Training in progress, epoch 1

README.md

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+---
+license: apache-2.0
+base_model: michiyasunaga/BioLinkBERT-base
+tags:
+- token-classification
+- generated_from_trainer
+datasets:
+- Rodrigo1771/drugtemist-en-ner
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: output
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: Rodrigo1771/drugtemist-en-ner
+      type: Rodrigo1771/drugtemist-en-ner
+      config: DrugTEMIST English NER
+      split: validation
+      args: DrugTEMIST English NER
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9327102803738317
+    - name: Recall
+      type: recall
+      value: 0.9301025163094129
+    - name: F1
+      type: f1
+      value: 0.9314045730284647
+    - name: Accuracy
+      type: accuracy
+      value: 0.9986953367008066
+---
+
+<!-- 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-ner dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0056
+- Precision: 0.9327
+- Recall: 0.9301
+- F1: 0.9314
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 434  | 0.0057          | 0.8938    | 0.8938 | 0.8938 | 0.9981   |
+| 0.0182        | 2.0   | 868  | 0.0044          | 0.9024    | 0.9301 | 0.9160 | 0.9985   |
+| 0.0039        | 3.0   | 1302 | 0.0045          | 0.9129    | 0.9282 | 0.9205 | 0.9987   |
+| 0.0024        | 4.0   | 1736 | 0.0051          | 0.8821    | 0.9348 | 0.9077 | 0.9983   |
+| 0.0017        | 5.0   | 2170 | 0.0057          | 0.9251    | 0.9320 | 0.9285 | 0.9986   |
+| 0.0012        | 6.0   | 2604 | 0.0061          | 0.9001    | 0.9236 | 0.9117 | 0.9984   |
+| 0.0009        | 7.0   | 3038 | 0.0056          | 0.9327    | 0.9301 | 0.9314 | 0.9987   |
+| 0.0009        | 8.0   | 3472 | 0.0068          | 0.9118    | 0.9348 | 0.9231 | 0.9986   |
+| 0.0006        | 9.0   | 3906 | 0.0072          | 0.9267    | 0.9310 | 0.9289 | 0.9987   |
+| 0.0004        | 10.0  | 4340 | 0.0073          | 0.9192    | 0.9329 | 0.9260 | 0.9986   |
+
+
+### Framework versions
+
+- Transformers 4.42.4
+- Pytorch 2.4.0+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

all_results.json

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+{
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+    "eval_accuracy": 0.9986953367008066,
+    "eval_f1": 0.9314045730284647,
+    "eval_loss": 0.005624314770102501,
+    "eval_precision": 0.9327102803738317,
+    "eval_recall": 0.9301025163094129,
+    "eval_runtime": 13.3976,
+    "eval_samples": 6946,
+    "eval_samples_per_second": 518.45,
+    "eval_steps_per_second": 64.862,
+    "predict_accuracy": 0.9986842934577083,
+    "predict_f1": 0.9213546039742514,
+    "predict_loss": 0.005766334943473339,
+    "predict_precision": 0.8892490545651,
+    "predict_recall": 0.9558652729384437,
+    "predict_runtime": 26.2719,
+    "predict_samples_per_second": 560.104,
+    "predict_steps_per_second": 70.037,
+    "total_flos": 1.0996932656642544e+16,
+    "train_loss": 0.003382195293697344,
+    "train_runtime": 1039.0596,
+    "train_samples": 27768,
+    "train_samples_per_second": 267.242,
+    "train_steps_per_second": 4.177
+}

config.json

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+{
+  "_name_or_path": "IVN-RIN/bioBIT",
+  "architectures": [
+    "BertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "finetuning_task": "ner",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "O",
+    "1": "B-FARMACO",
+    "2": "I-FARMACO"
+  },
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+  "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": "4.42.4",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 31102
+}

eval_results.json

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+{
+    "epoch": 10.0,
+    "eval_accuracy": 0.9986953367008066,
+    "eval_f1": 0.9314045730284647,
+    "eval_loss": 0.005624314770102501,
+    "eval_precision": 0.9327102803738317,
+    "eval_recall": 0.9301025163094129,
+    "eval_runtime": 13.3976,
+    "eval_samples": 6946,
+    "eval_samples_per_second": 518.45,
+    "eval_steps_per_second": 64.862
+}

