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INFO:__main__:Loading model
INFO:transformers.tokenization_utils:Model name '/data/p281734/bert_retrain/hate_bert' not found in model shortcut name list (bert-base-uncased, bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, bert-base-multilingual-cased, bert-base-chinese, bert-base-german-cased, bert-large-uncased-whole-word-masking, bert-large-cased-whole-word-masking, bert-large-uncased-whole-word-masking-finetuned-squad, bert-large-cased-whole-word-masking-finetuned-squad, bert-base-cased-finetuned-mrpc, bert-base-german-dbmdz-cased, bert-base-german-dbmdz-uncased, bert-base-finnish-cased-v1, bert-base-finnish-uncased-v1, bert-base-dutch-cased). Assuming '/data/p281734/bert_retrain/hate_bert' is a path, a model identifier, or url to a directory containing tokenizer files.
INFO:transformers.tokenization_utils:Didn't find file /data/p281734/bert_retrain/hate_bert/added_tokens.json. We won't load it.
INFO:transformers.tokenization_utils:loading file /data/p281734/bert_retrain/hate_bert/vocab.txt
INFO:transformers.tokenization_utils:loading file None
INFO:transformers.tokenization_utils:loading file /data/p281734/bert_retrain/hate_bert/special_tokens_map.json
INFO:transformers.tokenization_utils:loading file /data/p281734/bert_retrain/hate_bert/tokenizer_config.json
INFO:transformers.configuration_utils:loading configuration file /data/p281734/bert_retrain/hate_bert/config.json
INFO:transformers.configuration_utils:Model config BertConfig {
 "_num_labels": 2,
 "architectures": [
 "BertForMaskedLM"
 ],
 "attention_probs_dropout_prob": 0.1,
 "bad_words_ids": null,
 "bos_token_id": null,
 "decoder_start_token_id": null,
 "do_sample": false,
 "early_stopping": false,
 "eos_token_id": null,
 "finetuning_task": null,
 "hidden_act": "gelu",
 "hidden_dropout_prob": 0.1,
 "hidden_size": 768,
 "id2label": {
 "0": "LABEL_0",
 "1": "LABEL_1"
 },
 "initializer_range": 0.02,
 "intermediate_size": 3072,
 "is_decoder": false,
 "is_encoder_decoder": false,
 "label2id": {
 "LABEL_0": 0,
 "LABEL_1": 1
 },
 "layer_norm_eps": 1e-12,
 "length_penalty": 1.0,
 "max_length": 20,
 "max_position_embeddings": 512,
 "min_length": 0,
 "model_type": "bert",
 "no_repeat_ngram_size": 0,
 "num_attention_heads": 12,
 "num_beams": 1,
 "num_hidden_layers": 12,
 "num_return_sequences": 1,
 "output_attentions": false,
 "output_hidden_states": false,
 "output_past": true,
 "pad_token_id": 0,
 "prefix": null,
 "pruned_heads": {},
 "repetition_penalty": 1.0,
 "task_specific_params": null,
 "temperature": 1.0,
 "top_k": 50,
 "top_p": 1.0,
 "torchscript": false,
 "type_vocab_size": 2,
 "use_bfloat16": false,
 "vocab_size": 30522
}
INFO:transformers.modeling_utils:loading weights file /data/p281734/bert_retrain/hate_bert/pytorch_model.bin
INFO:transformers.modeling_utils:Weights of BertForSequenceClassification not initialized from pretrained model: ['classifier.weight', 'classifier.bias']
INFO:transformers.modeling_utils:Weights from pretrained model not used in BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.decoder.bias']
INFO:__main__:Bert for classification model has been loaded!
INFO:__main__:The BERT model has 201 different named parameters.
INFO:__main__:==== Embedding Layer ====
INFO:__main__:bert.embeddings.word_embeddings.weight (30522, 768)
INFO:__main__:bert.embeddings.position_embeddings.weight (512, 768)
INFO:__main__:bert.embeddings.token_type_embeddings.weight (2, 768)
INFO:__main__:bert.embeddings.LayerNorm.weight (768,)
INFO:__main__:bert.embeddings.LayerNorm.bias (768,)
INFO:__main__:
==== First Transformer ====
INFO:__main__:bert.encoder.layer.0.attention.self.query.weight (768, 768)
INFO:__main__:bert.encoder.layer.0.attention.self.query.bias (768,)
INFO:__main__:bert.encoder.layer.0.attention.self.key.weight (768, 768)
INFO:__main__:bert.encoder.layer.0.attention.self.key.bias (768,)
INFO:__main__:bert.encoder.layer.0.attention.self.value.weight (768, 768)
INFO:__main__:bert.encoder.layer.0.attention.self.value.bias (768,)
INFO:__main__:bert.encoder.layer.0.attention.output.dense.weight (768, 768)
INFO:__main__:bert.encoder.layer.0.attention.output.dense.bias (768,)
INFO:__main__:bert.encoder.layer.0.attention.output.LayerNorm.weight (768,)
INFO:__main__:bert.encoder.layer.0.attention.output.LayerNorm.bias (768,)
INFO:__main__:bert.encoder.layer.0.intermediate.dense.weight (3072, 768)
INFO:__main__:bert.encoder.layer.0.intermediate.dense.bias (3072,)
INFO:__main__:bert.encoder.layer.0.output.dense.weight (768, 3072)
INFO:__main__:bert.encoder.layer.0.output.dense.bias (768,)
INFO:__main__:bert.encoder.layer.0.output.LayerNorm.weight (768,)
INFO:__main__:bert.encoder.layer.0.output.LayerNorm.bias (768,)
INFO:__main__:
==== Output Layer ====
INFO:__main__:bert.pooler.dense.weight (768, 768)
INFO:__main__:bert.pooler.dense.bias (768,)
INFO:__main__:classifier.weight (2, 768)
INFO:__main__:classifier.bias (2,)
INFO:__main__:
INFO:__main__:======== Epoch 1 / 5 ========
INFO:__main__:Training...
INFO:__main__: Batch 40 of 414. Elapsed: 0:00:12.
INFO:__main__: Batch 80 of 414. Elapsed: 0:00:21.
INFO:__main__: Batch 120 of 414. Elapsed: 0:00:29.
INFO:__main__: Batch 160 of 414. Elapsed: 0:00:38.
INFO:__main__: Batch 200 of 414. Elapsed: 0:00:47.
INFO:__main__: Batch 240 of 414. Elapsed: 0:00:56.
INFO:__main__: Batch 280 of 414. Elapsed: 0:01:05.
INFO:__main__: Batch 320 of 414. Elapsed: 0:01:14.
INFO:__main__: Batch 360 of 414. Elapsed: 0:01:23.
