WARNING:__main__:Process rank: 2, device: cuda:2, n_gpu: 1 distributed training: True, 16-bits training: True
WARNING:__main__:Process rank: 3, device: cuda:3, n_gpu: 1 distributed training: True, 16-bits training: True
WARNING:__main__:Process rank: 1, device: cuda:1, n_gpu: 1 distributed training: True, 16-bits training: True
WARNING:__main__:Process rank: 0, device: cuda:0, n_gpu: 1 distributed training: True, 16-bits training: True
WARNING:datasets.builder:Using custom data configuration default-2f6794b69ce47e79
WARNING:datasets.builder:Reusing dataset csv (/project/jonmay_231/jacqueline/.cache/csv/default-2f6794b69ce47e79/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e)

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WARNING:datasets.builder:Reusing dataset csv (/project/jonmay_231/jacqueline/.cache/csv/default-2f6794b69ce47e79/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e)

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[INFO|configuration_utils.py:445] 2023-01-24 05:28:26,849 >> loading configuration file https://huggingface.co/roberta-large/resolve/main/config.json from cache at /project/jonmay_231/jacqueline/.cache/dea67b44b38d504f2523f3ddb6acb601b23d67bee52c942da336fa1283100990.94cae8b3a8dbab1d59b9d4827f7ce79e73124efa6bb970412cd503383a95f373
[INFO|configuration_utils.py:481] 2023-01-24 05:28:26,850 >> Model config RobertaConfig {
  "architectures": [
    "RobertaForMaskedLM"
  ],
  "attention_probs_dropout_prob": 0.1,
  "bos_token_id": 0,
  "eos_token_id": 2,
  "gradient_checkpointing": false,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 1024,
  "initializer_range": 0.02,
  "intermediate_size": 4096,
  "layer_norm_eps": 1e-05,
  "max_position_embeddings": 514,
  "model_type": "roberta",
  "num_attention_heads": 16,
  "num_hidden_layers": 24,
  "pad_token_id": 1,
  "position_embedding_type": "absolute",
  "transformers_version": "4.2.1",
  "type_vocab_size": 1,
  "use_cache": true,
  "vocab_size": 50265
}

[INFO|configuration_utils.py:445] 2023-01-24 05:28:27,146 >> loading configuration file https://huggingface.co/roberta-large/resolve/main/config.json from cache at /project/jonmay_231/jacqueline/.cache/dea67b44b38d504f2523f3ddb6acb601b23d67bee52c942da336fa1283100990.94cae8b3a8dbab1d59b9d4827f7ce79e73124efa6bb970412cd503383a95f373
[INFO|configuration_utils.py:481] 2023-01-24 05:28:27,146 >> Model config RobertaConfig {
  "architectures": [
    "RobertaForMaskedLM"
  ],
  "attention_probs_dropout_prob": 0.1,
  "bos_token_id": 0,
  "eos_token_id": 2,
  "gradient_checkpointing": false,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 1024,
  "initializer_range": 0.02,
  "intermediate_size": 4096,
  "layer_norm_eps": 1e-05,
  "max_position_embeddings": 514,
  "model_type": "roberta",
  "num_attention_heads": 16,
  "num_hidden_layers": 24,
  "pad_token_id": 1,
  "position_embedding_type": "absolute",
  "transformers_version": "4.2.1",
  "type_vocab_size": 1,
  "use_cache": true,
  "vocab_size": 50265
}

