Training in progress, epoch 1

config.json

@@ -0,0 +1,37 @@
+{
+  "_name_or_path": "michiyasunaga/BioLinkBERT-base",
+  "architectures": [
+    "BertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "finetuning_task": "ner",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "O",
+    "1": "B-FARMACO",
+    "2": "I-FARMACO"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "B-FARMACO": 1,
+    "I-FARMACO": 2,
+    "O": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 28895
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c3ceb2eb18620368af7015f3678369f766b8eb56aa29d7073dc117bce300c203
+size 430601004

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tb/events.out.tfevents.1725569221.c3806e32a2f8.1237.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5dce1bb39d0512415fbdfee1e9bab2de73533964f2c80d90eacdd674f9edf972
+size 5593

tokenizer_config.json

@@ -0,0 +1,57 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

train.log

@@ -0,0 +1,316 @@
+2024-09-05 20:46:30.525749: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+2024-09-05 20:46:30.543947: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
+2024-09-05 20:46:30.565279: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
+2024-09-05 20:46:30.571709: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-09-05 20:46:30.587388: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
+To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-09-05 20:46:31.893073: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead
+  warnings.warn(
+09/05/2024 20:46:34 - WARNING - __main__ -   Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
+09/05/2024 20:46:34 - INFO - __main__ -   Training/evaluation parameters TrainingArguments(
+_n_gpu=1,
+accelerator_config={'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None, 'use_configured_state': False},
+adafactor=False,
+adam_beta1=0.9,
+adam_beta2=0.999,
+adam_epsilon=1e-08,
+auto_find_batch_size=False,
+batch_eval_metrics=False,
+bf16=False,
+bf16_full_eval=False,
+data_seed=None,
+dataloader_drop_last=False,
+dataloader_num_workers=0,
+dataloader_persistent_workers=False,
+dataloader_pin_memory=True,
+dataloader_prefetch_factor=None,
+ddp_backend=None,
+ddp_broadcast_buffers=None,
+ddp_bucket_cap_mb=None,
+ddp_find_unused_parameters=None,
+ddp_timeout=1800,
+debug=[],
+deepspeed=None,
+disable_tqdm=False,
+dispatch_batches=None,
+do_eval=True,
+do_predict=True,
+do_train=True,
+eval_accumulation_steps=None,
+eval_delay=0,
+eval_do_concat_batches=True,
+eval_on_start=False,
+eval_steps=None,
+eval_strategy=epoch,
+eval_use_gather_object=False,
+evaluation_strategy=epoch,
+fp16=False,
+fp16_backend=auto,
+fp16_full_eval=False,
+fp16_opt_level=O1,
+fsdp=[],
+fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False},
+fsdp_min_num_params=0,
+fsdp_transformer_layer_cls_to_wrap=None,
+full_determinism=False,
+gradient_accumulation_steps=2,
+gradient_checkpointing=False,
+gradient_checkpointing_kwargs=None,
+greater_is_better=True,
+group_by_length=False,
+half_precision_backend=auto,
+hub_always_push=False,
+hub_model_id=None,
+hub_private_repo=False,
+hub_strategy=every_save,
+hub_token=<HUB_TOKEN>,
+ignore_data_skip=False,
+include_inputs_for_metrics=False,
+include_num_input_tokens_seen=False,
+include_tokens_per_second=False,
+jit_mode_eval=False,
+label_names=None,
+label_smoothing_factor=0.0,
+learning_rate=5e-05,
+length_column_name=length,
+load_best_model_at_end=True,
+local_rank=0,
+log_level=passive,
+log_level_replica=warning,
+log_on_each_node=True,
+logging_dir=/content/dissertation/scripts/ner/output/tb,
+logging_first_step=False,
+logging_nan_inf_filter=True,
+logging_steps=500,
+logging_strategy=steps,
+lr_scheduler_kwargs={},
+lr_scheduler_type=linear,
+max_grad_norm=1.0,
+max_steps=-1,
+metric_for_best_model=f1,
+mp_parameters=,
+neftune_noise_alpha=None,
+no_cuda=False,
+num_train_epochs=10.0,
+optim=adamw_torch,
+optim_args=None,
+optim_target_modules=None,
+output_dir=/content/dissertation/scripts/ner/output,
+overwrite_output_dir=True,
+past_index=-1,
+per_device_eval_batch_size=8,
+per_device_train_batch_size=32,
+prediction_loss_only=False,
+push_to_hub=True,
+push_to_hub_model_id=None,
+push_to_hub_organization=None,
+push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
+ray_scope=last,
+remove_unused_columns=True,
+report_to=['tensorboard'],
+restore_callback_states_from_checkpoint=False,
+resume_from_checkpoint=None,
+run_name=/content/dissertation/scripts/ner/output,
+save_on_each_node=False,
+save_only_model=False,
+save_safetensors=True,
+save_steps=500,
+save_strategy=epoch,
+save_total_limit=None,
+seed=42,
+skip_memory_metrics=True,
+split_batches=None,
+tf32=None,
+torch_compile=False,
+torch_compile_backend=None,
+torch_compile_mode=None,
+torch_empty_cache_steps=None,
+torchdynamo=None,
+tpu_metrics_debug=False,
+tpu_num_cores=None,
+use_cpu=False,
+use_ipex=False,
+use_legacy_prediction_loop=False,
+use_mps_device=False,
+warmup_ratio=0.0,
+warmup_steps=0,
+weight_decay=0.0,
+)
+
+
+
+
+
+
+
+[INFO|configuration_utils.py:733] 2024-09-05 20:46:49,939 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--michiyasunaga--BioLinkBERT-base/snapshots/b71f5d70f063d1c8f1124070ce86f1ee463ca1fe/config.json
+[INFO|configuration_utils.py:800] 2024-09-05 20:46:49,949 >> Model config BertConfig {
+  "_name_or_path": "michiyasunaga/BioLinkBERT-base",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "finetuning_task": "ner",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "O",