INFO:__main__: Batch 400 of 414. Elapsed: 0:01:31.
INFO:__main__:
INFO:__main__: Average training loss: 0.51
INFO:__main__: Training epcoh took: 0:01:35
INFO:__main__:
INFO:__main__:Running Validation...
INFO:__main__: precision recall f1-score support
0 0.8462 0.9565 0.8980 23
 1 0.8333 0.5556 0.6667 9
accuracy 0.8438 32
 macro avg 0.8397 0.7560 0.7823 32
weighted avg 0.8425 0.8438 0.8329 32
INFO:__main__: precision recall f1-score support
0 0.8696 0.9091 0.8889 22
 1 0.7778 0.7000 0.7368 10
accuracy 0.8438 32
 macro avg 0.8237 0.8045 0.8129 32
weighted avg 0.8409 0.8438 0.8414 32
INFO:__main__: precision recall f1-score support
0 0.9130 0.9130 0.9130 23
 1 0.7778 0.7778 0.7778 9
accuracy 0.8750 32
 macro avg 0.8454 0.8454 0.8454 32
weighted avg 0.8750 0.8750 0.8750 32
INFO:__main__: precision recall f1-score support
0 0.8929 1.0000 0.9434 25
 1 1.0000 0.5714 0.7273 7
accuracy 0.9062 32
 macro avg 0.9464 0.7857 0.8353 32
weighted avg 0.9163 0.9062 0.8961 32
INFO:__main__: precision recall f1-score support
0 0.6923 0.9474 0.8000 19
 1 0.8333 0.3846 0.5263 13
accuracy 0.7188 32
 macro avg 0.7628 0.6660 0.6632 32
weighted avg 0.7496 0.7188 0.6888 32
INFO:__main__: precision recall f1-score support
0 0.7500 1.0000 0.8571 21
 1 1.0000 0.3636 0.5333 11
accuracy 0.7812 32
 macro avg 0.8750 0.6818 0.6952 32
weighted avg 0.8359 0.7812 0.7458 32
INFO:__main__: precision recall f1-score support
0 0.8800 1.0000 0.9362 22
 1 1.0000 0.7000 0.8235 10
accuracy 0.9062 32
 macro avg 0.9400 0.8500 0.8798 32
weighted avg 0.9175 0.9062 0.9010 32
INFO:__main__: precision recall f1-score support
0 0.8621 1.0000 0.9259 25
 1 1.0000 0.4286 0.6000 7
accuracy 0.8750 32
 macro avg 0.9310 0.7143 0.7630 32
weighted avg 0.8922 0.8750 0.8546 32
INFO:__main__: precision recall f1-score support
0 0.6786 0.9500 0.7917 20
 1 0.7500 0.2500 0.3750 12
accuracy 0.6875 32
 macro avg 0.7143 0.6000 0.5833 32
weighted avg 0.7054 0.6875 0.6354 32
INFO:__main__: precision recall f1-score support
0 0.9200 1.0000 0.9583 23
 1 1.0000 0.7778 0.8750 9
accuracy 0.9375 32
 macro avg 0.9600 0.8889 0.9167 32
weighted avg 0.9425 0.9375 0.9349 32
INFO:__main__: precision recall f1-score support
0 0.7692 0.9091 0.8333 22
 1 0.6667 0.4000 0.5000 10
accuracy 0.7500 32
 macro avg 0.7179 0.6545 0.6667 32
weighted avg 0.7372 0.7500 0.7292 32
INFO:__main__: precision recall f1-score support
0 0.8077 0.9545 0.8750 22
 1 0.8333 0.5000 0.6250 10
accuracy 0.8125 32
 macro avg 0.8205 0.7273 0.7500 32
weighted avg 0.8157 0.8125 0.7969 32
INFO:__main__: precision recall f1-score support
0 0.8929 1.0000 0.9434 25
 1 1.0000 0.5714 0.7273 7
accuracy 0.9062 32
 macro avg 0.9464 0.7857 0.8353 32
weighted avg 0.9163 0.9062 0.8961 32
INFO:__main__: precision recall f1-score support
0 0.7917 0.9500 0.8636 20
 1 0.8750 0.5833 0.7000 12
accuracy 0.8125 32
 macro avg 0.8333 0.7667 0.7818 32
weighted avg 0.8229 0.8125 0.8023 32
INFO:__main__: precision recall f1-score support
0 0.8889 0.9600 0.9231 25
 1 0.8000 0.5714 0.6667 7
accuracy 0.8750 32
 macro avg 0.8444 0.7657 0.7949 32
weighted avg 0.8694 0.8750 0.8670 32
INFO:__main__: precision recall f1-score support
0 0.8000 1.0000 0.8889 20
 1 1.0000 0.5833 0.7368 12
accuracy 0.8438 32
 macro avg 0.9000 0.7917 0.8129 32
weighted avg 0.8750 0.8438 0.8319 32
INFO:__main__: precision recall f1-score support
0 0.9286 0.9630 0.9455 27
 1 0.7500 0.6000 0.6667 5
accuracy 0.9062 32
 macro avg 0.8393 0.7815 0.8061 32
weighted avg 0.9007 0.9062 0.9019 32
INFO:__main__: precision recall f1-score support
0 0.8276 1.0000 0.9057 24
 1 1.0000 0.3750 0.5455 8
accuracy 0.8438 32
 macro avg 0.9138 0.6875 0.7256 32
weighted avg 0.8707 0.8438 0.8156 32
INFO:__main__: precision recall f1-score support
0 0.9231 0.9600 0.9412 25
 1 0.8333 0.7143 0.7692 7
accuracy 0.9062 32
 macro avg 0.8782 0.8371 0.8552 32
weighted avg 0.9034 0.9062 0.9036 32
INFO:__main__: precision recall f1-score support
0 0.8696 0.9524 0.9091 21
 1 0.8889 0.7273 0.8000 11
accuracy 0.8750 32
 macro avg 0.8792 0.8398 0.8545 32
weighted avg 0.8762 0.8750 0.8716 32
INFO:__main__: precision recall f1-score support
0 0.8667 1.0000 0.9286 26
 1 1.0000 0.3333 0.5000 6
accuracy 0.8750 32
 macro avg 0.9333 0.6667 0.7143 32
weighted avg 0.8917 0.8750 0.8482 32
INFO:__main__: precision recall f1-score support
0 0.9130 0.9130 0.9130 23
 1 0.7778 0.7778 0.7778 9
accuracy 0.8750 32
 macro avg 0.8454 0.8454 0.8454 32
weighted avg 0.8750 0.8750 0.8750 32
INFO:__main__: precision recall f1-score support
0 0.9310 0.9310 0.9310 29
 1 0.3333 0.3333 0.3333 3
accuracy 0.8750 32
 macro avg 0.6322 0.6322 0.6322 32
weighted avg 0.8750 0.8750 0.8750 32
INFO:__main__: precision recall f1-score support
0 0.7600 1.0000 0.8636 19
 1 1.0000 0.5385 0.7000 13
accuracy 0.8125 32
 macro avg 0.8800 0.7692 0.7818 32
weighted avg 0.8575 0.8125 0.7972 32
INFO:__main__: precision recall f1-score support
0 0.8462 0.9167 0.8800 24
 1 0.6667 0.5000 0.5714 8
accuracy 0.8125 32
 macro avg 0.7564 0.7083 0.7257 32
weighted avg 0.8013 0.8125 0.8029 32
INFO:__main__: precision recall f1-score support
0 0.8889 0.9600 0.9231 25
 1 0.8000 0.5714 0.6667 7
accuracy 0.8750 32
 macro avg 0.8444 0.7657 0.7949 32
weighted avg 0.8694 0.8750 0.8670 32
INFO:__main__: precision recall f1-score support
0 0.8696 1.0000 0.9302 20
 1 1.0000 0.6250 0.7692 8
accuracy 0.8929 28
 macro avg 0.9348 0.8125 0.8497 28
weighted avg 0.9068 0.8929 0.8842 28
INFO:__main__: Accuracy: 0.85
INFO:__main__: Validation took: 0:00:02
INFO:__main__:
INFO:__main__:Evaluation on full prediction per epoch!