[INFO|tokenization_utils_base.py:1766] 2023-01-24 05:28:27,978 >> loading file https://huggingface.co/roberta-large/resolve/main/vocab.json from cache at /project/jonmay_231/jacqueline/.cache/7c1ba2435b05451bc3b4da073c8dec9630b22024a65f6c41053caccf2880eb8f.d67d6b367eb24ab43b08ad55e014cf254076934f71d832bbab9ad35644a375ab
[INFO|tokenization_utils_base.py:1766] 2023-01-24 05:28:27,978 >> loading file https://huggingface.co/roberta-large/resolve/main/merges.txt from cache at /project/jonmay_231/jacqueline/.cache/20b5a00a80e27ae9accbe25672aba42ad2d4d4cb2c4b9359b50ca8e34e107d6d.5d12962c5ee615a4c803841266e9c3be9a691a924f72d395d3a6c6c81157788b
[INFO|tokenization_utils_base.py:1766] 2023-01-24 05:28:27,979 >> loading file https://huggingface.co/roberta-large/resolve/main/tokenizer.json from cache at /project/jonmay_231/jacqueline/.cache/e16a2590deb9e6d73711d6e05bf27d832fa8c1162d807222e043ca650a556964.fc9576039592f026ad76a1c231b89aee8668488c671dfbe6616bab2ed298d730
[INFO|modeling_utils.py:1027] 2023-01-24 05:28:28,614 >> loading weights file https://huggingface.co/roberta-large/resolve/main/pytorch_model.bin from cache at /project/jonmay_231/jacqueline/.cache/8e36ec2f5052bec1e79e139b84c2c3089cb647694ba0f4f634fec7b8258f7c89.c43841d8c5cd23c435408295164cda9525270aa42cd0cc9200911570c0342352
Some weights of the model checkpoint at roberta-large were not used when initializing RobertaForMabel: ['roberta.pooler.dense.weight', 'roberta.pooler.dense.bias']
- This IS expected if you are initializing RobertaForMabel 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 RobertaForMabel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of RobertaForMabel were not initialized from the model checkpoint at roberta-large and are newly initialized: ['lm_head.decoder.bias', 'mlp.dense1.weight', 'mlp.dense1.bias', 'mlp.dense2.weight', 'mlp.dense2.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Some weights of the model checkpoint at roberta-large were not used when initializing RobertaForMabel: ['roberta.pooler.dense.weight', 'roberta.pooler.dense.bias']
- This IS expected if you are initializing RobertaForMabel 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 RobertaForMabel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of the model checkpoint at roberta-large were not used when initializing RobertaForMabel: ['roberta.pooler.dense.weight', 'roberta.pooler.dense.bias']
- This IS expected if you are initializing RobertaForMabel 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 RobertaForMabel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of RobertaForMabel were not initialized from the model checkpoint at roberta-large and are newly initialized: ['lm_head.decoder.bias', 'mlp.dense1.weight', 'mlp.dense1.bias', 'mlp.dense2.weight', 'mlp.dense2.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
[WARNING|modeling_utils.py:1135] 2023-01-24 05:29:01,118 >> Some weights of the model checkpoint at roberta-large were not used when initializing RobertaForMabel: ['roberta.pooler.dense.weight', 'roberta.pooler.dense.bias']
- This IS expected if you are initializing RobertaForMabel 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 RobertaForMabel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of RobertaForMabel were not initialized from the model checkpoint at roberta-large and are newly initialized: ['lm_head.decoder.bias', 'mlp.dense1.weight', 'mlp.dense1.bias', 'mlp.dense2.weight', 'mlp.dense2.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
[WARNING|modeling_utils.py:1146] 2023-01-24 05:29:01,123 >> Some weights of RobertaForMabel were not initialized from the model checkpoint at roberta-large and are newly initialized: ['lm_head.decoder.bias', 'mlp.dense1.weight', 'mlp.dense1.bias', 'mlp.dense2.weight', 'mlp.dense2.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
[INFO|configuration_utils.py:445] 2023-01-24 05:29:01,412 >> loading configuration file https://huggingface.co/roberta-large/resolve/main/config.json from cache at /project/jonmay_231/jacqueline/.cache/dea67b44b38d504f2523f3ddb6acb601b23d67bee52c942da336fa1283100990.94cae8b3a8dbab1d59b9d4827f7ce79e73124efa6bb970412cd503383a95f373
[INFO|configuration_utils.py:481] 2023-01-24 05:29:01,413 >> Model config RobertaConfig {
  "architectures": [
    "RobertaForMaskedLM"
  ],
  "attention_probs_dropout_prob": 0.1,
  "bos_token_id": 0,
  "eos_token_id": 2,
  "gradient_checkpointing": false,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 1024,
  "initializer_range": 0.02,
  "intermediate_size": 4096,
  "layer_norm_eps": 1e-05,
  "max_position_embeddings": 514,
  "model_type": "roberta",
  "num_attention_heads": 16,
  "num_hidden_layers": 24,
  "pad_token_id": 1,
  "position_embedding_type": "absolute",
  "transformers_version": "4.2.1",
  "type_vocab_size": 1,
  "use_cache": true,
  "vocab_size": 50265
}

[INFO|modeling_utils.py:1027] 2023-01-24 05:29:01,693 >> loading weights file https://huggingface.co/roberta-large/resolve/main/pytorch_model.bin from cache at /project/jonmay_231/jacqueline/.cache/8e36ec2f5052bec1e79e139b84c2c3089cb647694ba0f4f634fec7b8258f7c89.c43841d8c5cd23c435408295164cda9525270aa42cd0cc9200911570c0342352
Some weights of RobertaForMaskedLM were not initialized from the model checkpoint at roberta-large and are newly initialized: ['lm_head.decoder.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
WARNING:datasets.fingerprint:Parameter 'function'=<function main.<locals>.prepare_features at 0x7f6c09a9a830> 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.
WARNING:datasets.arrow_dataset:Loading cached processed dataset at /project/jonmay_231/jacqueline/.cache/csv/default-2f6794b69ce47e79/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e/cache-6b01a1c12a3a2107.arrow
Some weights of RobertaForMaskedLM were not initialized from the model checkpoint at roberta-large and are newly initialized: ['lm_head.decoder.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
[INFO|modeling_utils.py:1143] 2023-01-24 05:29:33,813 >> All model checkpoint weights were used when initializing RobertaForMaskedLM.