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predict_results.json

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+    "predict_accuracy": 0.9986842934577083,
+    "predict_f1": 0.9213546039742514,
+    "predict_loss": 0.005766334943473339,
+    "predict_precision": 0.8892490545651,
+    "predict_recall": 0.9558652729384437,
+    "predict_runtime": 26.2719,
+    "predict_samples_per_second": 560.104,
+    "predict_steps_per_second": 70.037
+}

special_tokens_map.json

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tokenizer_config.json

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+  "tokenizer_class": "BertTokenizer",
+  "truncation": true,
+  "unk_token": "[UNK]"
+}

train.log

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+2024-08-30 21:54:12.390238: 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`.
+2024-08-30 21:54:12.408272: 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
+2024-08-30 21:54:12.429605: 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
+2024-08-30 21:54:12.436048: 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
+2024-08-30 21:54:12.451309: 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.
+2024-08-30 21:54:13.743493: 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:1494: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead
+  warnings.warn(
+08/30/2024 21:54:15 - WARNING - __main__ -   Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
+08/30/2024 21:54:15 - 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,
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+ddp_timeout=1800,
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+)
+
+
+
+
+
+
+
+[INFO|configuration_utils.py:733] 2024-08-30 21:54:27,962 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--IVN-RIN--bioBIT/snapshots/83755ed79ee254c11854e9f54a53679557271018/config.json
+[INFO|configuration_utils.py:800] 2024-08-30 21:54:27,966 >> Model config BertConfig {
+  "_name_or_path": "IVN-RIN/bioBIT",
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "finetuning_task": "ner",
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+    "2": "I-FARMACO"
+  },
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+    "I-FARMACO": 2,
+    "O": 0
+  },
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+  "vocab_size": 31102
+}
+
+[INFO|tokenization_utils_base.py:2161] 2024-08-30 21:54:29,333 >> loading file vocab.txt from cache at /root/.cache/huggingface/hub/models--IVN-RIN--bioBIT/snapshots/83755ed79ee254c11854e9f54a53679557271018/vocab.txt
+[INFO|tokenization_utils_base.py:2161] 2024-08-30 21:54:29,334 >> loading file tokenizer.json from cache at /root/.cache/huggingface/hub/models--IVN-RIN--bioBIT/snapshots/83755ed79ee254c11854e9f54a53679557271018/tokenizer.json
+[INFO|tokenization_utils_base.py:2161] 2024-08-30 21:54:29,334 >> loading file added_tokens.json from cache at None
+[INFO|tokenization_utils_base.py:2161] 2024-08-30 21:54:29,334 >> loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--IVN-RIN--bioBIT/snapshots/83755ed79ee254c11854e9f54a53679557271018/special_tokens_map.json
+[INFO|tokenization_utils_base.py:2161] 2024-08-30 21:54:29,334 >> loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--IVN-RIN--bioBIT/snapshots/83755ed79ee254c11854e9f54a53679557271018/tokenizer_config.json
+[INFO|modeling_utils.py:3556] 2024-08-30 21:54:40,888 >> loading weights file model.safetensors from cache at /root/.cache/huggingface/hub/models--IVN-RIN--bioBIT/snapshots/83755ed79ee254c11854e9f54a53679557271018/model.safetensors
+[INFO|modeling_utils.py:4354] 2024-08-30 21:54:40,995 >> Some weights of the model checkpoint at IVN-RIN/bioBIT were not used when initializing BertForTokenClassification: ['cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.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:4366] 2024-08-30 21:54:40,995 >> Some weights of BertForTokenClassification were not initialized from the model checkpoint at IVN-RIN/bioBIT 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:805] 2024-08-30 21:54:47,484 >> 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:2128] 2024-08-30 21:54:48,041 >> ***** Running training *****
+[INFO|trainer.py:2129] 2024-08-30 21:54:48,041 >>   Num examples = 27,198
+[INFO|trainer.py:2130] 2024-08-30 21:54:48,041 >>   Num Epochs = 10
+[INFO|trainer.py:2131] 2024-08-30 21:54:48,041 >>   Instantaneous batch size per device = 32
+[INFO|trainer.py:2134] 2024-08-30 21:54:48,041 >>   Total train batch size (w. parallel, distributed & accumulation) = 64
+[INFO|trainer.py:2135] 2024-08-30 21:54:48,041 >>   Gradient Accumulation steps = 2
+[INFO|trainer.py:2136] 2024-08-30 21:54:48,041 >>   Total optimization steps = 4,250
+[INFO|trainer.py:2137] 2024-08-30 21:54:48,042 >>   Number of trainable parameters = 109,339,395
+
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 10%|█         | 425/4250 [01:48<14:18,  4.46it/s][INFO|trainer.py:805] 2024-08-30 21:56:36,656 >> 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:3788] 2024-08-30 21:56:36,658 >> 
+***** Running Evaluation *****
+[INFO|trainer.py:3790] 2024-08-30 21:56:36,658 >>   Num examples = 6798
+[INFO|trainer.py:3793] 2024-08-30 21:56:36,658 >>   Batch size = 8
+
+
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                                                 [INFO|trainer.py:3478] 2024-08-30 21:56:50,913 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-425
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