INFO:__main__:Gold labels860
INFO:__main__:Predicted labels860
INFO:__main__: precision recall f1-score support
0 0.8470 0.9645 0.9020 620
 1 0.8571 0.5500 0.6701 240
accuracy 0.8488 860
 macro avg 0.8521 0.7573 0.7860 860
weighted avg 0.8498 0.8488 0.8372 860
INFO:__main__:
INFO:__main__:======== Epoch 2 / 5 ========
INFO:__main__:Training...
INFO:__main__: Batch 40 of 414. Elapsed: 0:00:09.
INFO:__main__: Batch 80 of 414. Elapsed: 0:00:17.
INFO:__main__: Batch 120 of 414. Elapsed: 0:00:26.
INFO:__main__: Batch 160 of 414. Elapsed: 0:00:35.
INFO:__main__: Batch 200 of 414. Elapsed: 0:00:44.
INFO:__main__: Batch 240 of 414. Elapsed: 0:00:53.
INFO:__main__: Batch 280 of 414. Elapsed: 0:01:01.
INFO:__main__: Batch 320 of 414. Elapsed: 0:01:10.
INFO:__main__: Batch 360 of 414. Elapsed: 0:01:18.
INFO:__main__: Batch 400 of 414. Elapsed: 0:01:27.
INFO:__main__:
INFO:__main__: Average training loss: 0.40
INFO:__main__: Training epcoh took: 0:01:30
INFO:__main__:
INFO:__main__:Running Validation...
INFO:__main__: precision recall f1-score support
0 0.8750 0.9130 0.8936 23
 1 0.7500 0.6667 0.7059 9
accuracy 0.8438 32
 macro avg 0.8125 0.7899 0.7997 32
weighted avg 0.8398 0.8438 0.8408 32
INFO:__main__: precision recall f1-score support
0 0.9500 0.8636 0.9048 22
 1 0.7500 0.9000 0.8182 10
accuracy 0.8750 32
 macro avg 0.8500 0.8818 0.8615 32
weighted avg 0.8875 0.8750 0.8777 32
INFO:__main__: precision recall f1-score support
0 0.9130 0.9130 0.9130 23
 1 0.7778 0.7778 0.7778 9
accuracy 0.8750 32
 macro avg 0.8454 0.8454 0.8454 32
weighted avg 0.8750 0.8750 0.8750 32
INFO:__main__: precision recall f1-score support
0 0.9600 0.9600 0.9600 25
 1 0.8571 0.8571 0.8571 7
accuracy 0.9375 32
 macro avg 0.9086 0.9086 0.9086 32
weighted avg 0.9375 0.9375 0.9375 32
INFO:__main__: precision recall f1-score support
0 0.6957 0.8421 0.7619 19
 1 0.6667 0.4615 0.5455 13
accuracy 0.6875 32
 macro avg 0.6812 0.6518 0.6537 32
weighted avg 0.6839 0.6875 0.6740 32
INFO:__main__: precision recall f1-score support
0 0.7778 1.0000 0.8750 21
 1 1.0000 0.4545 0.6250 11
accuracy 0.8125 32
 macro avg 0.8889 0.7273 0.7500 32
weighted avg 0.8542 0.8125 0.7891 32
INFO:__main__: precision recall f1-score support
0 0.9565 1.0000 0.9778 22
 1 1.0000 0.9000 0.9474 10
accuracy 0.9688 32
 macro avg 0.9783 0.9500 0.9626 32
weighted avg 0.9701 0.9688 0.9683 32
INFO:__main__: precision recall f1-score support
0 0.8889 0.9600 0.9231 25
 1 0.8000 0.5714 0.6667 7
accuracy 0.8750 32
 macro avg 0.8444 0.7657 0.7949 32
weighted avg 0.8694 0.8750 0.8670 32
INFO:__main__: precision recall f1-score support
0 0.7308 0.9500 0.8261 20
 1 0.8333 0.4167 0.5556 12
accuracy 0.7500 32
 macro avg 0.7821 0.6833 0.6908 32
weighted avg 0.7692 0.7500 0.7246 32
INFO:__main__: precision recall f1-score support
0 0.9167 0.9565 0.9362 23
 1 0.8750 0.7778 0.8235 9
accuracy 0.9062 32
 macro avg 0.8958 0.8671 0.8798 32
weighted avg 0.9049 0.9062 0.9045 32
INFO:__main__: precision recall f1-score support
0 0.7917 0.8636 0.8261 22
 1 0.6250 0.5000 0.5556 10
accuracy 0.7500 32
 macro avg 0.7083 0.6818 0.6908 32
weighted avg 0.7396 0.7500 0.7415 32
INFO:__main__: precision recall f1-score support
0 0.8077 0.9545 0.8750 22
 1 0.8333 0.5000 0.6250 10
accuracy 0.8125 32
 macro avg 0.8205 0.7273 0.7500 32
weighted avg 0.8157 0.8125 0.7969 32
INFO:__main__: precision recall f1-score support
0 0.8929 1.0000 0.9434 25
 1 1.0000 0.5714 0.7273 7
accuracy 0.9062 32
 macro avg 0.9464 0.7857 0.8353 32
weighted avg 0.9163 0.9062 0.8961 32
INFO:__main__: precision recall f1-score support
0 0.9048 0.9500 0.9268 20
 1 0.9091 0.8333 0.8696 12
accuracy 0.9062 32
 macro avg 0.9069 0.8917 0.8982 32
