output.txt

Conda uses environments to load different sets of Python packages
type conda env list to see the environments availible.
07/18/2022 19:19:09 - WARNING - run_glue_f1_macro -   Process rank: -1, device: cuda:0, n_gpu: 3distributed training: False, 16-bits training: False
07/18/2022 19:19:09 - INFO - run_glue_f1_macro -   Training/evaluation parameters TrainingArguments(
_n_gpu=3,
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
dataloader_drop_last=False,
dataloader_num_workers=0,
dataloader_pin_memory=True,
ddp_find_unused_parameters=None,
debug=[],
deepspeed=None,
disable_tqdm=False,
do_eval=True,
do_predict=True,
do_train=True,
eval_accumulation_steps=None,
eval_steps=500,
evaluation_strategy=IntervalStrategy.EPOCH,
fp16=False,
fp16_backend=auto,
fp16_full_eval=False,
fp16_opt_level=O1,
gradient_accumulation_steps=1,
greater_is_better=True,
group_by_length=False,
ignore_data_skip=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=2e-05,
length_column_name=length,
load_best_model_at_end=True,
local_rank=-1,
log_on_each_node=True,
logging_dir=runs/Jul18_19-19-09_sharc-node100.shef.ac.uk,
logging_first_step=False,
logging_steps=500,
logging_strategy=IntervalStrategy.STEPS,
lr_scheduler_type=SchedulerType.LINEAR,
max_grad_norm=1.0,
max_steps=-1,
metric_for_best_model=f1,
mp_parameters=,
no_cuda=False,
num_train_epochs=9.0,
output_dir=./checkpoints,
overwrite_output_dir=False,
past_index=-1,
per_device_eval_batch_size=8,
per_device_train_batch_size=32,
prediction_loss_only=False,
push_to_hub=False,
remove_unused_columns=True,
report_to=[],
resume_from_checkpoint=None,
run_name=./checkpoints,
save_steps=500,
save_strategy=IntervalStrategy.EPOCH,
save_total_limit=3,
seed=26,
sharded_ddp=[],
skip_memory_metrics=True,
tpu_metrics_debug=False,
tpu_num_cores=None,
use_legacy_prediction_loop=False,
warmup_ratio=0.0,
warmup_steps=0,
weight_decay=0.0,
)
07/18/2022 19:19:09 - INFO - run_glue_f1_macro -   load a local file for train: ./tmp/train.csv
07/18/2022 19:19:09 - INFO - run_glue_f1_macro -   load a local file for validation: ./tmp/dev.csv
07/18/2022 19:19:09 - INFO - run_glue_f1_macro -   load a local file for test: ./tmp/test.csv
07/18/2022 19:19:09 - WARNING - datasets.builder -   Using custom data configuration default-9010ced73959fac9
07/18/2022 19:19:09 - WARNING - datasets.builder -   Reusing dataset csv (/home/acp20dah/.cache/huggingface/datasets/csv/default-9010ced73959fac9/0.0.0/2dc6629a9ff6b5697d82c25b73731dd440507a69cbce8b425db50b751e8fcfd0)
[INFO|configuration_utils.py:517] 2022-07-18 19:19:10,123 >> loading configuration file https://huggingface.co/bert-base-cased/resolve/main/config.json from cache at /home/acp20dah/.cache/huggingface/transformers/a803e0468a8fe090683bdc453f4fac622804f49de86d7cecaee92365d4a0f829.a64a22196690e0e82ead56f388a3ef3a50de93335926ccfa20610217db589307
[INFO|configuration_utils.py:553] 2022-07-18 19:19:10,125 >> Model config BertConfig {
  "architectures": [
    "BertForMaskedLM"
  ],
  "attention_probs_dropout_prob": 0.1,
  "gradient_checkpointing": false,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "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",
  "transformers_version": "4.7.0",
  "type_vocab_size": 2,
  "use_cache": true,
  "vocab_size": 28996
}

