Training in progress, epoch 2

README.md

@@ -0,0 +1,103 @@
+---
+library_name: transformers
+license: apache-2.0
+base_model: michiyasunaga/BioLinkBERT-base
+tags:
+- token-classification
+- generated_from_trainer
+datasets:
+- Rodrigo1771/drugtemist-en-75-ner
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: output
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: Rodrigo1771/drugtemist-en-75-ner
+      type: Rodrigo1771/drugtemist-en-75-ner
+      config: DrugTEMIST English NER
+      split: validation
+      args: DrugTEMIST English NER
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9342105263157895
+    - name: Recall
+      type: recall
+      value: 0.9263746505125815
+    - name: F1
+      type: f1
+      value: 0.930276087973795
+    - name: Accuracy
+      type: accuracy
+      value: 0.9987162671280663
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# output
+
+This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on the Rodrigo1771/drugtemist-en-75-ner dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0065
+- Precision: 0.9342
+- Recall: 0.9264
+- F1: 0.9303
+- Accuracy: 0.9987
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 32
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 64
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10.0
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0189        | 1.0   | 504  | 0.0052          | 0.8712    | 0.9394 | 0.9040 | 0.9984   |
+| 0.0047        | 2.0   | 1008 | 0.0048          | 0.9253    | 0.9236 | 0.9244 | 0.9987   |
+| 0.0027        | 3.0   | 1512 | 0.0059          | 0.9252    | 0.9226 | 0.9239 | 0.9986   |
+| 0.0015        | 4.0   | 2016 | 0.0065          | 0.9342    | 0.9264 | 0.9303 | 0.9987   |
+| 0.0011        | 5.0   | 2520 | 0.0073          | 0.9073    | 0.9394 | 0.9231 | 0.9986   |
+| 0.0005        | 6.0   | 3024 | 0.0090          | 0.9191    | 0.9217 | 0.9204 | 0.9984   |
+| 0.0007        | 7.0   | 3528 | 0.0084          | 0.9074    | 0.9310 | 0.9190 | 0.9986   |
+| 0.0004        | 8.0   | 4032 | 0.0085          | 0.9093    | 0.9338 | 0.9214 | 0.9986   |
+| 0.0003        | 9.0   | 4536 | 0.0080          | 0.9186    | 0.9357 | 0.9271 | 0.9987   |
+| 0.0002        | 10.0  | 5040 | 0.0083          | 0.9210    | 0.9348 | 0.9278 | 0.9987   |
+
+
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.4.0+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

all_results.json

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+{
+    "epoch": 10.0,
+    "eval_accuracy": 0.9987162671280663,
+    "eval_f1": 0.930276087973795,
+    "eval_loss": 0.0064844791777431965,
+    "eval_precision": 0.9342105263157895,
+    "eval_recall": 0.9263746505125815,
+    "eval_runtime": 13.1881,
+    "eval_samples": 6946,
+    "eval_samples_per_second": 526.688,
+    "eval_steps_per_second": 65.893,
+    "predict_accuracy": 0.9986488402874071,
+    "predict_f1": 0.9212396929201025,
+    "predict_loss": 0.006953952368348837,
+    "predict_precision": 0.9025069637883009,
+    "predict_recall": 0.9407665505226481,
+    "predict_runtime": 26.178,
+    "predict_samples_per_second": 562.112,
+    "predict_steps_per_second": 70.288,
+    "total_flos": 1.394320679130096e+16,
+    "train_loss": 0.0030765269683407886,
+    "train_runtime": 1249.6681,
+    "train_samples": 32232,
+    "train_samples_per_second": 257.924,
+    "train_steps_per_second": 4.033
+}

config.json

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+{
+  "_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
+}

eval_results.json

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+{
+    "epoch": 10.0,
+    "eval_accuracy": 0.9987162671280663,
+    "eval_f1": 0.930276087973795,
+    "eval_loss": 0.0064844791777431965,
+    "eval_precision": 0.9342105263157895,
+    "eval_recall": 0.9263746505125815,
+    "eval_runtime": 13.1881,
+    "eval_samples": 6946,
+    "eval_samples_per_second": 526.688,
+    "eval_steps_per_second": 65.893
+}

model.safetensors

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predict_results.json

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+{
+    "predict_accuracy": 0.9986488402874071,
+    "predict_f1": 0.9212396929201025,
+    "predict_loss": 0.006953952368348837,
+    "predict_precision": 0.9025069637883009,
+    "predict_recall": 0.9407665505226481,
+    "predict_runtime": 26.178,
+    "predict_samples_per_second": 562.112,
+    "predict_steps_per_second": 70.288
+}

special_tokens_map.json

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+{
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "lstrip": false,
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+    "single_word": false
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+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