+    "1": "B-FARMACO",
+    "2": "I-FARMACO"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "B-FARMACO": 1,
+    "I-FARMACO": 2,
+    "O": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.44.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 28895
+}
+
+[INFO|tokenization_utils_base.py:2269] 2024-09-05 20:46:52,215 >> loading file vocab.txt from cache at /root/.cache/huggingface/hub/models--michiyasunaga--BioLinkBERT-base/snapshots/b71f5d70f063d1c8f1124070ce86f1ee463ca1fe/vocab.txt
+[INFO|tokenization_utils_base.py:2269] 2024-09-05 20:46:52,215 >> loading file tokenizer.json from cache at /root/.cache/huggingface/hub/models--michiyasunaga--BioLinkBERT-base/snapshots/b71f5d70f063d1c8f1124070ce86f1ee463ca1fe/tokenizer.json
+[INFO|tokenization_utils_base.py:2269] 2024-09-05 20:46:52,215 >> loading file added_tokens.json from cache at None
+[INFO|tokenization_utils_base.py:2269] 2024-09-05 20:46:52,215 >> loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--michiyasunaga--BioLinkBERT-base/snapshots/b71f5d70f063d1c8f1124070ce86f1ee463ca1fe/special_tokens_map.json
+[INFO|tokenization_utils_base.py:2269] 2024-09-05 20:46:52,215 >> loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--michiyasunaga--BioLinkBERT-base/snapshots/b71f5d70f063d1c8f1124070ce86f1ee463ca1fe/tokenizer_config.json
+/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884
+  warnings.warn(
+[INFO|modeling_utils.py:3678] 2024-09-05 20:46:54,406 >> loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--michiyasunaga--BioLinkBERT-base/snapshots/b71f5d70f063d1c8f1124070ce86f1ee463ca1fe/pytorch_model.bin
+[INFO|modeling_utils.py:4497] 2024-09-05 20:46:54,546 >> Some weights of the model checkpoint at michiyasunaga/BioLinkBERT-base were not used when initializing BertForTokenClassification: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight']
+- This IS expected if you are initializing BertForTokenClassification 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 BertForTokenClassification 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:4509] 2024-09-05 20:46:54,546 >> Some weights of BertForTokenClassification were not initialized from the model checkpoint at michiyasunaga/BioLinkBERT-base and are newly initialized: ['classifier.bias', 'classifier.weight']
+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+
+
+
+/content/dissertation/scripts/ner/run_ner_train.py:397: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate
+  metric = load_metric("seqeval", trust_remote_code=True)
+
+[INFO|trainer.py:811] 2024-09-05 20:47:01,019 >> The following columns in the training set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: id, tokens, ner_tags. If id, tokens, ner_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
+[INFO|trainer.py:2134] 2024-09-05 20:47:01,709 >> ***** Running training *****
+[INFO|trainer.py:2135] 2024-09-05 20:47:01,709 >>   Num examples = 27,967
+[INFO|trainer.py:2136] 2024-09-05 20:47:01,709 >>   Num Epochs = 10
+[INFO|trainer.py:2137] 2024-09-05 20:47:01,709 >>   Instantaneous batch size per device = 32
+[INFO|trainer.py:2140] 2024-09-05 20:47:01,709 >>   Total train batch size (w. parallel, distributed & accumulation) = 64
+[INFO|trainer.py:2141] 2024-09-05 20:47:01,709 >>   Gradient Accumulation steps = 2
+[INFO|trainer.py:2142] 2024-09-05 20:47:01,709 >>   Total optimization steps = 4,370
+[INFO|trainer.py:2143] 2024-09-05 20:47:01,710 >>   Number of trainable parameters = 107,644,419
+
  0%|          | 0/4370 [00:00<?, ?it/s]
  0%|          | 1/4370 [00:01<2:06:45,  1.74s/it]
  0%|          | 2/4370 [00:01<1:01:47,  1.18it/s]
  0%|          | 3/4370 [00:02<45:00,  1.62it/s]  
  0%|          | 4/4370 [00:02<31:33,  2.31it/s]
  0%|          | 5/4370 [00:02<25:03,  2.90it/s]
  0%|          | 6/4370 [00:02<20:29,  3.55it/s]
  0%|          | 7/4370 [00:03<18:58,  3.83it/s]
  0%|          | 8/4370 [00:03<17:35,  4.13it/s]
  0%|          | 9/4370 [00:03<16:39,  4.36it/s]
  0%|          | 10/4370 [00:03<16:22,  4.44it/s]
  0%|          | 11/4370 [00:03<18:38,  3.90it/s]
  0%|          | 12/4370 [00:04<16:48,  4.32it/s]
  0%|          | 13/4370 [00:04<16:17,  4.46it/s]
  0%|          | 14/4370 [00:04<15:45,  4.61it/s]
  0%|          | 15/4370 [00:04<14:35,  4.98it/s]
  0%|          | 16/4370 [00:04<14:45,  4.92it/s]
  0%|          | 17/4370 [00:05<14:03,  5.16it/s]
  0%|          | 18/4370 [00:05<13:30,  5.37it/s]
  0%|          | 19/4370 [00:05<13:12,  5.49it/s]
  0%|          | 20/4370 [00:05<15:28,  4.69it/s]
  0%|          | 21/4370 [00:05<14:54,  4.86it/s]
  1%|          | 22/4370 [00:06<14:39,  4.94it/s]
  1%|          | 23/4370 [00:06<16:12,  4.47it/s]
  1%|          | 24/4370 [00:06<15:39,  4.63it/s]
  1%|          | 25/4370 [00:06<15:31,  4.66it/s]
  1%|          | 26/4370 [00:07<16:39,  4.35it/s]
  1%|          | 27/4370 [00:07<16:12,  4.47it/s]
  1%|          | 28/4370 [00:07<15:38,  4.63it/s]
  1%|          | 29/4370 [00:07<17:41,  4.09it/s]
  1%|          | 30/4370 [00:08<17:28,  4.14it/s]
  1%|          | 31/4370 [00:08<17:06,  4.23it/s]
  1%|          | 32/4370 [00:08<16:00,  4.52it/s]
  1%|          | 33/4370 [00:08<15:28,  4.67it/s]
  1%|          | 34/4370 [00:08<16:06,  4.49it/s]
  1%|          | 35/4370 [00:09<14:53,  4.85it/s]
  1%|          | 36/4370 [00:09<14:34,  4.96it/s]
  1%|          | 37/4370 [00:09<13:39,  5.29it/s]
  1%|          | 38/4370 [00:09<13:35,  5.31it/s]
