Training in progress, epoch 0

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: michiyasunaga/BioLinkBERT-base
+tags:
+- token-classification
+- generated_from_trainer
+datasets:
+- Rodrigo1771/drugtemist-en-fasttext-75-ner
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: output
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: Rodrigo1771/drugtemist-en-fasttext-75-ner
+      type: Rodrigo1771/drugtemist-en-fasttext-75-ner
+      config: DrugTEMIST English NER
+      split: validation
+      args: DrugTEMIST English NER
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9249771271729186
+    - name: Recall
+      type: recall
+      value: 0.9422180801491147
+    - name: F1
+      type: f1
+      value: 0.9335180055401663
+    - name: Accuracy
+      type: accuracy
+      value: 0.998772081600759
+---
+
+<!-- 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-fasttext-75-ner dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0076
+- Precision: 0.9250
+- Recall: 0.9422
+- F1: 0.9335
+- Accuracy: 0.9988
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0183        | 1.0   | 507  | 0.0055          | 0.8974    | 0.9376 | 0.9170 | 0.9985   |
+| 0.0043        | 2.0   | 1014 | 0.0059          | 0.9099    | 0.9320 | 0.9208 | 0.9986   |
+| 0.0022        | 3.0   | 1521 | 0.0057          | 0.9015    | 0.9301 | 0.9156 | 0.9985   |
+| 0.0018        | 4.0   | 2028 | 0.0072          | 0.9275    | 0.9180 | 0.9227 | 0.9986   |
+| 0.0009        | 5.0   | 2535 | 0.0064          | 0.9078    | 0.9357 | 0.9215 | 0.9987   |
+| 0.0007        | 6.0   | 3042 | 0.0064          | 0.9194    | 0.9357 | 0.9275 | 0.9987   |
+| 0.0004        | 7.0   | 3549 | 0.0072          | 0.9289    | 0.9376 | 0.9332 | 0.9988   |
+| 0.0004        | 8.0   | 4056 | 0.0076          | 0.9250    | 0.9422 | 0.9335 | 0.9988   |
+| 0.0003        | 9.0   | 4563 | 0.0077          | 0.9161    | 0.9366 | 0.9263 | 0.9987   |
+| 0.0002        | 10.0  | 5070 | 0.0077          | 0.9195    | 0.9366 | 0.9280 | 0.9988   |
+
+
+### 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": 0.998772081600759,
+    "eval_f1": 0.9335180055401663,
+    "eval_loss": 0.007628325838595629,
+    "eval_precision": 0.9249771271729186,
+    "eval_recall": 0.9422180801491147,
+    "eval_runtime": 15.1819,
+    "eval_samples": 6946,
+    "eval_samples_per_second": 457.519,
+    "eval_steps_per_second": 57.239,
+    "predict_accuracy": 0.9986685364931299,
+    "predict_f1": 0.9202592279515356,
+    "predict_loss": 0.007816320285201073,
+    "predict_precision": 0.8938149972632731,
+    "predict_recall": 0.9483159117305459,
+    "predict_runtime": 28.7456,
+    "predict_samples_per_second": 511.904,
+    "predict_steps_per_second": 64.01,
+    "total_flos": 1.394810359803495e+16,
+    "train_loss": 0.0028968164414402532,
+    "train_runtime": 2196.5741,
+    "train_samples": 32447,
+    "train_samples_per_second": 147.716,
+    "train_steps_per_second": 2.308
+}

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"
+  },
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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": "4.44.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 28895
+}

eval_results.json

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+{
+    "epoch": 10.0,
+    "eval_accuracy": 0.998772081600759,
+    "eval_f1": 0.9335180055401663,
+    "eval_loss": 0.007628325838595629,
+    "eval_precision": 0.9249771271729186,
+    "eval_recall": 0.9422180801491147,
+    "eval_runtime": 15.1819,
+    "eval_samples": 6946,
+    "eval_samples_per_second": 457.519,
+    "eval_steps_per_second": 57.239
+}