[INFO|configuration_utils.py:517] 2022-07-18 19:19:10,526 >> loading configuration file https://huggingface.co/bert-base-cased/resolve/main/config.json from cache at /home/acp20dah/.cache/huggingface/transformers/a803e0468a8fe090683bdc453f4fac622804f49de86d7cecaee92365d4a0f829.a64a22196690e0e82ead56f388a3ef3a50de93335926ccfa20610217db589307
[INFO|configuration_utils.py:553] 2022-07-18 19:19:10,527 >> Model config BertConfig {
  "architectures": [
    "BertForMaskedLM"
  ],
  "attention_probs_dropout_prob": 0.1,
  "gradient_checkpointing": false,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "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",
  "transformers_version": "4.7.0",
  "type_vocab_size": 2,
  "use_cache": true,
  "vocab_size": 28996
}

[INFO|tokenization_utils_base.py:1717] 2022-07-18 19:19:12,449 >> loading file https://huggingface.co/bert-base-cased/resolve/main/vocab.txt from cache at /home/acp20dah/.cache/huggingface/transformers/6508e60ab3c1200bffa26c95f4b58ac6b6d95fba4db1f195f632fa3cd7bc64cc.437aa611e89f6fc6675a049d2b5545390adbc617e7d655286421c191d2be2791
[INFO|tokenization_utils_base.py:1717] 2022-07-18 19:19:12,450 >> loading file https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json from cache at /home/acp20dah/.cache/huggingface/transformers/226a307193a9f4344264cdc76a12988448a25345ba172f2c7421f3b6810fddad.3dab63143af66769bbb35e3811f75f7e16b2320e12b7935e216bd6159ce6d9a6
[INFO|tokenization_utils_base.py:1717] 2022-07-18 19:19:12,450 >> loading file https://huggingface.co/bert-base-cased/resolve/main/added_tokens.json from cache at None
[INFO|tokenization_utils_base.py:1717] 2022-07-18 19:19:12,450 >> loading file https://huggingface.co/bert-base-cased/resolve/main/special_tokens_map.json from cache at None
[INFO|tokenization_utils_base.py:1717] 2022-07-18 19:19:12,451 >> loading file https://huggingface.co/bert-base-cased/resolve/main/tokenizer_config.json from cache at /home/acp20dah/.cache/huggingface/transformers/ec84e86ee39bfe112543192cf981deebf7e6cbe8c91b8f7f8f63c9be44366158.ec5c189f89475aac7d8cbd243960a0655cfadc3d0474da8ff2ed0bf1699c2a5f
[INFO|modeling_utils.py:1152] 2022-07-18 19:19:12,887 >> loading weights file https://huggingface.co/bert-base-cased/resolve/main/pytorch_model.bin from cache at /home/acp20dah/.cache/huggingface/transformers/092cc582560fc3833e556b3f833695c26343cb54b7e88cd02d40821462a74999.1f48cab6c959fc6c360d22bea39d06959e90f5b002e77e836d2da45464875cda
[WARNING|modeling_utils.py:1327] 2022-07-18 19:19:17,994 >> Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias']
- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
[WARNING|modeling_utils.py:1338] 2022-07-18 19:19:17,994 >> Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
07/18/2022 19:19:17 - WARNING - datasets.fingerprint -   Parameter 'function'=<function main.<locals>.preprocess_function at 0x2ae3d616bf70> 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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100%|██████████| 5/5 [00:00<00:00, 13.22ba/s]07/18/2022 19:19:21 - INFO - run_glue_f1_macro -   Sample 35652 of the training set: {'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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'input_ids': [101, 1109, 13094, 1916, 1965, 1579, 3471, 1120, 1103, 1334, 1104, 1103, 23458, 113, 1170, 1103, 14516, 25043, 7562, 1138, 1151, 6561, 114, 1118, 16366, 170, 13094, 1113, 1296, 3111, 1104, 1594, 1643, 15060, 1107, 170, 10012, 2447, 1506, 1103, 9346, 1104, 1103, 23458, 119, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'label': 0, 'sentence_0': 'The knotting process always begins at the side of the rug ( after the selvedges have been secured ) by tying a knot on each pair of warp strands in a horizontal direction across the width of the rug .', '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]}.
07/18/2022 19:19:21 - INFO - run_glue_f1_macro -   Sample 3719 of the training set: {'attention_mask': [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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'input_ids': [101, 2545, 1144, 1688, 170, 7053, 1526, 1105, 1117, 6600, 3001, 1138, 10558, 5409, 119, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'label': 1, 'sentence_0': 'Tom has joined a laughter club and his stress levels have decreased significantly .', '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]}.
07/18/2022 19:19:21 - INFO - run_glue_f1_macro -   Sample 8380 of the training set: {'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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'input_ids': [101, 138, 1248, 1137, 1177, 1196, 1544, 1159, 9784, 1431, 1138, 1151, 2044, 1105, 3126, 1112, 6242, 2690, 1855, 1103, 2112, 119, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'label': 0, 'sentence_0': 'A second or so before half time Bradford should have been dead and buried as Tommy Jones hit the post .', '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]}.