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tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "2": {
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+      "single_word": false,
+      "special": true
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  },
+  "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

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+2024-09-05 09:20:26.769117: 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 09:20:26.787235: 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 09:20:26.808505: 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 09:20:26.814951: 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 09:20:26.830934: 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 09:20:28.127288: 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 09:20:29 - WARNING - __main__ -   Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
+09/05/2024 09:20:29 - 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,
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+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 09:20:46,434 >> 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 09:20:46,437 >> 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 09:20:46,675 >> 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 09:20:46,675 >> 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 09:20:46,675 >> loading file added_tokens.json from cache at None
+[INFO|tokenization_utils_base.py:2269] 2024-09-05 09:20:46,675 >> 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 09:20:46,675 >> 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 09:20:46,972 >> 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 09:20:47,051 >> 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 09:20:47,051 >> 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 09:20:54,057 >> The following columns in the training set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, ner_tags, id. If tokens, ner_tags, id are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
+[INFO|trainer.py:2134] 2024-09-05 09:20:54,619 >> ***** Running training *****
+[INFO|trainer.py:2135] 2024-09-05 09:20:54,619 >>   Num examples = 31,536
+[INFO|trainer.py:2136] 2024-09-05 09:20:54,619 >>   Num Epochs = 10
+[INFO|trainer.py:2137] 2024-09-05 09:20:54,619 >>   Instantaneous batch size per device = 32
+[INFO|trainer.py:2140] 2024-09-05 09:20:54,619 >>   Total train batch size (w. parallel, distributed & accumulation) = 64
+[INFO|trainer.py:2141] 2024-09-05 09:20:54,619 >>   Gradient Accumulation steps = 2
+[INFO|trainer.py:2142] 2024-09-05 09:20:54,619 >>   Total optimization steps = 4,930
+[INFO|trainer.py:2143] 2024-09-05 09:20:54,620 >>   Number of trainable parameters = 107,644,419
+
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 10%|█         | 493/4930 [01:46<14:25,  5.13it/s][INFO|trainer.py:811] 2024-09-05 09:22:40,903 >> The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, ner_tags, id. If tokens, ner_tags, id are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
+[INFO|trainer.py:3819] 2024-09-05 09:22:40,906 >> 
+***** Running Evaluation *****
+[INFO|trainer.py:3821] 2024-09-05 09:22:40,906 >>   Num examples = 6946
+[INFO|trainer.py:3824] 2024-09-05 09:22:40,906 >>   Batch size = 8
+
+
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                                                 [INFO|trainer.py:3503] 2024-09-05 09:22:54,048 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-493
+[INFO|configuration_utils.py:472] 2024-09-05 09:22:54,050 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-493/config.json
+[INFO|modeling_utils.py:2799] 2024-09-05 09:22:54,939 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-493/model.safetensors
+[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:22:54,940 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-493/tokenizer_config.json
+[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:22:54,940 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-493/special_tokens_map.json
+[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:22:56,701 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
+[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:22:56,701 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
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+[INFO|trainer.py:3819] 2024-09-05 09:24:42,834 >> 
+***** Running Evaluation *****
+[INFO|trainer.py:3821] 2024-09-05 09:24:42,834 >>   Num examples = 6946
+[INFO|trainer.py:3824] 2024-09-05 09:24:42,834 >>   Batch size = 8
+{'eval_loss': 0.004987192340195179, 'eval_precision': 0.9288389513108615, 'eval_recall': 0.9245107176141659, 'eval_f1': 0.9266697804764128, 'eval_accuracy': 0.9986744062735468, 'eval_runtime': 13.1382, 'eval_samples_per_second': 528.689, 'eval_steps_per_second': 66.143, 'epoch': 1.0}
+{'loss': 0.018, 'grad_norm': 0.12838158011436462, 'learning_rate': 4.4929006085192696e-05, 'epoch': 1.01}
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                                                 [INFO|trainer.py:3503] 2024-09-05 09:24:56,032 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-986
+[INFO|configuration_utils.py:472] 2024-09-05 09:24:56,033 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-986/config.json
+[INFO|modeling_utils.py:2799] 2024-09-05 09:24:56,916 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-986/model.safetensors
+[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:24:56,917 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-986/tokenizer_config.json
+[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:24:56,917 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-986/special_tokens_map.json
+[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:24:58,668 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
+[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:24:58,668 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
+
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