  1%|          | 39/4370 [00:09<13:02,  5.53it/s]
  1%|          | 40/4370 [00:09<12:00,  6.01it/s]
  1%|          | 41/4370 [00:10<11:55,  6.05it/s]
  1%|          | 42/4370 [00:10<13:36,  5.30it/s]
  1%|          | 43/4370 [00:10<14:23,  5.01it/s]
  1%|          | 44/4370 [00:10<14:43,  4.90it/s]
  1%|          | 45/4370 [00:10<14:29,  4.98it/s]
  1%|          | 46/4370 [00:11<14:08,  5.09it/s]
  1%|          | 47/4370 [00:11<14:05,  5.11it/s]
  1%|          | 48/4370 [00:11<16:25,  4.39it/s]
  1%|          | 49/4370 [00:11<17:05,  4.21it/s]
  1%|          | 50/4370 [00:12<16:45,  4.30it/s]
  1%|          | 51/4370 [00:12<15:45,  4.57it/s]
  1%|          | 52/4370 [00:12<14:55,  4.82it/s]
  1%|          | 53/4370 [00:12<13:41,  5.26it/s]
  1%|          | 54/4370 [00:12<14:42,  4.89it/s]
  1%|▏         | 55/4370 [00:13<15:50,  4.54it/s]
  1%|▏         | 56/4370 [00:13<15:18,  4.70it/s]
  1%|▏         | 57/4370 [00:13<15:10,  4.74it/s]
  1%|▏         | 58/4370 [00:13<13:57,  5.15it/s]
  1%|▏         | 59/4370 [00:13<14:19,  5.02it/s]
  1%|▏         | 60/4370 [00:14<14:06,  5.09it/s]
  1%|▏         | 61/4370 [00:14<13:29,  5.32it/s]
  1%|▏         | 62/4370 [00:14<13:38,  5.26it/s]
  1%|▏         | 63/4370 [00:14<13:40,  5.25it/s]
  1%|▏         | 64/4370 [00:14<13:15,  5.41it/s]
  1%|▏         | 65/4370 [00:14<13:13,  5.43it/s]
  2%|▏         | 66/4370 [00:15<13:26,  5.33it/s]
  2%|▏         | 67/4370 [00:15<13:04,  5.49it/s]
  2%|▏         | 68/4370 [00:15<13:10,  5.44it/s]
  2%|▏         | 69/4370 [00:15<12:41,  5.65it/s]
  2%|▏         | 70/4370 [00:15<12:56,  5.54it/s]
  2%|▏         | 71/4370 [00:16<13:17,  5.39it/s]
  2%|▏         | 72/4370 [00:16<13:22,  5.35it/s]
  2%|▏         | 73/4370 [00:16<13:01,  5.50it/s]
  2%|▏         | 74/4370 [00:16<12:39,  5.66it/s]
  2%|▏         | 75/4370 [00:16<12:30,  5.72it/s]
  2%|▏         | 76/4370 [00:16<13:10,  5.44it/s]
  2%|▏         | 77/4370 [00:17<13:13,  5.41it/s]
  2%|▏         | 78/4370 [00:17<13:50,  5.17it/s]
  2%|▏         | 79/4370 [00:17<15:01,  4.76it/s]
  2%|▏         | 80/4370 [00:17<15:16,  4.68it/s]
  2%|▏         | 81/4370 [00:17<14:21,  4.98it/s]
  2%|▏         | 82/4370 [00:18<15:20,  4.66it/s]
  2%|▏         | 83/4370 [00:18<14:35,  4.89it/s]
  2%|▏         | 84/4370 [00:18<14:17,  5.00it/s]
  2%|▏         | 85/4370 [00:18<13:48,  5.17it/s]
  2%|▏         | 86/4370 [00:18<13:32,  5.27it/s]
  2%|▏         | 87/4370 [00:19<14:42,  4.85it/s]
  2%|▏         | 88/4370 [00:19<14:55,  4.78it/s]
  2%|▏         | 89/4370 [00:19<14:05,  5.06it/s]
  2%|▏         | 90/4370 [00:19<12:54,  5.53it/s]
  2%|▏         | 91/4370 [00:19<13:10,  5.41it/s]
  2%|▏         | 92/4370 [00:20<13:17,  5.36it/s]
  2%|▏         | 93/4370 [00:20<14:18,  4.98it/s]
  2%|▏         | 94/4370 [00:20<14:15,  5.00it/s]
  2%|▏         | 95/4370 [00:20<13:33,  5.25it/s]
  2%|▏         | 96/4370 [00:20<13:16,  5.37it/s]
  2%|▏         | 97/4370 [00:21<15:12,  4.68it/s]
  2%|▏         | 98/4370 [00:21<14:42,  4.84it/s]
  2%|▏         | 99/4370 [00:21<15:04,  4.72it/s]
  2%|▏         | 100/4370 [00:21<18:08,  3.92it/s]
  2%|▏         | 101/4370 [00:22<17:42,  4.02it/s]
  2%|▏         | 102/4370 [00:22<16:38,  4.28it/s]
  2%|▏         | 103/4370 [00:22<16:15,  4.37it/s]
  2%|▏         | 104/4370 [00:22<16:12,  4.39it/s]
  2%|▏         | 105/4370 [00:23<15:14,  4.66it/s]
  2%|▏         | 106/4370 [00:23<14:16,  4.98it/s]
  2%|▏         | 107/4370 [00:23<13:59,  5.08it/s]
  2%|▏         | 108/4370 [00:23<14:43,  4.83it/s]
  2%|▏         | 109/4370 [00:23<14:26,  4.92it/s]
  3%|▎         | 110/4370 [00:23<14:12,  5.00it/s]
  3%|▎         | 111/4370 [00:24<14:39,  4.84it/s]
  3%|▎         | 112/4370 [00:24<14:02,  5.05it/s]
  3%|▎         | 113/4370 [00:24<15:21,  4.62it/s]
  3%|▎         | 114/4370 [00:24<14:57,  4.74it/s]
  3%|▎         | 115/4370 [00:25<14:05,  5.03it/s]
  3%|▎         | 116/4370 [00:25<15:06,  4.69it/s]
  3%|▎         | 117/4370 [00:25<15:10,  4.67it/s]
  3%|▎         | 118/4370 [00:25<15:20,  4.62it/s]
  3%|▎         | 119/4370 [00:25<14:25,  4.91it/s]
  3%|▎         | 120/4370 [00:26<15:01,  4.72it/s]
  3%|▎         | 121/4370 [00:26<14:28,  4.89it/s]
  3%|▎         | 122/4370 [00:26<15:04,  4.70it/s]
  3%|▎         | 123/4370 [00:26<13:36,  5.20it/s]
  3%|▎         | 124/4370 [00:26<13:44,  5.15it/s]
  3%|▎         | 125/4370 [00:27<14:28,  4.89it/s]
  3%|▎         | 126/4370 [00:27<14:49,  4.77it/s]
  3%|▎         | 127/4370 [00:27<13:41,  5.17it/s]
  3%|▎         | 128/4370 [00:27<12:56,  5.47it/s]
  3%|▎         | 129/4370 [00:27<12:52,  5.49it/s]
  3%|▎         | 130/4370 [00:27<13:10,  5.37it/s]
  3%|▎         | 131/4370 [00:28<12:51,  5.49it/s]
  3%|▎         | 132/4370 [00:28<14:02,  5.03it/s]
  3%|▎         | 133/4370 [00:28<13:44,  5.14it/s]
  3%|▎         | 134/4370 [00:28<12:50,  5.50it/s]
  3%|▎         | 135/4370 [00:28<13:05,  5.39it/s]
  3%|▎         | 136/4370 [00:29<13:24,  5.26it/s]
  3%|▎         | 137/4370 [00:29<13:15,  5.32it/s]
  3%|▎         | 138/4370 [00:29<14:03,  5.02it/s]
  3%|▎         | 139/4370 [00:29<13:13,  5.33it/s]
  3%|▎         | 140/4370 [00:29<13:00,  5.42it/s]
  3%|▎         | 141/4370 [00:30<13:06,  5.38it/s]
  3%|▎         | 142/4370 [00:30<12:44,  5.53it/s]
  3%|▎         | 143/4370 [00:30<14:48,  4.76it/s]
  3%|▎         | 144/4370 [00:30<15:31,  4.54it/s]
  3%|▎         | 145/4370 [00:30<14:01,  5.02it/s]
  3%|▎         | 146/4370 [00:31<13:28,  5.22it/s]
  3%|▎         | 147/4370 [00:31<13:49,  5.09it/s]