[INFO|trainer.py:514] 2022-07-18 19:19:29,058 >> The following columns in the training set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: sentence_0.
[INFO|trainer.py:1147] 2022-07-18 19:19:29,102 >> ***** Running training *****
[INFO|trainer.py:1148] 2022-07-18 19:19:29,102 >>   Num examples = 38715
[INFO|trainer.py:1149] 2022-07-18 19:19:29,102 >>   Num Epochs = 9
[INFO|trainer.py:1150] 2022-07-18 19:19:29,102 >>   Instantaneous batch size per device = 32
[INFO|trainer.py:1151] 2022-07-18 19:19:29,102 >>   Total train batch size (w. parallel, distributed & accumulation) = 96
[INFO|trainer.py:1152] 2022-07-18 19:19:29,102 >>   Gradient Accumulation steps = 1
[INFO|trainer.py:1153] 2022-07-18 19:19:29,102 >>   Total optimization steps = 3636

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[INFO|trainer.py:2140] 2022-07-18 19:28:52,629 >> ***** Running Evaluation *****
[INFO|trainer.py:2142] 2022-07-18 19:28:52,629 >>   Num examples = 4840
[INFO|trainer.py:2145] 2022-07-18 19:28:52,629 >>   Batch size = 24


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                                                 [INFO|trainer.py:1894] 2022-07-18 19:29:24,957 >> Saving model checkpoint to ./checkpoints/checkpoint-404
[INFO|configuration_utils.py:351] 2022-07-18 19:29:24,962 >> Configuration saved in ./checkpoints/checkpoint-404/config.json
[INFO|modeling_utils.py:886] 2022-07-18 19:29:29,168 >> Model weights saved in ./checkpoints/checkpoint-404/pytorch_model.bin
[INFO|tokenization_utils_base.py:1925] 2022-07-18 19:29:29,171 >> tokenizer config file saved in ./checkpoints/checkpoint-404/tokenizer_config.json
[INFO|tokenization_utils_base.py:1931] 2022-07-18 19:29:29,172 >> Special tokens file saved in ./checkpoints/checkpoint-404/special_tokens_map.json
/home/acp20dah/.conda/envs/dis_venv_1/lib/python3.8/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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[INFO|trainer.py:2140] 2022-07-18 19:38:54,194 >> ***** Running Evaluation *****
[INFO|trainer.py:2142] 2022-07-18 19:38:54,194 >>   Num examples = 4840
[INFO|trainer.py:2145] 2022-07-18 19:38:54,194 >>   Batch size = 24