weighted avg 0.9064 0.9062 0.9054 32
INFO:__main__: precision recall f1-score support
0 0.8889 0.9600 0.9231 25
 1 0.8000 0.5714 0.6667 7
accuracy 0.8750 32
 macro avg 0.8444 0.7657 0.7949 32
weighted avg 0.8694 0.8750 0.8670 32
INFO:__main__: precision recall f1-score support
0 0.7826 0.9000 0.8372 20
 1 0.7778 0.5833 0.6667 12
accuracy 0.7812 32
 macro avg 0.7802 0.7417 0.7519 32
weighted avg 0.7808 0.7812 0.7733 32
INFO:__main__: precision recall f1-score support
0 0.9630 0.9630 0.9630 27
 1 0.8000 0.8000 0.8000 5
accuracy 0.9375 32
 macro avg 0.8815 0.8815 0.8815 32
weighted avg 0.9375 0.9375 0.9375 32
INFO:__main__: precision recall f1-score support
0 0.8519 0.9583 0.9020 24
 1 0.8000 0.5000 0.6154 8
accuracy 0.8438 32
 macro avg 0.8259 0.7292 0.7587 32
weighted avg 0.8389 0.8438 0.8303 32
INFO:__main__: precision recall f1-score support
0 0.9583 0.9200 0.9388 25
 1 0.7500 0.8571 0.8000 7
accuracy 0.9062 32
 macro avg 0.8542 0.8886 0.8694 32
weighted avg 0.9128 0.9062 0.9084 32
INFO:__main__: precision recall f1-score support
0 0.9524 0.9524 0.9524 21
 1 0.9091 0.9091 0.9091 11
accuracy 0.9375 32
 macro avg 0.9307 0.9307 0.9307 32
weighted avg 0.9375 0.9375 0.9375 32
INFO:__main__: precision recall f1-score support
0 0.9259 0.9615 0.9434 26
 1 0.8000 0.6667 0.7273 6
accuracy 0.9062 32
 macro avg 0.8630 0.8141 0.8353 32
weighted avg 0.9023 0.9062 0.9029 32
INFO:__main__: precision recall f1-score support
0 0.9000 0.7826 0.8372 23
 1 0.5833 0.7778 0.6667 9
accuracy 0.7812 32
 macro avg 0.7417 0.7802 0.7519 32
weighted avg 0.8109 0.7812 0.7892 32
INFO:__main__: precision recall f1-score support
0 0.9231 0.8276 0.8727 29
 1 0.1667 0.3333 0.2222 3
accuracy 0.7812 32
 macro avg 0.5449 0.5805 0.5475 32
weighted avg 0.8522 0.7812 0.8117 32
INFO:__main__: precision recall f1-score support
0 0.8095 0.8947 0.8500 19
 1 0.8182 0.6923 0.7500 13
accuracy 0.8125 32
 macro avg 0.8139 0.7935 0.8000 32
weighted avg 0.8130 0.8125 0.8094 32
INFO:__main__: precision recall f1-score support
0 0.9167 0.9167 0.9167 24
 1 0.7500 0.7500 0.7500 8
accuracy 0.8750 32
 macro avg 0.8333 0.8333 0.8333 32
weighted avg 0.8750 0.8750 0.8750 32
INFO:__main__: precision recall f1-score support
0 0.8889 0.9600 0.9231 25
 1 0.8000 0.5714 0.6667 7
accuracy 0.8750 32
 macro avg 0.8444 0.7657 0.7949 32
weighted avg 0.8694 0.8750 0.8670 32
INFO:__main__: precision recall f1-score support
0 1.0000 0.9500 0.9744 20
 1 0.8889 1.0000 0.9412 8
accuracy 0.9643 28
 macro avg 0.9444 0.9750 0.9578 28
weighted avg 0.9683 0.9643 0.9649 28
INFO:__main__: Accuracy: 0.86
INFO:__main__: Validation took: 0:00:02
INFO:__main__:
INFO:__main__:Evaluation on full prediction per epoch!
INFO:__main__:Gold labels860
INFO:__main__:Predicted labels860
INFO:__main__: precision recall f1-score support
0 0.8807 0.9290 0.9042 620
 1 0.7864 0.6750 0.7265 240
accuracy 0.8581 860
 macro avg 0.8336 0.8020 0.8153 860
weighted avg 0.8544 0.8581 0.8546 860
INFO:__main__:
INFO:__main__:======== Epoch 3 / 5 ========
INFO:__main__:Training...
INFO:__main__: Batch 40 of 414. Elapsed: 0:00:09.
INFO:__main__: Batch 80 of 414. Elapsed: 0:00:17.
INFO:__main__: Batch 120 of 414. Elapsed: 0:00:26.
INFO:__main__: Batch 160 of 414. Elapsed: 0:00:34.
INFO:__main__: Batch 200 of 414. Elapsed: 0:00:43.
INFO:__main__: Batch 240 of 414. Elapsed: 0:00:51.
INFO:__main__: Batch 280 of 414. Elapsed: 0:01:00.
INFO:__main__: Batch 320 of 414. Elapsed: 0:01:09.
INFO:__main__: Batch 360 of 414. Elapsed: 0:01:17.
INFO:__main__: Batch 400 of 414. Elapsed: 0:01:27.
INFO:__main__:
INFO:__main__: Average training loss: 0.35
INFO:__main__: Training epcoh took: 0:01:30
INFO:__main__:
INFO:__main__:Running Validation...