  3%|▎         | 148/4370 [00:31<13:42,  5.13it/s]
  3%|▎         | 149/4370 [00:31<13:18,  5.28it/s]
  3%|▎         | 150/4370 [00:31<12:53,  5.46it/s]
  3%|▎         | 151/4370 [00:32<12:32,  5.61it/s]
  3%|▎         | 152/4370 [00:32<12:17,  5.72it/s]
  4%|▎         | 153/4370 [00:32<13:58,  5.03it/s]
  4%|▎         | 154/4370 [00:32<14:12,  4.94it/s]
  4%|▎         | 155/4370 [00:32<14:00,  5.02it/s]
  4%|▎         | 156/4370 [00:33<13:33,  5.18it/s]
  4%|▎         | 157/4370 [00:33<12:58,  5.41it/s]
  4%|▎         | 158/4370 [00:33<13:47,  5.09it/s]
  4%|▎         | 159/4370 [00:33<13:47,  5.09it/s]
  4%|▎         | 160/4370 [00:33<12:46,  5.49it/s]
  4%|▎         | 161/4370 [00:33<12:23,  5.66it/s]
  4%|▎         | 162/4370 [00:34<12:00,  5.84it/s]
  4%|▎         | 163/4370 [00:34<12:57,  5.41it/s]
  4%|▍         | 164/4370 [00:34<13:50,  5.06it/s]
  4%|▍         | 165/4370 [00:34<13:06,  5.34it/s]
  4%|▍         | 166/4370 [00:34<13:41,  5.12it/s]
  4%|▍         | 167/4370 [00:35<15:58,  4.38it/s]
  4%|▍         | 168/4370 [00:35<18:16,  3.83it/s]
  4%|▍         | 169/4370 [00:35<16:34,  4.23it/s]
  4%|▍         | 170/4370 [00:35<17:41,  3.96it/s]
  4%|▍         | 171/4370 [00:36<17:02,  4.10it/s]
  4%|▍         | 172/4370 [00:36<15:22,  4.55it/s]
  4%|▍         | 173/4370 [00:36<16:15,  4.30it/s]
  4%|▍         | 174/4370 [00:36<15:08,  4.62it/s]
  4%|▍         | 175/4370 [00:36<13:27,  5.19it/s]
  4%|▍         | 176/4370 [00:37<12:07,  5.76it/s]
  4%|▍         | 177/4370 [00:37<11:33,  6.04it/s]
  4%|▍         | 178/4370 [00:37<11:21,  6.15it/s]
  4%|▍         | 179/4370 [00:37<11:41,  5.98it/s]
  4%|▍         | 180/4370 [00:37<11:42,  5.97it/s]
  4%|▍         | 181/4370 [00:37<12:23,  5.63it/s]
  4%|▍         | 182/4370 [00:38<12:23,  5.63it/s]
  4%|▍         | 183/4370 [00:38<12:55,  5.40it/s]
  4%|▍         | 184/4370 [00:38<12:51,  5.43it/s]
  4%|▍         | 185/4370 [00:38<13:50,  5.04it/s]
  4%|▍         | 186/4370 [00:38<12:40,  5.50it/s]
  4%|▍         | 187/4370 [00:39<12:10,  5.73it/s]
  4%|▍         | 188/4370 [00:39<12:27,  5.60it/s]
  4%|▍         | 189/4370 [00:39<12:00,  5.80it/s]
  4%|▍         | 190/4370 [00:39<14:08,  4.92it/s]
  4%|▍         | 191/4370 [00:39<14:58,  4.65it/s]
  4%|▍         | 192/4370 [00:40<16:15,  4.28it/s]
  4%|▍         | 193/4370 [00:40<14:54,  4.67it/s]
  4%|▍         | 194/4370 [00:40<13:28,  5.17it/s]
  4%|▍         | 195/4370 [00:40<13:34,  5.13it/s]
  4%|▍         | 196/4370 [00:40<13:01,  5.34it/s]
  5%|▍         | 197/4370 [00:41<15:56,  4.36it/s]
  5%|▍         | 198/4370 [00:41<14:32,  4.78it/s]
  5%|▍         | 199/4370 [00:41<13:11,  5.27it/s]
  5%|▍         | 200/4370 [00:41<14:09,  4.91it/s]
  5%|▍         | 201/4370 [00:42<15:47,  4.40it/s]
  5%|▍         | 202/4370 [00:42<15:29,  4.49it/s]
  5%|▍         | 203/4370 [00:42<15:33,  4.46it/s]
  5%|▍         | 204/4370 [00:42<15:09,  4.58it/s]
  5%|▍         | 205/4370 [00:42<15:35,  4.45it/s]
  5%|▍         | 206/4370 [00:43<14:41,  4.73it/s]
  5%|▍         | 207/4370 [00:43<14:44,  4.71it/s]
  5%|▍         | 208/4370 [00:43<13:42,  5.06it/s]
  5%|▍         | 209/4370 [00:43<13:25,  5.17it/s]
  5%|▍         | 210/4370 [00:43<13:05,  5.29it/s]
  5%|▍         | 211/4370 [00:44<13:24,  5.17it/s]
  5%|▍         | 212/4370 [00:44<12:46,  5.42it/s]
  5%|▍         | 213/4370 [00:44<12:02,  5.75it/s]
  5%|▍         | 214/4370 [00:44<12:02,  5.75it/s]
  5%|▍         | 215/4370 [00:44<12:32,  5.52it/s]
  5%|▍         | 216/4370 [00:44<13:01,  5.31it/s]
  5%|▍         | 217/4370 [00:45<13:15,  5.22it/s]
  5%|▍         | 218/4370 [00:45<13:42,  5.05it/s]
  5%|▌         | 219/4370 [00:45<17:13,  4.02it/s]
  5%|▌         | 220/4370 [00:45<16:01,  4.32it/s]
  5%|▌         | 221/4370 [00:46<14:55,  4.63it/s]
  5%|▌         | 222/4370 [00:46<14:00,  4.94it/s]
  5%|▌         | 223/4370 [00:46<13:55,  4.97it/s]
  5%|▌         | 224/4370 [00:46<13:15,  5.21it/s]
  5%|▌         | 225/4370 [00:46<13:30,  5.11it/s]
  5%|▌         | 226/4370 [00:46<13:34,  5.09it/s]
  5%|▌         | 227/4370 [00:47<15:00,  4.60it/s]
  5%|▌         | 228/4370 [00:47<14:02,  4.92it/s]
  5%|▌         | 229/4370 [00:47<12:59,  5.31it/s]
  5%|▌         | 230/4370 [00:47<12:51,  5.37it/s]
  5%|▌         | 231/4370 [00:47<12:34,  5.48it/s]
  5%|▌         | 232/4370 [00:48<12:41,  5.43it/s]
  5%|▌         | 233/4370 [00:48<12:36,  5.47it/s]
  5%|▌         | 234/4370 [00:48<12:18,  5.60it/s]
  5%|▌         | 235/4370 [00:48<11:43,  5.88it/s]
  5%|▌         | 236/4370 [00:48<12:29,  5.52it/s]
  5%|▌         | 237/4370 [00:49<12:42,  5.42it/s]
  5%|▌         | 238/4370 [00:49<12:37,  5.45it/s]
  5%|▌         | 239/4370 [00:49<12:18,  5.59it/s]
  5%|▌         | 240/4370 [00:49<14:41,  4.69it/s]
  6%|▌         | 241/4370 [00:49<14:45,  4.66it/s]
  6%|▌         | 242/4370 [00:50<15:07,  4.55it/s]
  6%|▌         | 243/4370 [00:50<13:46,  4.99it/s]
  6%|▌         | 244/4370 [00:50<13:42,  5.02it/s]
  6%|▌         | 245/4370 [00:50<13:13,  5.20it/s]
  6%|▌         | 246/4370 [00:50<12:32,  5.48it/s]
  6%|▌         | 247/4370 [00:50<12:36,  5.45it/s]
  6%|▌         | 248/4370 [00:51<12:18,  5.58it/s]
  6%|▌         | 249/4370 [00:51<12:33,  5.47it/s]
  6%|▌         | 250/4370 [00:51<13:26,  5.11it/s]
  6%|▌         | 251/4370 [00:51<12:58,  5.29it/s]
  6%|▌         | 252/4370 [00:51<13:07,  5.23it/s]
  6%|▌         | 253/4370 [00:52<13:03,  5.26it/s]
  6%|▌         | 254/4370 [00:52<12:24,  5.53it/s]
  6%|▌         | 255/4370 [00:52<12:03,  5.68it/s]