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                                                 [INFO|trainer.py:1894] 2022-07-18 19:39:26,560 >> Saving model checkpoint to ./checkpoints/checkpoint-808
[INFO|configuration_utils.py:351] 2022-07-18 19:39:26,564 >> Configuration saved in ./checkpoints/checkpoint-808/config.json
[INFO|modeling_utils.py:886] 2022-07-18 19:39:30,762 >> Model weights saved in ./checkpoints/checkpoint-808/pytorch_model.bin
[INFO|tokenization_utils_base.py:1925] 2022-07-18 19:39:30,764 >> tokenizer config file saved in ./checkpoints/checkpoint-808/tokenizer_config.json
[INFO|tokenization_utils_base.py:1931] 2022-07-18 19:39:30,766 >> Special tokens file saved in ./checkpoints/checkpoint-808/special_tokens_map.json
/home/acp20dah/.conda/envs/dis_venv_1/lib/python3.8/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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[INFO|trainer.py:2140] 2022-07-18 19:48:55,632 >> ***** Running Evaluation *****
[INFO|trainer.py:2142] 2022-07-18 19:48:55,633 >>   Num examples = 4840
[INFO|trainer.py:2145] 2022-07-18 19:48:55,633 >>   Batch size = 24


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                                                 [INFO|trainer.py:1894] 2022-07-18 19:49:27,981 >> Saving model checkpoint to ./checkpoints/checkpoint-1212
[INFO|configuration_utils.py:351] 2022-07-18 19:49:27,985 >> Configuration saved in ./checkpoints/checkpoint-1212/config.json
[INFO|modeling_utils.py:886] 2022-07-18 19:49:32,170 >> Model weights saved in ./checkpoints/checkpoint-1212/pytorch_model.bin
[INFO|tokenization_utils_base.py:1925] 2022-07-18 19:49:32,173 >> tokenizer config file saved in ./checkpoints/checkpoint-1212/tokenizer_config.json
[INFO|tokenization_utils_base.py:1931] 2022-07-18 19:49:32,174 >> Special tokens file saved in ./checkpoints/checkpoint-1212/special_tokens_map.json
/home/acp20dah/.conda/envs/dis_venv_1/lib/python3.8/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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[INFO|trainer.py:2140] 2022-07-18 19:58:57,752 >> ***** Running Evaluation *****
[INFO|trainer.py:2142] 2022-07-18 19:58:57,753 >>   Num examples = 4840
[INFO|trainer.py:2145] 2022-07-18 19:58:57,753 >>   Batch size = 24


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                                                 [INFO|trainer.py:1894] 2022-07-18 19:59:30,020 >> Saving model checkpoint to ./checkpoints/checkpoint-1616
[INFO|configuration_utils.py:351] 2022-07-18 19:59:30,023 >> Configuration saved in ./checkpoints/checkpoint-1616/config.json
[INFO|modeling_utils.py:886] 2022-07-18 19:59:34,249 >> Model weights saved in ./checkpoints/checkpoint-1616/pytorch_model.bin
[INFO|tokenization_utils_base.py:1925] 2022-07-18 19:59:34,252 >> tokenizer config file saved in ./checkpoints/checkpoint-1616/tokenizer_config.json
[INFO|tokenization_utils_base.py:1931] 2022-07-18 19:59:34,253 >> Special tokens file saved in ./checkpoints/checkpoint-1616/special_tokens_map.json
[INFO|trainer.py:1970] 2022-07-18 19:59:42,721 >> Deleting older checkpoint [checkpoints/checkpoint-808] due to args.save_total_limit
/home/acp20dah/.conda/envs/dis_venv_1/lib/python3.8/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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[INFO|trainer.py:2140] 2022-07-18 20:08:59,105 >> ***** Running Evaluation *****
[INFO|trainer.py:2142] 2022-07-18 20:08:59,105 >>   Num examples = 4840
[INFO|trainer.py:2145] 2022-07-18 20:08:59,105 >>   Batch size = 24