INFO:__main__: precision recall f1-score support
0 0.8750 0.9130 0.8936 23
 1 0.7500 0.6667 0.7059 9
accuracy 0.8438 32
 macro avg 0.8125 0.7899 0.7997 32
weighted avg 0.8398 0.8438 0.8408 32
INFO:__main__: precision recall f1-score support
0 0.9500 0.8636 0.9048 22
 1 0.7500 0.9000 0.8182 10
accuracy 0.8750 32
 macro avg 0.8500 0.8818 0.8615 32
weighted avg 0.8875 0.8750 0.8777 32
INFO:__main__: precision recall f1-score support
0 0.9524 0.8696 0.9091 23
 1 0.7273 0.8889 0.8000 9
accuracy 0.8750 32
 macro avg 0.8398 0.8792 0.8545 32
weighted avg 0.8891 0.8750 0.8784 32
INFO:__main__: precision recall f1-score support
0 0.9583 0.9200 0.9388 25
 1 0.7500 0.8571 0.8000 7
accuracy 0.9062 32
 macro avg 0.8542 0.8886 0.8694 32
weighted avg 0.9128 0.9062 0.9084 32
INFO:__main__: precision recall f1-score support
0 0.6957 0.8421 0.7619 19
 1 0.6667 0.4615 0.5455 13
accuracy 0.6875 32
 macro avg 0.6812 0.6518 0.6537 32
weighted avg 0.6839 0.6875 0.6740 32
INFO:__main__: precision recall f1-score support
0 0.7778 1.0000 0.8750 21
 1 1.0000 0.4545 0.6250 11
accuracy 0.8125 32
 macro avg 0.8889 0.7273 0.7500 32
weighted avg 0.8542 0.8125 0.7891 32
INFO:__main__: precision recall f1-score support
0 0.9545 0.9545 0.9545 22
 1 0.9000 0.9000 0.9000 10
accuracy 0.9375 32
 macro avg 0.9273 0.9273 0.9273 32
weighted avg 0.9375 0.9375 0.9375 32
INFO:__main__: precision recall f1-score support
0 0.9167 0.8800 0.8980 25
 1 0.6250 0.7143 0.6667 7
accuracy 0.8438 32
 macro avg 0.7708 0.7971 0.7823 32
weighted avg 0.8529 0.8438 0.8474 32
INFO:__main__: precision recall f1-score support
0 0.7308 0.9500 0.8261 20
 1 0.8333 0.4167 0.5556 12
accuracy 0.7500 32
 macro avg 0.7821 0.6833 0.6908 32
weighted avg 0.7692 0.7500 0.7246 32
INFO:__main__: precision recall f1-score support
0 0.9167 0.9565 0.9362 23
 1 0.8750 0.7778 0.8235 9
accuracy 0.9062 32
 macro avg 0.8958 0.8671 0.8798 32
weighted avg 0.9049 0.9062 0.9045 32
INFO:__main__: precision recall f1-score support
0 0.8182 0.8182 0.8182 22
 1 0.6000 0.6000 0.6000 10
accuracy 0.7500 32
 macro avg 0.7091 0.7091 0.7091 32
weighted avg 0.7500 0.7500 0.7500 32
INFO:__main__: precision recall f1-score support
0 0.8000 0.9091 0.8511 22
 1 0.7143 0.5000 0.5882 10
accuracy 0.7812 32
 macro avg 0.7571 0.7045 0.7196 32
weighted avg 0.7732 0.7812 0.7689 32
INFO:__main__: precision recall f1-score support
0 0.8929 1.0000 0.9434 25
 1 1.0000 0.5714 0.7273 7
accuracy 0.9062 32
 macro avg 0.9464 0.7857 0.8353 32
weighted avg 0.9163 0.9062 0.8961 32
INFO:__main__: precision recall f1-score support
0 0.9048 0.9500 0.9268 20
 1 0.9091 0.8333 0.8696 12
accuracy 0.9062 32
 macro avg 0.9069 0.8917 0.8982 32
weighted avg 0.9064 0.9062 0.9054 32
INFO:__main__: precision recall f1-score support
0 0.8889 0.9600 0.9231 25
 1 0.8000 0.5714 0.6667 7
accuracy 0.8750 32
 macro avg 0.8444 0.7657 0.7949 32
weighted avg 0.8694 0.8750 0.8670 32
INFO:__main__: precision recall f1-score support
0 0.7619 0.8000 0.7805 20
 1 0.6364 0.5833 0.6087 12
accuracy 0.7188 32
 macro avg 0.6991 0.6917 0.6946 32
weighted avg 0.7148 0.7188 0.7161 32
INFO:__main__: precision recall f1-score support
0 0.9630 0.9630 0.9630 27
 1 0.8000 0.8000 0.8000 5
accuracy 0.9375 32
 macro avg 0.8815 0.8815 0.8815 32
weighted avg 0.9375 0.9375 0.9375 32
INFO:__main__: precision recall f1-score support
0 0.8400 0.8750 0.8571 24
 1 0.5714 0.5000 0.5333 8
accuracy 0.7812 32
 macro avg 0.7057 0.6875 0.6952 32
weighted avg 0.7729 0.7812 0.7762 32
INFO:__main__: precision recall f1-score support
0 0.9583 0.9200 0.9388 25
 1 0.7500 0.8571 0.8000 7
accuracy 0.9062 32
 macro avg 0.8542 0.8886 0.8694 32
weighted avg 0.9128 0.9062 0.9084 32
INFO:__main__: precision recall f1-score support
0 0.9524 0.9524 0.9524 21
 1 0.9091 0.9091 0.9091 11
accuracy 0.9375 32
 macro avg 0.9307 0.9307 0.9307 32
weighted avg 0.9375 0.9375 0.9375 32
INFO:__main__: precision recall f1-score support
0 0.9259 0.9615 0.9434 26
 1 0.8000 0.6667 0.7273 6
accuracy 0.9062 32
 macro avg 0.8630 0.8141 0.8353 32
weighted avg 0.9023 0.9062 0.9029 32
INFO:__main__: precision recall f1-score support
0 0.9048 0.8261 0.8636 23
 1 0.6364 0.7778 0.7000 9
accuracy 0.8125 32
 macro avg 0.7706 0.8019 0.7818 32
weighted avg 0.8293 0.8125 0.8176 32
INFO:__main__: precision recall f1-score support
0 0.9231 0.8276 0.8727 29
 1 0.1667 0.3333 0.2222 3
accuracy 0.7812 32
 macro avg 0.5449 0.5805 0.5475 32
weighted avg 0.8522 0.7812 0.8117 32
INFO:__main__: precision recall f1-score support
0 0.8000 0.8421 0.8205 19
 1 0.7500 0.6923 0.7200 13
accuracy 0.7812 32
 macro avg 0.7750 0.7672 0.7703 32
weighted avg 0.7797 0.7812 0.7797 32
INFO:__main__: precision recall f1-score support
0 0.9167 0.9167 0.9167 24
 1 0.7500 0.7500 0.7500 8
accuracy 0.8750 32
 macro avg 0.8333 0.8333 0.8333 32
weighted avg 0.8750 0.8750 0.8750 32
INFO:__main__: precision recall f1-score support
0 0.8889 0.9600 0.9231 25
 1 0.8000 0.5714 0.6667 7
accuracy 0.8750 32
 macro avg 0.8444 0.7657 0.7949 32
weighted avg 0.8694 0.8750 0.8670 32
INFO:__main__: precision recall f1-score support
0 1.0000 0.9500 0.9744 20
 1 0.8889 1.0000 0.9412 8
accuracy 0.9643 28
 macro avg 0.9444 0.9750 0.9578 28
weighted avg 0.9683 0.9643 0.9649 28
INFO:__main__: Accuracy: 0.85
INFO:__main__: Validation took: 0:00:02
INFO:__main__:
INFO:__main__:Evaluation on full prediction per epoch!