  6%|▌         | 256/4370 [00:52<11:50,  5.79it/s]
  6%|▌         | 257/4370 [00:52<11:39,  5.88it/s]
  6%|▌         | 258/4370 [00:52<11:23,  6.02it/s]
  6%|▌         | 259/4370 [00:53<10:40,  6.42it/s]
  6%|▌         | 260/4370 [00:53<10:58,  6.24it/s]
  6%|▌         | 261/4370 [00:53<11:32,  5.93it/s]
  6%|▌         | 262/4370 [00:53<11:50,  5.78it/s]
  6%|▌         | 263/4370 [00:53<12:07,  5.64it/s]
  6%|▌         | 264/4370 [00:53<11:43,  5.83it/s]
  6%|▌         | 265/4370 [00:54<12:58,  5.27it/s]
  6%|▌         | 266/4370 [00:54<12:28,  5.48it/s]
  6%|▌         | 267/4370 [00:54<12:51,  5.32it/s]
  6%|▌         | 268/4370 [00:54<12:53,  5.30it/s]
  6%|▌         | 269/4370 [00:54<13:27,  5.08it/s]
  6%|▌         | 270/4370 [00:55<12:52,  5.31it/s]
  6%|▌         | 271/4370 [00:55<12:42,  5.38it/s]
  6%|▌         | 272/4370 [00:55<11:57,  5.71it/s]
  6%|▌         | 273/4370 [00:55<11:14,  6.07it/s]
  6%|▋         | 274/4370 [00:55<11:29,  5.94it/s]
  6%|▋         | 275/4370 [00:55<11:16,  6.05it/s]
  6%|▋         | 276/4370 [00:56<11:20,  6.02it/s]
  6%|▋         | 277/4370 [00:56<11:36,  5.88it/s]
  6%|▋         | 278/4370 [00:56<12:08,  5.61it/s]
  6%|▋         | 279/4370 [00:56<11:42,  5.82it/s]
  6%|▋         | 280/4370 [00:56<11:37,  5.86it/s]
  6%|▋         | 281/4370 [00:57<15:06,  4.51it/s]
  6%|▋         | 282/4370 [00:57<13:59,  4.87it/s]
  6%|▋         | 283/4370 [00:57<12:54,  5.27it/s]
  6%|▋         | 284/4370 [00:57<13:46,  4.94it/s]
  7%|▋         | 285/4370 [00:57<12:28,  5.46it/s]
  7%|▋         | 286/4370 [00:58<12:00,  5.67it/s]
  7%|▋         | 287/4370 [00:58<11:41,  5.82it/s]
  7%|▋         | 288/4370 [00:58<14:06,  4.82it/s]
  7%|▋         | 289/4370 [00:58<14:23,  4.73it/s]
  7%|▋         | 290/4370 [00:58<13:58,  4.87it/s]
  7%|▋         | 291/4370 [00:59<13:33,  5.01it/s]
  7%|▋         | 292/4370 [00:59<14:16,  4.76it/s]
  7%|▋         | 293/4370 [00:59<16:31,  4.11it/s]
  7%|▋         | 294/4370 [00:59<15:47,  4.30it/s]
  7%|▋         | 295/4370 [01:00<14:56,  4.55it/s]
  7%|▋         | 296/4370 [01:00<14:46,  4.60it/s]
  7%|▋         | 297/4370 [01:00<13:47,  4.92it/s]
  7%|▋         | 298/4370 [01:00<15:27,  4.39it/s]
  7%|▋         | 299/4370 [01:00<14:30,  4.68it/s]
  7%|▋         | 300/4370 [01:01<14:23,  4.71it/s]
  7%|▋         | 301/4370 [01:01<13:30,  5.02it/s]
  7%|▋         | 302/4370 [01:01<13:50,  4.90it/s]
  7%|▋         | 303/4370 [01:01<14:39,  4.62it/s]
  7%|▋         | 304/4370 [01:01<13:16,  5.11it/s]
  7%|▋         | 305/4370 [01:02<12:30,  5.42it/s]
  7%|▋         | 306/4370 [01:02<11:57,  5.66it/s]
  7%|▋         | 307/4370 [01:02<11:08,  6.08it/s]
  7%|▋         | 308/4370 [01:02<10:51,  6.23it/s]
  7%|▋         | 309/4370 [01:02<11:55,  5.68it/s]
  7%|▋         | 310/4370 [01:02<12:17,  5.51it/s]
  7%|▋         | 311/4370 [01:03<12:34,  5.38it/s]
  7%|▋         | 312/4370 [01:03<12:23,  5.46it/s]
  7%|▋         | 313/4370 [01:03<13:14,  5.10it/s]
  7%|▋         | 314/4370 [01:03<12:38,  5.35it/s]
  7%|▋         | 315/4370 [01:03<12:40,  5.33it/s]
  7%|▋         | 316/4370 [01:03<12:39,  5.34it/s]
  7%|▋         | 317/4370 [01:04<12:12,  5.54it/s]
  7%|▋         | 318/4370 [01:04<13:37,  4.96it/s]
  7%|▋         | 319/4370 [01:04<13:45,  4.91it/s]
  7%|▋         | 320/4370 [01:04<14:14,  4.74it/s]
  7%|▋         | 321/4370 [01:05<14:06,  4.78it/s]
  7%|▋         | 322/4370 [01:05<13:02,  5.17it/s]
  7%|▋         | 323/4370 [01:05<11:47,  5.72it/s]
  7%|▋         | 324/4370 [01:05<11:47,  5.72it/s]
  7%|▋         | 325/4370 [01:05<11:29,  5.87it/s]
  7%|▋         | 326/4370 [01:05<11:53,  5.67it/s]
  7%|▋         | 327/4370 [01:06<11:57,  5.63it/s]
  8%|▊         | 328/4370 [01:06<11:54,  5.65it/s]
  8%|▊         | 329/4370 [01:06<12:36,  5.34it/s]
  8%|▊         | 330/4370 [01:06<14:24,  4.67it/s]
  8%|▊         | 331/4370 [01:06<13:43,  4.90it/s]
  8%|▊         | 332/4370 [01:07<14:43,  4.57it/s]
  8%|▊         | 333/4370 [01:07<13:56,  4.83it/s]
  8%|▊         | 334/4370 [01:07<12:54,  5.21it/s]
  8%|▊         | 335/4370 [01:07<12:38,  5.32it/s]
  8%|▊         | 336/4370 [01:07<13:10,  5.10it/s]
  8%|▊         | 337/4370 [01:08<12:53,  5.21it/s]
  8%|▊         | 338/4370 [01:08<12:20,  5.45it/s]
  8%|▊         | 339/4370 [01:08<12:01,  5.59it/s]
  8%|▊         | 340/4370 [01:08<12:22,  5.43it/s]
  8%|▊         | 341/4370 [01:08<12:14,  5.48it/s]
  8%|▊         | 342/4370 [01:08<12:04,  5.56it/s]
  8%|▊         | 343/4370 [01:09<14:07,  4.75it/s]
  8%|▊         | 344/4370 [01:09<13:17,  5.05it/s]
  8%|▊         | 345/4370 [01:09<14:02,  4.78it/s]
  8%|▊         | 346/4370 [01:09<15:17,  4.39it/s]
  8%|▊         | 347/4370 [01:10<14:10,  4.73it/s]
  8%|▊         | 348/4370 [01:10<14:04,  4.76it/s]
  8%|▊         | 349/4370 [01:10<13:58,  4.79it/s]
  8%|▊         | 350/4370 [01:10<14:39,  4.57it/s]
  8%|▊         | 351/4370 [01:10<14:24,  4.65it/s]
  8%|▊         | 352/4370 [01:11<14:48,  4.52it/s]
  8%|▊         | 353/4370 [01:11<15:40,  4.27it/s]
  8%|▊         | 354/4370 [01:11<14:22,  4.65it/s]
  8%|▊         | 355/4370 [01:12<21:07,  3.17it/s]
  8%|▊         | 356/4370 [01:12<19:30,  3.43it/s]
  8%|▊         | 357/4370 [01:12<18:03,  3.71it/s]
  8%|▊         | 358/4370 [01:12<16:24,  4.07it/s]
  8%|▊         | 359/4370 [01:12<15:15,  4.38it/s]
  8%|▊         | 360/4370 [01:13<14:40,  4.55it/s]
  8%|▊         | 361/4370 [01:13<14:47,  4.52it/s]
  8%|▊         | 362/4370 [01:13<14:35,  4.58it/s]
  8%|▊         | 363/4370 [01:13<13:24,  4.98it/s]