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                                                 [INFO|trainer.py:1894] 2022-07-18 20:09:31,401 >> Saving model checkpoint to ./checkpoints/checkpoint-2020
[INFO|configuration_utils.py:351] 2022-07-18 20:09:31,403 >> Configuration saved in ./checkpoints/checkpoint-2020/config.json
[INFO|modeling_utils.py:886] 2022-07-18 20:09:35,590 >> Model weights saved in ./checkpoints/checkpoint-2020/pytorch_model.bin
[INFO|tokenization_utils_base.py:1925] 2022-07-18 20:09:35,595 >> tokenizer config file saved in ./checkpoints/checkpoint-2020/tokenizer_config.json
[INFO|tokenization_utils_base.py:1931] 2022-07-18 20:09:35,598 >> Special tokens file saved in ./checkpoints/checkpoint-2020/special_tokens_map.json
[INFO|trainer.py:1970] 2022-07-18 20:09:44,030 >> Deleting older checkpoint [checkpoints/checkpoint-1212] due to args.save_total_limit
/home/acp20dah/.conda/envs/dis_venv_1/lib/python3.8/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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[INFO|trainer.py:2140] 2022-07-18 20:18:59,921 >> ***** Running Evaluation *****
[INFO|trainer.py:2142] 2022-07-18 20:18:59,921 >>   Num examples = 4840
[INFO|trainer.py:2145] 2022-07-18 20:18:59,921 >>   Batch size = 24


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                                                 [INFO|trainer.py:1894] 2022-07-18 20:19:32,171 >> Saving model checkpoint to ./checkpoints/checkpoint-2424
[INFO|configuration_utils.py:351] 2022-07-18 20:19:32,174 >> Configuration saved in ./checkpoints/checkpoint-2424/config.json
[INFO|modeling_utils.py:886] 2022-07-18 20:19:36,367 >> Model weights saved in ./checkpoints/checkpoint-2424/pytorch_model.bin
[INFO|tokenization_utils_base.py:1925] 2022-07-18 20:19:36,370 >> tokenizer config file saved in ./checkpoints/checkpoint-2424/tokenizer_config.json
[INFO|tokenization_utils_base.py:1931] 2022-07-18 20:19:36,371 >> Special tokens file saved in ./checkpoints/checkpoint-2424/special_tokens_map.json
[INFO|trainer.py:1970] 2022-07-18 20:19:44,862 >> Deleting older checkpoint [checkpoints/checkpoint-1616] due to args.save_total_limit
/home/acp20dah/.conda/envs/dis_venv_1/lib/python3.8/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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[INFO|trainer.py:2140] 2022-07-18 20:29:01,502 >> ***** Running Evaluation *****
[INFO|trainer.py:2142] 2022-07-18 20:29:01,502 >>   Num examples = 4840
[INFO|trainer.py:2145] 2022-07-18 20:29:01,502 >>   Batch size = 24


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                                                 [INFO|trainer.py:1894] 2022-07-18 20:29:33,749 >> Saving model checkpoint to ./checkpoints/checkpoint-2828
[INFO|configuration_utils.py:351] 2022-07-18 20:29:33,754 >> Configuration saved in ./checkpoints/checkpoint-2828/config.json
[INFO|modeling_utils.py:886] 2022-07-18 20:29:37,945 >> Model weights saved in ./checkpoints/checkpoint-2828/pytorch_model.bin
[INFO|tokenization_utils_base.py:1925] 2022-07-18 20:29:37,947 >> tokenizer config file saved in ./checkpoints/checkpoint-2828/tokenizer_config.json
[INFO|tokenization_utils_base.py:1931] 2022-07-18 20:29:37,949 >> Special tokens file saved in ./checkpoints/checkpoint-2828/special_tokens_map.json
[INFO|trainer.py:1970] 2022-07-18 20:29:46,437 >> Deleting older checkpoint [checkpoints/checkpoint-2020] due to args.save_total_limit
/home/acp20dah/.conda/envs/dis_venv_1/lib/python3.8/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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[INFO|trainer.py:2140] 2022-07-18 20:39:02,295 >> ***** Running Evaluation *****
[INFO|trainer.py:2142] 2022-07-18 20:39:02,295 >>   Num examples = 4840
[INFO|trainer.py:2145] 2022-07-18 20:39:02,295 >>   Batch size = 24