INFO:__main__:Gold labels860
INFO:__main__:Predicted labels860
INFO:__main__: precision recall f1-score support
0 0.8828 0.9113 0.8968 620
 1 0.7500 0.6875 0.7174 240
accuracy 0.8488 860
 macro avg 0.8164 0.7994 0.8071 860
weighted avg 0.8457 0.8488 0.8468 860
INFO:__main__:
INFO:__main__:======== Epoch 4 / 5 ========
INFO:__main__:Training...
INFO:__main__: Batch 40 of 414. Elapsed: 0:00:09.
INFO:__main__: Batch 80 of 414. Elapsed: 0:00:17.
INFO:__main__: Batch 120 of 414. Elapsed: 0:00:26.
INFO:__main__: Batch 160 of 414. Elapsed: 0:00:35.
INFO:__main__: Batch 200 of 414. Elapsed: 0:00:43.
INFO:__main__: Batch 240 of 414. Elapsed: 0:00:52.
INFO:__main__: Batch 280 of 414. Elapsed: 0:01:04.
INFO:__main__: Batch 320 of 414. Elapsed: 0:01:12.
INFO:__main__: Batch 360 of 414. Elapsed: 0:01:21.
INFO:__main__: Batch 400 of 414. Elapsed: 0:01:29.
INFO:__main__:
INFO:__main__: Average training loss: 0.31
INFO:__main__: Training epcoh took: 0:01:32
INFO:__main__:
INFO:__main__:Running Validation...
INFO:__main__: precision recall f1-score support
0 0.8750 0.9130 0.8936 23
 1 0.7500 0.6667 0.7059 9
accuracy 0.8438 32
 macro avg 0.8125 0.7899 0.7997 32
weighted avg 0.8398 0.8438 0.8408 32
INFO:__main__: precision recall f1-score support
0 0.9500 0.8636 0.9048 22
 1 0.7500 0.9000 0.8182 10
accuracy 0.8750 32
 macro avg 0.8500 0.8818 0.8615 32
weighted avg 0.8875 0.8750 0.8777 32
INFO:__main__: precision recall f1-score support
0 0.9524 0.8696 0.9091 23
 1 0.7273 0.8889 0.8000 9
accuracy 0.8750 32
 macro avg 0.8398 0.8792 0.8545 32
weighted avg 0.8891 0.8750 0.8784 32
INFO:__main__: precision recall f1-score support
0 0.9600 0.9600 0.9600 25
 1 0.8571 0.8571 0.8571 7
accuracy 0.9375 32
 macro avg 0.9086 0.9086 0.9086 32
weighted avg 0.9375 0.9375 0.9375 32
INFO:__main__: precision recall f1-score support
0 0.7273 0.8421 0.7805 19
 1 0.7000 0.5385 0.6087 13
accuracy 0.7188 32
 macro avg 0.7136 0.6903 0.6946 32
weighted avg 0.7162 0.7188 0.7107 32
INFO:__main__: precision recall f1-score support
0 0.8077 1.0000 0.8936 21
 1 1.0000 0.5455 0.7059 11
accuracy 0.8438 32
 macro avg 0.9038 0.7727 0.7997 32
weighted avg 0.8738 0.8438 0.8291 32
INFO:__main__: precision recall f1-score support
0 1.0000 0.9545 0.9767 22
 1 0.9091 1.0000 0.9524 10
accuracy 0.9688 32
 macro avg 0.9545 0.9773 0.9646 32
weighted avg 0.9716 0.9688 0.9691 32
INFO:__main__: precision recall f1-score support
0 0.8846 0.9200 0.9020 25
 1 0.6667 0.5714 0.6154 7
accuracy 0.8438 32
 macro avg 0.7756 0.7457 0.7587 32
weighted avg 0.8369 0.8438 0.8393 32
INFO:__main__: precision recall f1-score support
0 0.7308 0.9500 0.8261 20
 1 0.8333 0.4167 0.5556 12
accuracy 0.7500 32
 macro avg 0.7821 0.6833 0.6908 32
weighted avg 0.7692 0.7500 0.7246 32
INFO:__main__: precision recall f1-score support
0 0.9167 0.9565 0.9362 23
 1 0.8750 0.7778 0.8235 9
accuracy 0.9062 32
 macro avg 0.8958 0.8671 0.8798 32
weighted avg 0.9049 0.9062 0.9045 32
INFO:__main__: precision recall f1-score support
0 0.8182 0.8182 0.8182 22
 1 0.6000 0.6000 0.6000 10
accuracy 0.7500 32
 macro avg 0.7091 0.7091 0.7091 32
weighted avg 0.7500 0.7500 0.7500 32
INFO:__main__: precision recall f1-score support
0 0.8333 0.9091 0.8696 22
 1 0.7500 0.6000 0.6667 10
accuracy 0.8125 32
 macro avg 0.7917 0.7545 0.7681 32
weighted avg 0.8073 0.8125 0.8062 32
INFO:__main__: precision recall f1-score support
0 0.8846 0.9200 0.9020 25
 1 0.6667 0.5714 0.6154 7
accuracy 0.8438 32
 macro avg 0.7756 0.7457 0.7587 32
weighted avg 0.8369 0.8438 0.8393 32
INFO:__main__: precision recall f1-score support
0 0.9048 0.9500 0.9268 20
 1 0.9091 0.8333 0.8696 12
accuracy 0.9062 32
 macro avg 0.9069 0.8917 0.8982 32
weighted avg 0.9064 0.9062 0.9054 32
INFO:__main__: precision recall f1-score support
0 0.8889 0.9600 0.9231 25
 1 0.8000 0.5714 0.6667 7
accuracy 0.8750 32
 macro avg 0.8444 0.7657 0.7949 32
weighted avg 0.8694 0.8750 0.8670 32
INFO:__main__: precision recall f1-score support
0 0.7619 0.8000 0.7805 20
 1 0.6364 0.5833 0.6087 12
accuracy 0.7188 32
 macro avg 0.6991 0.6917 0.6946 32
weighted avg 0.7148 0.7188 0.7161 32
INFO:__main__: precision recall f1-score support
0 0.9630 0.9630 0.9630 27
 1 0.8000 0.8000 0.8000 5
accuracy 0.9375 32
 macro avg 0.8815 0.8815 0.8815 32
weighted avg 0.9375 0.9375 0.9375 32
INFO:__main__: precision recall f1-score support
0 0.8750 0.8750 0.8750 24
 1 0.6250 0.6250 0.6250 8
accuracy 0.8125 32
 macro avg 0.7500 0.7500 0.7500 32
weighted avg 0.8125 0.8125 0.8125 32
INFO:__main__: precision recall f1-score support