  8%|▊         | 364/4370 [01:14<15:51,  4.21it/s]
  8%|▊         | 365/4370 [01:14<14:39,  4.56it/s]
  8%|▊         | 366/4370 [01:14<15:40,  4.26it/s]
  8%|▊         | 367/4370 [01:14<14:59,  4.45it/s]
  8%|▊         | 368/4370 [01:14<13:25,  4.97it/s]
  8%|▊         | 369/4370 [01:15<12:57,  5.14it/s]
  8%|▊         | 370/4370 [01:15<12:21,  5.39it/s]
  8%|▊         | 371/4370 [01:15<14:17,  4.67it/s]
  9%|▊         | 372/4370 [01:15<13:56,  4.78it/s]
  9%|▊         | 373/4370 [01:15<13:01,  5.11it/s]
  9%|▊         | 374/4370 [01:16<16:01,  4.16it/s]
  9%|▊         | 375/4370 [01:16<14:59,  4.44it/s]
  9%|▊         | 376/4370 [01:16<13:31,  4.92it/s]
  9%|▊         | 377/4370 [01:16<12:49,  5.19it/s]
  9%|▊         | 378/4370 [01:16<12:49,  5.19it/s]
  9%|▊         | 379/4370 [01:17<13:49,  4.81it/s]
  9%|▊         | 380/4370 [01:17<13:22,  4.97it/s]
  9%|▊         | 381/4370 [01:17<13:40,  4.86it/s]
  9%|▊         | 382/4370 [01:17<14:12,  4.68it/s]
  9%|▉         | 383/4370 [01:17<12:48,  5.19it/s]
  9%|▉         | 384/4370 [01:18<13:08,  5.05it/s]
  9%|▉         | 385/4370 [01:18<13:33,  4.90it/s]
  9%|▉         | 386/4370 [01:18<13:35,  4.88it/s]
  9%|▉         | 387/4370 [01:18<14:55,  4.45it/s]
  9%|▉         | 388/4370 [01:19<13:31,  4.91it/s]
  9%|▉         | 389/4370 [01:19<12:17,  5.40it/s]
  9%|▉         | 390/4370 [01:19<13:00,  5.10it/s]
  9%|▉         | 391/4370 [01:19<12:16,  5.40it/s]
  9%|▉         | 392/4370 [01:19<11:54,  5.57it/s]
  9%|▉         | 393/4370 [01:20<15:09,  4.37it/s]
  9%|▉         | 394/4370 [01:20<14:43,  4.50it/s]
  9%|▉         | 395/4370 [01:20<13:39,  4.85it/s]
  9%|▉         | 396/4370 [01:20<13:40,  4.84it/s]
  9%|▉         | 397/4370 [01:20<13:20,  4.96it/s]
  9%|▉         | 398/4370 [01:21<13:27,  4.92it/s]
  9%|▉         | 399/4370 [01:21<13:08,  5.04it/s]
  9%|▉         | 400/4370 [01:21<13:02,  5.07it/s]
  9%|▉         | 401/4370 [01:21<13:21,  4.95it/s]
  9%|▉         | 402/4370 [01:21<13:17,  4.98it/s]
  9%|▉         | 403/4370 [01:21<12:43,  5.20it/s]
  9%|▉         | 404/4370 [01:22<13:02,  5.07it/s]
  9%|▉         | 405/4370 [01:22<12:05,  5.46it/s]
  9%|▉         | 406/4370 [01:22<11:16,  5.86it/s]
  9%|▉         | 407/4370 [01:22<10:41,  6.17it/s]
  9%|▉         | 408/4370 [01:22<11:49,  5.59it/s]
  9%|▉         | 409/4370 [01:23<12:47,  5.16it/s]
  9%|▉         | 410/4370 [01:23<14:15,  4.63it/s]
  9%|▉         | 411/4370 [01:23<13:57,  4.73it/s]
  9%|▉         | 412/4370 [01:23<13:41,  4.82it/s]
  9%|▉         | 413/4370 [01:23<13:26,  4.90it/s]
  9%|▉         | 414/4370 [01:24<13:05,  5.03it/s]
  9%|▉         | 415/4370 [01:24<13:16,  4.97it/s]
 10%|▉         | 416/4370 [01:24<14:25,  4.57it/s]
 10%|▉         | 417/4370 [01:24<15:14,  4.32it/s]
 10%|▉         | 418/4370 [01:25<14:02,  4.69it/s]
 10%|▉         | 419/4370 [01:25<12:50,  5.13it/s]
 10%|▉         | 420/4370 [01:25<12:20,  5.33it/s]
 10%|▉         | 421/4370 [01:25<12:30,  5.26it/s]
 10%|▉         | 422/4370 [01:25<11:48,  5.57it/s]
 10%|▉         | 423/4370 [01:25<13:00,  5.05it/s]
 10%|▉         | 424/4370 [01:26<12:50,  5.12it/s]
 10%|▉         | 425/4370 [01:26<13:31,  4.86it/s]
 10%|▉         | 426/4370 [01:26<13:01,  5.04it/s]
 10%|▉         | 427/4370 [01:26<14:21,  4.58it/s]
 10%|▉         | 428/4370 [01:27<18:00,  3.65it/s]
 10%|▉         | 429/4370 [01:27<15:39,  4.19it/s]
 10%|▉         | 430/4370 [01:27<15:31,  4.23it/s]
 10%|▉         | 431/4370 [01:27<13:59,  4.69it/s]
 10%|▉         | 432/4370 [01:28<15:00,  4.38it/s]
 10%|▉         | 433/4370 [01:28<14:16,  4.60it/s]
 10%|▉         | 434/4370 [01:28<13:14,  4.96it/s]
 10%|▉         | 435/4370 [01:28<12:42,  5.16it/s]
 10%|▉         | 436/4370 [01:28<13:00,  5.04it/s]
 10%|█         | 437/4370 [01:28<12:31,  5.23it/s][INFO|trainer.py:811] 2024-09-05 20:48:30,653 >> The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: id, tokens, ner_tags. If id, tokens, ner_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
+[INFO|trainer.py:3819] 2024-09-05 20:48:30,656 >> 
+***** Running Evaluation *****
+[INFO|trainer.py:3821] 2024-09-05 20:48:30,656 >>   Num examples = 6946
+[INFO|trainer.py:3824] 2024-09-05 20:48:30,656 >>   Batch size = 8
+
+
  0%|          | 0/869 [00:00<?, ?it/s]
+
  1%|▏         | 11/869 [00:00<00:08, 101.23it/s]
+
  3%|▎         | 22/869 [00:00<00:09, 91.51it/s] 
+
  4%|▎         | 32/869 [00:00<00:09, 90.28it/s]
+
  5%|▍         | 42/869 [00:00<00:09, 91.73it/s]
+
  6%|▌         | 52/869 [00:00<00:08, 92.54it/s]
+
  7%|▋         | 62/869 [00:00<00:08, 92.44it/s]
+
  8%|▊         | 72/869 [00:00<00:08, 90.47it/s]
+
  9%|▉         | 82/869 [00:00<00:08, 92.22it/s]
+
 11%|█         | 92/869 [00:00<00:08, 92.98it/s]
+
 12%|█▏        | 102/869 [00:01<00:08, 94.35it/s]
+
 13%|█▎        | 112/869 [00:01<00:08, 92.62it/s]
+
 14%|█▍        | 122/869 [00:01<00:08, 91.65it/s]
+
 15%|█▌        | 132/869 [00:01<00:08, 91.74it/s]
+
 16%|█▋        | 142/869 [00:01<00:08, 90.63it/s]
+
 17%|█▋        | 152/869 [00:01<00:07, 90.80it/s]
+
 19%|█▊        | 162/869 [00:01<00:07, 90.11it/s]
+
 20%|█▉        | 172/869 [00:01<00:07, 89.26it/s]
+
 21%|██        | 181/869 [00:01<00:07, 88.34it/s]
+
 22%|██▏       | 190/869 [00:02<00:07, 87.81it/s]
+
 23%|██▎       | 200/869 [00:02<00:07, 90.06it/s]
+
 24%|██▍       | 210/869 [00:02<00:07, 91.03it/s]
+
 25%|██▌       | 220/869 [00:02<00:07, 92.47it/s]
+
 26%|██▋       | 230/869 [00:02<00:06, 91.95it/s]
+
 28%|██▊       | 240/869 [00:02<00:07, 89.75it/s]