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                                                 [INFO|trainer.py:1894] 2022-07-18 20:39:34,575 >> Saving model checkpoint to ./checkpoints/checkpoint-3232
[INFO|configuration_utils.py:351] 2022-07-18 20:39:34,577 >> Configuration saved in ./checkpoints/checkpoint-3232/config.json
[INFO|modeling_utils.py:886] 2022-07-18 20:39:38,766 >> Model weights saved in ./checkpoints/checkpoint-3232/pytorch_model.bin
[INFO|tokenization_utils_base.py:1925] 2022-07-18 20:39:38,771 >> tokenizer config file saved in ./checkpoints/checkpoint-3232/tokenizer_config.json
[INFO|tokenization_utils_base.py:1931] 2022-07-18 20:39:38,774 >> Special tokens file saved in ./checkpoints/checkpoint-3232/special_tokens_map.json
[INFO|trainer.py:1970] 2022-07-18 20:39:47,237 >> Deleting older checkpoint [checkpoints/checkpoint-2424] due to args.save_total_limit
/home/acp20dah/.conda/envs/dis_venv_1/lib/python3.8/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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100%|██████████| 3636/3636 [1:29:34<00:00,  1.12s/it][INFO|trainer.py:514] 2022-07-18 20:49:03,572 >> The following columns in the evaluation set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: sentence_0.
[INFO|trainer.py:2140] 2022-07-18 20:49:03,577 >> ***** Running Evaluation *****
[INFO|trainer.py:2142] 2022-07-18 20:49:03,577 >>   Num examples = 4840
[INFO|trainer.py:2145] 2022-07-18 20:49:03,577 >>   Batch size = 24


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                                                 [INFO|trainer.py:1894] 2022-07-18 20:49:35,746 >> Saving model checkpoint to ./checkpoints/checkpoint-3636
[INFO|configuration_utils.py:351] 2022-07-18 20:49:35,749 >> Configuration saved in ./checkpoints/checkpoint-3636/config.json
[INFO|modeling_utils.py:886] 2022-07-18 20:49:39,937 >> Model weights saved in ./checkpoints/checkpoint-3636/pytorch_model.bin
[INFO|tokenization_utils_base.py:1925] 2022-07-18 20:49:39,939 >> tokenizer config file saved in ./checkpoints/checkpoint-3636/tokenizer_config.json
[INFO|tokenization_utils_base.py:1931] 2022-07-18 20:49:39,941 >> Special tokens file saved in ./checkpoints/checkpoint-3636/special_tokens_map.json
[INFO|trainer.py:1970] 2022-07-18 20:49:48,392 >> Deleting older checkpoint [checkpoints/checkpoint-2828] due to args.save_total_limit
[INFO|trainer.py:1343] 2022-07-18 20:49:48,491 >> 

Training completed. Do not forget to share your model on huggingface.co/models =)


[INFO|trainer.py:1351] 2022-07-18 20:49:48,491 >> Loading best model from ./checkpoints/checkpoint-404 (score: 0.8300959291705878).

                                                     