Some weights of RobertaForMaskedLM were not initialized from the model checkpoint at roberta-large and are newly initialized: ['lm_head.decoder.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
[WARNING|modeling_utils.py:1146] 2023-01-24 05:29:33,833 >> Some weights of RobertaForMaskedLM were not initialized from the model checkpoint at roberta-large and are newly initialized: ['lm_head.decoder.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
WARNING:datasets.fingerprint:Parameter 'function'=<function main.<locals>.prepare_features at 0x7fe9c9381830> 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.
WARNING:datasets.fingerprint:Parameter 'function'=<function main.<locals>.prepare_features at 0x7f3a1c382830> 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.
WARNING:datasets.fingerprint:Parameter 'function'=<function main.<locals>.prepare_features at 0x7f90f406b830> 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.
WARNING:datasets.arrow_dataset:Loading cached processed dataset at /project/jonmay_231/jacqueline/.cache/csv/default-2f6794b69ce47e79/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e/cache-6b01a1c12a3a2107.arrow
WARNING:datasets.arrow_dataset:Loading cached processed dataset at /project/jonmay_231/jacqueline/.cache/csv/default-2f6794b69ce47e79/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e/cache-6b01a1c12a3a2107.arrow
WARNING:datasets.arrow_dataset:Loading cached processed dataset at /project/jonmay_231/jacqueline/.cache/csv/default-2f6794b69ce47e79/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e/cache-6b01a1c12a3a2107.arrow
Dataset({
    features: ['bin_mask', 'input_ids', 'attention_mask'],
    num_rows: 142158
})Dataset({
    features: ['bin_mask', 'input_ids', 'attention_mask'],
    num_rows: 142158
})Dataset({
    features: ['bin_mask', 'input_ids', 'attention_mask'],
    num_rows: 142158
})Dataset({
    features: ['bin_mask', 'input_ids', 'attention_mask'],
    num_rows: 142158
})



[INFO|trainer.py:442] 2023-01-24 05:29:44,654 >> The following columns in the training set don't have a corresponding argument in `RobertaForMabel.forward` and have been ignored: .
[INFO|trainer.py:358] 2023-01-24 05:29:44,654 >> Using amp fp16 backend

  0%|          | 0/2222 [00:00<?, ?it/s][W reducer.cpp:1050] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters. This flag results in an extra traversal of the autograd graph every iteration, which can adversely affect performance. If your model indeed never has any unused parameters, consider turning this flag off. Note that this warning may be a false positive your model has flow control causing later iterations to have unused parameters. (function operator())
[W reducer.cpp:1050] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters. This flag results in an extra traversal of the autograd graph every iteration, which can adversely affect performance. If your model indeed never has any unused parameters, consider turning this flag off. Note that this warning may be a false positive your model has flow control causing later iterations to have unused parameters. (function operator())
[W reducer.cpp:1050] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters. This flag results in an extra traversal of the autograd graph every iteration, which can adversely affect performance. If your model indeed never has any unused parameters, consider turning this flag off. Note that this warning may be a false positive your model has flow control causing later iterations to have unused parameters. (function operator())
[W reducer.cpp:1050] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters. This flag results in an extra traversal of the autograd graph every iteration, which can adversely affect performance. If your model indeed never has any unused parameters, consider turning this flag off. Note that this warning may be a false positive your model has flow control causing later iterations to have unused parameters. (function operator())
/home1/jh_445/.conda/envs/mabel/lib/python3.7/site-packages/torch/optim/lr_scheduler.py:134: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
  "https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning)
/home1/jh_445/.conda/envs/mabel/lib/python3.7/site-packages/torch/optim/lr_scheduler.py:134: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
  "https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning)
/home1/jh_445/.conda/envs/mabel/lib/python3.7/site-packages/torch/optim/lr_scheduler.py:134: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
  "https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning)
/home1/jh_445/.conda/envs/mabel/lib/python3.7/site-packages/torch/optim/lr_scheduler.py:134: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
  "https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning)

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{'loss': 4.8347, 'learning_rate': 0.000154995499549955, 'epoch': 0.45}

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{'loss': 2.2463, 'learning_rate': 0.00010999099909990999, 'epoch': 0.9}

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{'loss': 1.6708, 'learning_rate': 6.498649864986499e-05, 'epoch': 1.35}

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{'loss': 1.3752, 'learning_rate': 1.9981998199819982e-05, 'epoch': 1.8}

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{'train_runtime': 3913.5949, 'train_samples_per_second': 0.568, 'epoch': 2.0}

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[INFO|trainer.py:1344] 2023-01-24 06:34:58,894 >> Saving model checkpoint to /project/jonmay_231/jacqueline/mabel/training/rob-large-out/mabel-joint-cl-al1-mlm-bs-32-lr-2e-4-msl-128-ep-2-atemp-0.025
[INFO|configuration_utils.py:300] 2023-01-24 06:34:58,941 >> Configuration saved in /project/jonmay_231/jacqueline/mabel/training/rob-large-out/mabel-joint-cl-al1-mlm-bs-32-lr-2e-4-msl-128-ep-2-atemp-0.025/config.json
[INFO|modeling_utils.py:817] 2023-01-24 06:35:02,197 >> Model weights saved in /project/jonmay_231/jacqueline/mabel/training/rob-large-out/mabel-joint-cl-al1-mlm-bs-32-lr-2e-4-msl-128-ep-2-atemp-0.025/pytorch_model.bin
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Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
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