+
 29%|██▊       | 249/869 [00:02<00:07, 87.37it/s]
+
 30%|██▉       | 259/869 [00:02<00:06, 90.63it/s]
+
 31%|███       | 269/869 [00:02<00:06, 90.54it/s]
+
 32%|███▏      | 279/869 [00:03<00:06, 90.05it/s]
+
 33%|███▎      | 289/869 [00:03<00:06, 89.12it/s]
+
 34%|███▍      | 298/869 [00:03<00:06, 88.93it/s]
+
 35%|███▌      | 307/869 [00:03<00:06, 88.88it/s]
+
 36%|███▋      | 317/869 [00:03<00:06, 89.54it/s]
+
 38%|███▊      | 326/869 [00:03<00:06, 89.59it/s]
+
 39%|███▊      | 336/869 [00:03<00:05, 91.34it/s]
+
 40%|███▉      | 346/869 [00:03<00:05, 89.56it/s]
+
 41%|████      | 356/869 [00:03<00:05, 90.14it/s]
+
 42%|████▏     | 366/869 [00:04<00:05, 92.17it/s]
+
 43%|████▎     | 376/869 [00:04<00:05, 88.58it/s]
+
 44%|████▍     | 385/869 [00:04<00:05, 88.83it/s]
+
 45%|████▌     | 394/869 [00:04<00:05, 87.30it/s]
+
 46%|████▋     | 403/869 [00:04<00:05, 87.21it/s]
+
 48%|████▊     | 413/869 [00:04<00:05, 90.10it/s]
+
 49%|████▊     | 423/869 [00:04<00:04, 90.84it/s]
+
 50%|████▉     | 433/869 [00:04<00:04, 91.10it/s]
+
 51%|█████     | 443/869 [00:04<00:04, 87.90it/s]
+
 52%|█████▏    | 453/869 [00:05<00:04, 90.57it/s]
+
 53%|█████▎    | 463/869 [00:05<00:04, 89.49it/s]
+
 54%|█████▍    | 473/869 [00:05<00:04, 91.35it/s]
+
 56%|█████▌    | 484/869 [00:05<00:04, 93.70it/s]
+
 57%|█████▋    | 494/869 [00:05<00:04, 90.76it/s]
+
 58%|█████▊    | 504/869 [00:05<00:04, 88.92it/s]
+
 59%|█████▉    | 514/869 [00:05<00:03, 90.62it/s]
+
 60%|██████    | 524/869 [00:05<00:03, 87.02it/s]
+
 61%|██████▏   | 533/869 [00:05<00:03, 87.76it/s]
+
 62%|██████▏   | 542/869 [00:06<00:03, 85.30it/s]
+
 63%|██████▎   | 551/869 [00:06<00:03, 86.30it/s]
+
 65%|██████▍   | 561/869 [00:06<00:03, 88.08it/s]
+
 66%|██████▌   | 570/869 [00:06<00:03, 88.12it/s]
+
 67%|██████▋   | 580/869 [00:06<00:03, 88.75it/s]
+
 68%|██████▊   | 590/869 [00:06<00:03, 89.63it/s]
+
 69%|██████▉   | 600/869 [00:06<00:02, 91.18it/s]
+
 70%|███████   | 610/869 [00:06<00:02, 90.68it/s]
+
 71%|███████▏  | 620/869 [00:06<00:02, 90.38it/s]
+
 72%|███████▏  | 630/869 [00:06<00:02, 91.00it/s]
+
 74%|███████▎  | 640/869 [00:07<00:02, 90.13it/s]
+
 75%|███████▍  | 650/869 [00:07<00:02, 90.69it/s]
+
 76%|███████▌  | 660/869 [00:07<00:02, 90.37it/s]
+
 77%|███████▋  | 670/869 [00:07<00:02, 91.58it/s]
+
 78%|███████▊  | 680/869 [00:07<00:02, 93.04it/s]
+
 79%|███████▉  | 690/869 [00:07<00:02, 86.86it/s]
+
 81%|████████  | 700/869 [00:07<00:01, 88.28it/s]
+
 82%|████████▏ | 710/869 [00:07<00:01, 90.00it/s]
+
 83%|████████▎ | 720/869 [00:07<00:01, 89.02it/s]
+
 84%|████████▍ | 730/869 [00:08<00:01, 90.80it/s]
+
 85%|████████▌ | 740/869 [00:08<00:01, 91.14it/s]
+
 86%|████████▋ | 750/869 [00:08<00:01, 90.37it/s]
+
 87%|████████▋ | 760/869 [00:08<00:01, 91.86it/s]
+
 89%|████████▊ | 770/869 [00:08<00:01, 89.85it/s]
+
 90%|████████▉ | 780/869 [00:08<00:01, 84.79it/s]
+
 91%|█████████ | 790/869 [00:08<00:00, 87.94it/s]
+
 92%|█████████▏| 800/869 [00:08<00:00, 90.37it/s]
+
 93%|█████████▎| 810/869 [00:08<00:00, 91.73it/s]
+
 94%|█████████▍| 820/869 [00:09<00:00, 90.53it/s]
+
 96%|█████████▌| 830/869 [00:09<00:00, 93.05it/s]
+
 97%|█████████▋| 840/869 [00:09<00:00, 93.45it/s]
+
 98%|█████████▊| 851/869 [00:09<00:00, 95.58it/s]
+
 99%|█████████▉| 861/869 [00:09<00:00, 92.09it/s]
                                                  
+
                                                 
 10%|█         | 437/4370 [01:42<12:31,  5.23it/s]
+
+
                                                 [INFO|trainer.py:3503] 2024-09-05 20:48:44,033 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-437
+[INFO|configuration_utils.py:472] 2024-09-05 20:48:44,034 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-437/config.json
+[INFO|modeling_utils.py:2799] 2024-09-05 20:48:44,918 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-437/model.safetensors
+[INFO|tokenization_utils_base.py:2684] 2024-09-05 20:48:44,919 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-437/tokenizer_config.json
+[INFO|tokenization_utils_base.py:2693] 2024-09-05 20:48:44,919 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-437/special_tokens_map.json
+[INFO|tokenization_utils_base.py:2684] 2024-09-05 20:48:46,827 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
+[INFO|tokenization_utils_base.py:2693] 2024-09-05 20:48:46,827 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
+
 10%|█         | 438/4370 [01:45<5:31:31,  5.06s/it]
 10%|█         | 439/4370 [01:45<3:56:14,  3.61s/it]
 10%|█         | 440/4370 [01:45<2:49:11,  2.58s/it]
 10%|█         | 441/4370 [01:46<2:03:39,  1.89s/it]
 10%|█         | 442/4370 [01:46<1:29:45,  1.37s/it]
 10%|█         | 443/4370 [01:46<1:06:06,  1.01s/it]
 10%|█         | 444/4370 [01:46<50:50,  1.29it/s]  
 10%|█         | 445/4370 [01:46<40:42,  1.61it/s]
 10%|█         | 446/4370 [01:47<32:01,  2.04it/s]
 10%|█         | 447/4370 [01:47<25:57,  2.52it/s]
 10%|█         | 448/4370 [01:47<22:16,  2.93it/s]
 10%|█         | 449/4370 [01:47<18:51,  3.47it/s]
 10%|█         | 450/4370 [01:47<17:21,  3.77it/s]
 10%|█         | 451/4370 [01:48<16:41,  3.91it/s]
 10%|█         | 452/4370 [01:48<17:52,  3.65it/s]
 10%|█         | 453/4370 [01:48<16:01,  4.07it/s]
 10%|█         | 454/4370 [01:48<14:31,  4.49it/s]
 10%|█         | 455/4370 [01:48<14:12,  4.59it/s]
 10%|█         | 456/4370 [01:49<13:57,  4.68it/s]
 10%|█         | 457/4370 [01:49<13:45,  4.74it/s]
 10%|█         | 458/4370 [01:49<13:32,  4.82it/s]