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[INFO|trainer.py:1894] 2022-07-18 20:49:52,813 >> Saving model checkpoint to ./checkpoints
[INFO|configuration_utils.py:351] 2022-07-18 20:49:52,816 >> Configuration saved in ./checkpoints/config.json
[INFO|modeling_utils.py:886] 2022-07-18 20:49:57,038 >> Model weights saved in ./checkpoints/pytorch_model.bin
[INFO|tokenization_utils_base.py:1925] 2022-07-18 20:49:57,041 >> tokenizer config file saved in ./checkpoints/tokenizer_config.json
[INFO|tokenization_utils_base.py:1931] 2022-07-18 20:49:57,043 >> Special tokens file saved in ./checkpoints/special_tokens_map.json
[INFO|trainer_pt_utils.py:908] 2022-07-18 20:49:57,114 >> ***** train metrics *****
[INFO|trainer_pt_utils.py:913] 2022-07-18 20:49:57,114 >>   epoch                    =        9.0
[INFO|trainer_pt_utils.py:913] 2022-07-18 20:49:57,114 >>   train_loss               =     0.0782
[INFO|trainer_pt_utils.py:913] 2022-07-18 20:49:57,114 >>   train_runtime            = 1:30:23.70
[INFO|trainer_pt_utils.py:913] 2022-07-18 20:49:57,114 >>   train_samples            =      38715
[INFO|trainer_pt_utils.py:913] 2022-07-18 20:49:57,114 >>   train_samples_per_second =     64.243
[INFO|trainer_pt_utils.py:913] 2022-07-18 20:49:57,114 >>   train_steps_per_second   =       0.67
{'eval_loss': 0.340609610080719, 'eval_accuracy': 0.8516529202461243, 'eval_f1': 0.8300959291705878, 'eval_runtime': 32.3262, 'eval_samples_per_second': 149.724, 'eval_steps_per_second': 6.249, 'epoch': 1.0}
{'loss': 0.254, 'learning_rate': 1.724972497249725e-05, 'epoch': 1.24}
{'eval_loss': 0.416839599609375, 'eval_accuracy': 0.8500000238418579, 'eval_f1': 0.8238115361465539, 'eval_runtime': 32.3319, 'eval_samples_per_second': 149.697, 'eval_steps_per_second': 6.248, 'epoch': 2.0}
{'loss': 0.118, 'learning_rate': 1.4499449944994501e-05, 'epoch': 2.48}
{'eval_loss': 0.5434129238128662, 'eval_accuracy': 0.8229339122772217, 'eval_f1': 0.8022003793875959, 'eval_runtime': 32.3466, 'eval_samples_per_second': 149.629, 'eval_steps_per_second': 6.245, 'epoch': 3.0}
{'loss': 0.0673, 'learning_rate': 1.174917491749175e-05, 'epoch': 3.71}
{'eval_loss': 0.6625058054924011, 'eval_accuracy': 0.8221074342727661, 'eval_f1': 0.79855340963886, 'eval_runtime': 32.266, 'eval_samples_per_second': 150.003, 'eval_steps_per_second': 6.26, 'epoch': 4.0}
{'loss': 0.0457, 'learning_rate': 8.998899889989e-06, 'epoch': 4.95}
{'eval_loss': 0.673636257648468, 'eval_accuracy': 0.8266528844833374, 'eval_f1': 0.8022902674078713, 'eval_runtime': 32.2935, 'eval_samples_per_second': 149.875, 'eval_steps_per_second': 6.255, 'epoch': 5.0}
{'eval_loss': 0.6744675040245056, 'eval_accuracy': 0.8326446413993835, 'eval_f1': 0.8079356046354128, 'eval_runtime': 32.2483, 'eval_samples_per_second': 150.085, 'eval_steps_per_second': 6.264, 'epoch': 6.0}
{'loss': 0.0338, 'learning_rate': 6.248624862486249e-06, 'epoch': 6.19}
{'eval_loss': 0.750978410243988, 'eval_accuracy': 0.8311983346939087, 'eval_f1': 0.8051717857677967, 'eval_runtime': 32.238, 'eval_samples_per_second': 150.133, 'eval_steps_per_second': 6.266, 'epoch': 7.0}
{'loss': 0.0252, 'learning_rate': 3.4983498349834986e-06, 'epoch': 7.43}
{'eval_loss': 0.8610924482345581, 'eval_accuracy': 0.8212810158729553, 'eval_f1': 0.7964990704509565, 'eval_runtime': 32.2776, 'eval_samples_per_second': 149.949, 'eval_steps_per_second': 6.258, 'epoch': 8.0}
{'loss': 0.0196, 'learning_rate': 7.480748074807481e-07, 'epoch': 8.66}
