-03 13:03:05,415 - transformers.trainer - INFO - ***** Running Prediction *****
2022-07-03 13:03:05,415 - transformers.trainer - INFO - Num examples = 9479
2022-07-03 13:03:05,415 - transformers.trainer - INFO - Batch size = 16
2022-07-03 13:03:49,560 - __main__ - INFO - precision recall f1-score support
CARDINAL 0.84 0.86 0.85 935
DATE 0.83 0.88 0.85 1602
EVENT 0.57 0.57 0.57 63
FAC 0.55 0.62 0.58 135
GPE 0.95 0.92 0.94 2240
LANGUAGE 0.82 0.64 0.72 22
LAW 0.50 0.50 0.50 40
LOC 0.55 0.72 0.62 179
MONEY 0.87 0.89 0.88 314
NORP 0.85 0.89 0.87 841
ORDINAL 0.81 0.88 0.84 195
ORG 0.81 0.83 0.82 1795
PERCENT 0.87 0.89 0.88 349
PERSON 0.93 0.93 0.93 1988
PRODUCT 0.55 0.55 0.55 76
QUANTITY 0.71 0.80 0.75 105
TIME 0.59 0.66 0.62 212
WORK_OF_ART 0.42 0.44 0.43 166
micro avg 0.85 0.86 0.86 11257
macro avg 0.72 0.75 0.73 11257
weighted avg 0.85 0.86 0.86 11257