0 1.0000 0.9200 0.9583 25
 1 0.7778 1.0000 0.8750 7
accuracy 0.9375 32
 macro avg 0.8889 0.9600 0.9167 32
weighted avg 0.9514 0.9375 0.9401 32
INFO:__main__: precision recall f1-score support
0 0.9524 0.9524 0.9524 21
 1 0.9091 0.9091 0.9091 11
accuracy 0.9375 32
 macro avg 0.9307 0.9307 0.9307 32
weighted avg 0.9375 0.9375 0.9375 32
INFO:__main__: precision recall f1-score support
0 0.9259 0.9615 0.9434 26
 1 0.8000 0.6667 0.7273 6
accuracy 0.9062 32
 macro avg 0.8630 0.8141 0.8353 32
weighted avg 0.9023 0.9062 0.9029 32
INFO:__main__: precision recall f1-score support
0 0.9048 0.8261 0.8636 23
 1 0.6364 0.7778 0.7000 9
accuracy 0.8125 32
 macro avg 0.7706 0.8019 0.7818 32
weighted avg 0.8293 0.8125 0.8176 32
INFO:__main__: precision recall f1-score support
0 0.9231 0.8276 0.8727 29
 1 0.1667 0.3333 0.2222 3
accuracy 0.7812 32
 macro avg 0.5449 0.5805 0.5475 32
weighted avg 0.8522 0.7812 0.8117 32
INFO:__main__: precision recall f1-score support
0 0.8000 0.8421 0.8205 19
 1 0.7500 0.6923 0.7200 13
accuracy 0.7812 32
 macro avg 0.7750 0.7672 0.7703 32
weighted avg 0.7797 0.7812 0.7797 32
INFO:__main__: precision recall f1-score support
0 0.9524 0.8333 0.8889 24
 1 0.6364 0.8750 0.7368 8
accuracy 0.8438 32
 macro avg 0.7944 0.8542 0.8129 32
weighted avg 0.8734 0.8438 0.8509 32
INFO:__main__: precision recall f1-score support
0 0.8889 0.9600 0.9231 25
 1 0.8000 0.5714 0.6667 7
accuracy 0.8750 32
 macro avg 0.8444 0.7657 0.7949 32
weighted avg 0.8694 0.8750 0.8670 32
INFO:__main__: precision recall f1-score support
0 1.0000 0.9500 0.9744 20
 1 0.8889 1.0000 0.9412 8
accuracy 0.9643 28
 macro avg 0.9444 0.9750 0.9578 28
weighted avg 0.9683 0.9643 0.9649 28
INFO:__main__: Accuracy: 0.85
INFO:__main__: Validation took: 0:00:02
INFO:__main__:
INFO:__main__:Evaluation on full prediction per epoch!
INFO:__main__:Gold labels860
INFO:__main__:Predicted labels860
INFO:__main__: precision recall f1-score support
0 0.8908 0.9081 0.8994 620
 1 0.7500 0.7125 0.7308 240
accuracy 0.8535 860
 macro avg 0.8204 0.8103 0.8151 860
weighted avg 0.8515 0.8535 0.8523 860
INFO:__main__:
INFO:__main__:======== Epoch 5 / 5 ========
INFO:__main__:Training...
INFO:__main__: Batch 40 of 414. Elapsed: 0:00:09.
INFO:__main__: Batch 80 of 414. Elapsed: 0:00:17.
INFO:__main__: Batch 120 of 414. Elapsed: 0:00:26.
INFO:__main__: Batch 160 of 414. Elapsed: 0:00:34.
INFO:__main__: Batch 200 of 414. Elapsed: 0:00:43.
INFO:__main__: Batch 240 of 414. Elapsed: 0:00:51.
INFO:__main__: Batch 280 of 414. Elapsed: 0:01:00.
INFO:__main__: Batch 320 of 414. Elapsed: 0:01:08.
INFO:__main__: Batch 360 of 414. Elapsed: 0:01:19.
INFO:__main__: Batch 400 of 414. Elapsed: 0:01:28.
INFO:__main__:
INFO:__main__: Average training loss: 0.29
INFO:__main__: Training epcoh took: 0:01:31
INFO:__main__:
INFO:__main__:Running Validation...
INFO:__main__: precision recall f1-score support
0 0.8750 0.9130 0.8936 23
 1 0.7500 0.6667 0.7059 9
accuracy 0.8438 32
 macro avg 0.8125 0.7899 0.7997 32
weighted avg 0.8398 0.8438 0.8408 32
INFO:__main__: precision recall f1-score support
0 0.9500 0.8636 0.9048 22
 1 0.7500 0.9000 0.8182 10
accuracy 0.8750 32
 macro avg 0.8500 0.8818 0.8615 32
weighted avg 0.8875 0.8750 0.8777 32
INFO:__main__: precision recall f1-score support
0 0.9524 0.8696 0.9091 23
 1 0.7273 0.8889 0.8000 9
accuracy 0.8750 32
 macro avg 0.8398 0.8792 0.8545 32
weighted avg 0.8891 0.8750 0.8784 32
INFO:__main__: precision recall f1-score support
0 0.9600 0.9600 0.9600 25
 1 0.8571 0.8571 0.8571 7
accuracy 0.9375 32
 macro avg 0.9086 0.9086 0.9086 32
weighted avg 0.9375 0.9375 0.9375 32
INFO:__main__: precision recall f1-score support
0 0.7619 0.8421 0.8000 19
 1 0.7273 0.6154 0.6667 13
accuracy 0.7500 32
 macro avg 0.7446 0.7287 0.7333 32
weighted avg 0.7478 0.7500 0.7458 32
INFO:__main__: precision recall f1-score support
0 0.8077 1.0000 0.8936 21
 1 1.0000 0.5455 0.7059 11
accuracy 0.8438 32
 macro avg 0.9038 0.7727 0.7997 32
weighted avg 0.8738 0.8438 0.8291 32
INFO:__main__: precision recall f1-score support
0 1.0000 0.9545 0.9767 22
 1 0.9091 1.0000 0.9524 10
accuracy 0.9688 32
 macro avg 0.9545 0.9773 0.9646 32
weighted avg 0.9716 0.9688 0.9691 32
INFO:__main__: precision recall f1-score support
0 0.8846 0.9200 0.9020 25
 1 0.6667 0.5714 0.6154 7
accuracy 0.8438 32
 macro avg 0.7756 0.7457 0.7587 32
weighted avg 0.8369 0.8438 0.8393 32
INFO:__main__: precision recall f1-score support
0 0.7308 0.9500 0.8261 20
 1 0.8333 0.4167 0.5556 12
accuracy 0.7500 32
 macro avg 0.7821 0.6833 0.6908 32