{'eval_loss': 0.8909189701080322, 'eval_accuracy': 0.8188016414642334, 'eval_f1': 0.7950063649406158, 'eval_runtime': 32.1683, 'eval_samples_per_second': 150.459, 'eval_steps_per_second': 6.279, 'epoch': 9.0}
{'train_runtime': 5423.7015, 'train_samples_per_second': 64.243, 'train_steps_per_second': 0.67, 'train_loss': 0.07822002554097192, 'epoch': 9.0}
07/18/2022 20:49:57 - INFO - run_glue_f1_macro -   *** Evaluate ***
[INFO|trainer.py:514] 2022-07-18 20:49:57,121 >> The following columns in the evaluation set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: sentence_0.
[INFO|trainer.py:2140] 2022-07-18 20:49:57,127 >> ***** Running Evaluation *****
[INFO|trainer.py:2142] 2022-07-18 20:49:57,127 >>   Num examples = 4840
[INFO|trainer.py:2145] 2022-07-18 20:49:57,127 >>   Batch size = 24
/home/acp20dah/.conda/envs/dis_venv_1/lib/python3.8/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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[INFO|trainer_pt_utils.py:908] 2022-07-18 20:50:29,232 >> ***** eval metrics *****
[INFO|trainer_pt_utils.py:913] 2022-07-18 20:50:29,233 >>   epoch                   =        9.0
[INFO|trainer_pt_utils.py:913] 2022-07-18 20:50:29,233 >>   eval_accuracy           =     0.8517
[INFO|trainer_pt_utils.py:913] 2022-07-18 20:50:29,233 >>   eval_f1                 =     0.8301
[INFO|trainer_pt_utils.py:913] 2022-07-18 20:50:29,233 >>   eval_loss               =     0.3406
[INFO|trainer_pt_utils.py:913] 2022-07-18 20:50:29,233 >>   eval_runtime            = 0:00:32.10
[INFO|trainer_pt_utils.py:913] 2022-07-18 20:50:29,233 >>   eval_samples            =       4840
[INFO|trainer_pt_utils.py:913] 2022-07-18 20:50:29,233 >>   eval_samples_per_second =    150.754
[INFO|trainer_pt_utils.py:913] 2022-07-18 20:50:29,233 >>   eval_steps_per_second   =      6.292
07/18/2022 20:50:29 - INFO - run_glue_f1_macro -   *** Test ***
/data/acp20dah/dissertation/idiom-principle-on-magpie-corpus/experiments/exp0/../../exp_helpers/run_glue_f1_macro.py:518: FutureWarning: remove_columns_ is deprecated and will be removed in the next major version of datasets. Use Dataset.remove_columns instead.
  test_dataset.remove_columns_("label")
[INFO|trainer.py:514] 2022-07-18 20:50:29,239 >> The following columns in the test set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: sentence_0.
[INFO|trainer.py:2140] 2022-07-18 20:50:29,245 >> ***** Running Prediction *****
[INFO|trainer.py:2142] 2022-07-18 20:50:29,245 >>   Num examples = 4840
[INFO|trainer.py:2145] 2022-07-18 20:50:29,245 >>   Batch size = 24

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[INFO|trainer.py:514] 2022-07-18 20:51:01,296 >> The following columns in the evaluation set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: sentence_0.
[INFO|trainer.py:2140] 2022-07-18 20:51:01,302 >> ***** Running Evaluation *****
[INFO|trainer.py:2142] 2022-07-18 20:51:01,302 >>   Num examples = 4840
[INFO|trainer.py:2145] 2022-07-18 20:51:01,302 >>   Batch size = 24

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[INFO|trainer_pt_utils.py:908] 2022-07-18 20:51:33,391 >> ***** test metrics *****
[INFO|trainer_pt_utils.py:913] 2022-07-18 20:51:33,391 >>   epoch                   =        9.0
[INFO|trainer_pt_utils.py:913] 2022-07-18 20:51:33,392 >>   eval_runtime            = 0:00:32.08
[INFO|trainer_pt_utils.py:913] 2022-07-18 20:51:33,392 >>   eval_samples_per_second =    150.834
[INFO|trainer_pt_utils.py:913] 2022-07-18 20:51:33,392 >>   eval_steps_per_second   =      6.295
[INFO|trainer_pt_utils.py:913] 2022-07-18 20:51:33,392 >>   test_samples            =       4840