 11%|█         | 459/4370 [01:49<14:12,  4.59it/s]
 11%|█         | 460/4370 [01:49<12:33,  5.19it/s]
 11%|█         | 461/4370 [01:50<12:08,  5.37it/s]
 11%|█         | 462/4370 [01:50<12:06,  5.38it/s]
 11%|█         | 463/4370 [01:50<15:08,  4.30it/s]
 11%|█         | 464/4370 [01:50<14:44,  4.42it/s]
 11%|█         | 465/4370 [01:50<13:11,  4.93it/s]
 11%|█         | 466/4370 [01:51<13:08,  4.95it/s]
 11%|█         | 467/4370 [01:51<12:43,  5.11it/s]
 11%|█         | 468/4370 [01:51<12:43,  5.11it/s]
 11%|█         | 469/4370 [01:51<14:53,  4.37it/s]
 11%|█         | 470/4370 [01:52<15:05,  4.31it/s]
 11%|█         | 471/4370 [01:52<15:29,  4.19it/s]
 11%|█         | 472/4370 [01:52<14:31,  4.47it/s]
 11%|█         | 473/4370 [01:52<13:49,  4.70it/s]
 11%|█         | 474/4370 [01:52<12:24,  5.24it/s]
 11%|█         | 475/4370 [01:53<13:27,  4.82it/s]
 11%|█         | 476/4370 [01:53<13:34,  4.78it/s]
 11%|█         | 477/4370 [01:53<12:56,  5.01it/s]
 11%|█         | 478/4370 [01:53<13:10,  4.92it/s]
 11%|█         | 479/4370 [01:53<12:51,  5.04it/s]
 11%|█         | 480/4370 [01:54<12:48,  5.06it/s]
 11%|█         | 481/4370 [01:54<13:03,  4.96it/s]
 11%|█         | 482/4370 [01:54<12:28,  5.19it/s]
 11%|█         | 483/4370 [01:54<12:10,  5.32it/s]
 11%|█         | 484/4370 [01:54<12:40,  5.11it/s]
 11%|█         | 485/4370 [01:55<13:10,  4.92it/s]
 11%|█         | 486/4370 [01:55<13:00,  4.98it/s]
 11%|█         | 487/4370 [01:55<13:17,  4.87it/s]
 11%|█         | 488/4370 [01:55<12:37,  5.13it/s]
 11%|█         | 489/4370 [01:55<12:53,  5.02it/s]
 11%|█         | 490/4370 [01:56<12:48,  5.05it/s]
 11%|█         | 491/4370 [01:56<12:04,  5.35it/s]
 11%|█▏        | 492/4370 [01:56<11:50,  5.46it/s]
 11%|█▏        | 493/4370 [01:56<11:57,  5.40it/s]
 11%|█▏        | 494/4370 [01:56<11:10,  5.78it/s]
 11%|█▏        | 495/4370 [01:56<10:53,  5.93it/s]
 11%|█▏        | 496/4370 [01:57<11:08,  5.79it/s]
 11%|█▏        | 497/4370 [01:57<10:32,  6.12it/s]
 11%|█▏        | 498/4370 [01:57<12:01,  5.36it/s]
 11%|█▏        | 499/4370 [01:57<12:16,  5.26it/s]
 11%|█▏        | 500/4370 [01:57<12:51,  5.01it/s]
                                                  
 11%|█▏        | 500/4370 [01:57<12:51,  5.01it/s]
 11%|█▏        | 501/4370 [01:58<13:06,  4.92it/s]
 11%|█▏        | 502/4370 [01:58<13:53,  4.64it/s]
 12%|█▏        | 503/4370 [01:58<13:15,  4.86it/s]
 12%|█▏        | 504/4370 [01:58<13:34,  4.74it/s]
 12%|█▏        | 505/4370 [01:58<13:00,  4.95it/s]
 12%|█▏        | 506/4370 [01:59<12:26,  5.18it/s]
 12%|█▏        | 507/4370 [01:59<11:54,  5.41it/s]
 12%|█▏        | 508/4370 [01:59<12:10,  5.28it/s]
 12%|█▏        | 509/4370 [01:59<11:43,  5.49it/s]
 12%|█▏        | 510/4370 [01:59<12:48,  5.03it/s]
 12%|█▏        | 511/4370 [02:00<12:13,  5.26it/s]
 12%|█▏        | 512/4370 [02:00<11:58,  5.37it/s]
 12%|█▏        | 513/4370 [02:00<11:53,  5.40it/s]
 12%|█▏        | 514/4370 [02:00<12:07,  5.30it/s]
 12%|█▏        | 515/4370 [02:00<11:44,  5.47it/s]
 12%|█▏        | 516/4370 [02:00<10:50,  5.93it/s]
 12%|█▏        | 517/4370 [02:01<10:36,  6.05it/s]
 12%|█▏        | 518/4370 [02:01<10:51,  5.91it/s]
 12%|█▏        | 519/4370 [02:01<10:24,  6.16it/s]
 12%|█▏        | 520/4370 [02:01<10:17,  6.23it/s]
 12%|█▏        | 521/4370 [02:01<10:32,  6.09it/s]
 12%|█▏        | 522/4370 [02:01<12:40,  5.06it/s]
 12%|█▏        | 523/4370 [02:02<13:08,  4.88it/s]
 12%|█▏        | 524/4370 [02:02<12:18,  5.21it/s]
 12%|█▏        | 525/4370 [02:02<12:10,  5.26it/s]
 12%|█▏        | 526/4370 [02:02<12:21,  5.19it/s]
 12%|█▏        | 527/4370 [02:02<11:18,  5.66it/s]
 12%|█▏        | 528/4370 [02:03<11:08,  5.75it/s]
 12%|█▏        | 529/4370 [02:03<13:55,  4.59it/s]
 12%|█▏        | 530/4370 [02:03<13:25,  4.77it/s]
 12%|█▏        | 531/4370 [02:03<12:38,  5.06it/s]
 12%|█▏        | 532/4370 [02:03<12:11,  5.24it/s]
 12%|█▏        | 533/4370 [02:04<11:58,  5.34it/s]
 12%|█▏        | 534/4370 [02:04<11:35,  5.52it/s]
 12%|█▏        | 535/4370 [02:04<11:43,  5.45it/s]
 12%|█▏        | 536/4370 [02:04<12:30,  5.11it/s]
 12%|█▏        | 537/4370 [02:04<13:41,  4.66it/s]
 12%|█▏        | 538/4370 [02:05<12:49,  4.98it/s]
 12%|█▏        | 539/4370 [02:05<12:40,  5.04it/s]
 12%|█▏        | 540/4370 [02:05<12:03,  5.30it/s]
 12%|█▏        | 541/4370 [02:05<11:44,  5.44it/s]
 12%|█▏        | 542/4370 [02:05<11:32,  5.52it/s]
 12%|█▏        | 543/4370 [02:05<11:43,  5.44it/s]
 12%|█▏        | 544/4370 [02:06<12:52,  4.95it/s]
 12%|█▏        | 545/4370 [02:06<14:00,  4.55it/s]
 12%|█▏        | 546/4370 [02:06<13:39,  4.67it/s]
 13%|█▎        | 547/4370 [02:06<13:14,  4.81it/s]
 13%|█▎        | 548/4370 [02:07<14:12,  4.49it/s]
 13%|█▎        | 549/4370 [02:07<12:46,  4.99it/s]
 13%|█▎        | 550/4370 [02:07<12:04,  5.28it/s]
 13%|█▎        | 551/4370 [02:07<13:56,  4.57it/s]
 13%|█▎        | 552/4370 [02:07<14:35,  4.36it/s]
 13%|█▎        | 553/4370 [02:08<13:14,  4.80it/s]
 13%|█▎        | 554/4370 [02:08<14:49,  4.29it/s]
 13%|█▎        | 555/4370 [02:08<13:40,  4.65it/s]
 13%|█▎        | 556/4370 [02:08<13:31,  4.70it/s]
 13%|█▎        | 557/4370 [02:09<13:50,  4.59it/s]
 13%|█▎        | 558/4370 [02:09<14:19,  4.44it/s]
 13%|█▎        | 559/4370 [02:09<14:10,  4.48it/s]
 13%|█▎        | 560/4370 [02:09<13:48,  4.60it/s]
 13%|█▎        | 561/4370 [02:09<12:40,  5.01it/s]

training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:13556e6c97b2f39e25d5830ab0bc61ce81f807bcf643d150d23dd97c2f606c57
+size 5240