weighted avg 0.7692 0.7500 0.7246 32
INFO:__main__: precision recall f1-score support
0 0.9167 0.9565 0.9362 23
 1 0.8750 0.7778 0.8235 9
accuracy 0.9062 32
 macro avg 0.8958 0.8671 0.8798 32
weighted avg 0.9049 0.9062 0.9045 32
INFO:__main__: precision recall f1-score support
0 0.7917 0.8636 0.8261 22
 1 0.6250 0.5000 0.5556 10
accuracy 0.7500 32
 macro avg 0.7083 0.6818 0.6908 32
weighted avg 0.7396 0.7500 0.7415 32
INFO:__main__: precision recall f1-score support
0 0.8333 0.9091 0.8696 22
 1 0.7500 0.6000 0.6667 10
accuracy 0.8125 32
 macro avg 0.7917 0.7545 0.7681 32
weighted avg 0.8073 0.8125 0.8062 32
INFO:__main__: precision recall f1-score support
0 0.8889 0.9600 0.9231 25
 1 0.8000 0.5714 0.6667 7
accuracy 0.8750 32
 macro avg 0.8444 0.7657 0.7949 32
weighted avg 0.8694 0.8750 0.8670 32
INFO:__main__: precision recall f1-score support
0 0.9048 0.9500 0.9268 20
 1 0.9091 0.8333 0.8696 12
accuracy 0.9062 32
 macro avg 0.9069 0.8917 0.8982 32
weighted avg 0.9064 0.9062 0.9054 32
INFO:__main__: precision recall f1-score support
0 0.8889 0.9600 0.9231 25
 1 0.8000 0.5714 0.6667 7
accuracy 0.8750 32
 macro avg 0.8444 0.7657 0.7949 32
weighted avg 0.8694 0.8750 0.8670 32
INFO:__main__: precision recall f1-score support
0 0.7619 0.8000 0.7805 20
 1 0.6364 0.5833 0.6087 12
accuracy 0.7188 32
 macro avg 0.6991 0.6917 0.6946 32
weighted avg 0.7148 0.7188 0.7161 32
INFO:__main__: precision recall f1-score support
0 0.9630 0.9630 0.9630 27
 1 0.8000 0.8000 0.8000 5
accuracy 0.9375 32
 macro avg 0.8815 0.8815 0.8815 32
weighted avg 0.9375 0.9375 0.9375 32
INFO:__main__: precision recall f1-score support
0 0.8750 0.8750 0.8750 24
 1 0.6250 0.6250 0.6250 8
accuracy 0.8125 32
 macro avg 0.7500 0.7500 0.7500 32
weighted avg 0.8125 0.8125 0.8125 32
INFO:__main__: precision recall f1-score support
0 1.0000 0.9200 0.9583 25
 1 0.7778 1.0000 0.8750 7
accuracy 0.9375 32
 macro avg 0.8889 0.9600 0.9167 32
weighted avg 0.9514 0.9375 0.9401 32
INFO:__main__: precision recall f1-score support
0 0.9524 0.9524 0.9524 21
 1 0.9091 0.9091 0.9091 11
accuracy 0.9375 32
 macro avg 0.9307 0.9307 0.9307 32
weighted avg 0.9375 0.9375 0.9375 32
INFO:__main__: precision recall f1-score support
0 0.9259 0.9615 0.9434 26
 1 0.8000 0.6667 0.7273 6
accuracy 0.9062 32
 macro avg 0.8630 0.8141 0.8353 32
weighted avg 0.9023 0.9062 0.9029 32
INFO:__main__: precision recall f1-score support
0 0.9091 0.8696 0.8889 23
 1 0.7000 0.7778 0.7368 9
accuracy 0.8438 32
 macro avg 0.8045 0.8237 0.8129 32
weighted avg 0.8503 0.8438 0.8461 32
INFO:__main__: precision recall f1-score support
0 0.9231 0.8276 0.8727 29
 1 0.1667 0.3333 0.2222 3
accuracy 0.7812 32
 macro avg 0.5449 0.5805 0.5475 32
weighted avg 0.8522 0.7812 0.8117 32
INFO:__main__: precision recall f1-score support
0 0.8000 0.8421 0.8205 19
 1 0.7500 0.6923 0.7200 13
accuracy 0.7812 32
 macro avg 0.7750 0.7672 0.7703 32
weighted avg 0.7797 0.7812 0.7797 32
INFO:__main__: precision recall f1-score support
0 0.9545 0.8750 0.9130 24
 1 0.7000 0.8750 0.7778 8
accuracy 0.8750 32
 macro avg 0.8273 0.8750 0.8454 32
weighted avg 0.8909 0.8750 0.8792 32
INFO:__main__: precision recall f1-score support
0 0.8889 0.9600 0.9231 25
 1 0.8000 0.5714 0.6667 7
accuracy 0.8750 32
 macro avg 0.8444 0.7657 0.7949 32
weighted avg 0.8694 0.8750 0.8670 32
INFO:__main__: precision recall f1-score support
0 1.0000 0.9500 0.9744 20
 1 0.8889 1.0000 0.9412 8
accuracy 0.9643 28
 macro avg 0.9444 0.9750 0.9578 28
weighted avg 0.9683 0.9643 0.9649 28
INFO:__main__: Accuracy: 0.86
INFO:__main__: Validation took: 0:00:02
INFO:__main__:
INFO:__main__:Evaluation on full prediction per epoch!
INFO:__main__:Gold labels860
INFO:__main__:Predicted labels860
INFO:__main__: precision recall f1-score support
0 0.8915 0.9145 0.9029 620
 1 0.7634 0.7125 0.7371 240
accuracy 0.8581 860
 macro avg 0.8275 0.8135 0.8200 860
weighted avg 0.8558 0.8581 0.8566 860
INFO:__main__:
INFO:__main__:Training complete!
INFO:__main__:Saving model to: /data/p281734/reddish_abuseval/models/offenseval_retrain/2020-11-07_00:11:11.673161/
INFO:__main__:# Saving best-practices: if you use save_pretrained for the model and tokenizer, you can reload them using from_pretrained()
INFO:transformers.configuration_utils:Configuration saved in /data/p281734/reddish_abuseval/models/offenseval_retrain/2020-11-07_00:11:11.673161/config.json
INFO:transformers.modeling_utils:Model weights saved in /data/p281734/reddish_abuseval/models/offenseval_retrain/2020-11-07_00:11:11.673161/pytorch_model.bin