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predict_results.json

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+{
+    "predict_accuracy": 0.9986685364931299,
+    "predict_f1": 0.9202592279515356,
+    "predict_loss": 0.007816320285201073,
+    "predict_precision": 0.8938149972632731,
+    "predict_recall": 0.9483159117305459,
+    "predict_runtime": 28.7456,
+    "predict_samples_per_second": 511.904,
+    "predict_steps_per_second": 64.01
+}

special_tokens_map.json

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tokenizer_config.json

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

train.log

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+2024-09-09 13:31:26.574394: 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`.
+2024-09-09 13:31:26.592525: 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
+2024-09-09 13:31:26.613856: 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
+2024-09-09 13:31:26.620385: 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
+2024-09-09 13:31:26.635777: 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.
+2024-09-09 13:31:27.891241: 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(
+09/09/2024 13:31:29 - WARNING - __main__ -   Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
+09/09/2024 13:31:29 - 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},
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+)
+
+
+
+
+
+
+
+[INFO|configuration_utils.py:733] 2024-09-09 13:31:48,080 >> 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] 2024-09-09 13:31:48,083 >> Model config BertConfig {
+  "_name_or_path": "michiyasunaga/BioLinkBERT-base",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
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+  },
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+  },
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+  "transformers_version": "4.44.2",
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+}
+
+[INFO|tokenization_utils_base.py:2269] 2024-09-09 13:31:48,337 >> 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] 2024-09-09 13:31:48,337 >> 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] 2024-09-09 13:31:48,337 >> loading file added_tokens.json from cache at None
+[INFO|tokenization_utils_base.py:2269] 2024-09-09 13:31:48,337 >> 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] 2024-09-09 13:31:48,337 >> 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] 2024-09-09 13:31:48,637 >> 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] 2024-09-09 13:31:48,717 >> 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] 2024-09-09 13:31:48,717 >> 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] 2024-09-09 13:31:55,820 >> 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] 2024-09-09 13:31:56,387 >> ***** Running training *****
+[INFO|trainer.py:2135] 2024-09-09 13:31:56,387 >>   Num examples = 30,812
+[INFO|trainer.py:2136] 2024-09-09 13:31:56,388 >>   Num Epochs = 10
+[INFO|trainer.py:2137] 2024-09-09 13:31:56,388 >>   Instantaneous batch size per device = 32
+[INFO|trainer.py:2140] 2024-09-09 13:31:56,388 >>   Total train batch size (w. parallel, distributed & accumulation) = 64
+[INFO|trainer.py:2141] 2024-09-09 13:31:56,388 >>   Gradient Accumulation steps = 2
+[INFO|trainer.py:2142] 2024-09-09 13:31:56,388 >>   Total optimization steps = 4,810
+[INFO|trainer.py:2143] 2024-09-09 13:31:56,388 >>   Number of trainable parameters = 107,644,419
+
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 10%|█         | 481/4810 [03:05<25:13,  2.86it/s][INFO|trainer.py:811] 2024-09-09 13:35:02,425 >> 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] 2024-09-09 13:35:02,428 >> 
+***** Running Evaluation *****
+[INFO|trainer.py:3821] 2024-09-09 13:35:02,428 >>   Num examples = 6946
+[INFO|trainer.py:3824] 2024-09-09 13:35:02,428 >>   Batch size = 8
+
+
  0%|          | 0/869 [00:00<?, ?it/s]
+
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                                                 [INFO|trainer.py:3503] 2024-09-09 13:35:17,601 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-481
+[INFO|configuration_utils.py:472] 2024-09-09 13:35:17,602 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-481/config.json
+[INFO|modeling_utils.py:2799] 2024-09-09 13:35:18,488 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-481/model.safetensors
+[INFO|tokenization_utils_base.py:2684] 2024-09-09 13:35:18,489 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-481/tokenizer_config.json
+[INFO|tokenization_utils_base.py:2693] 2024-09-09 13:35:18,489 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-481/special_tokens_map.json
+[INFO|tokenization_utils_base.py:2684] 2024-09-09 13:35:24,962 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
+[INFO|tokenization_utils_base.py:2693] 2024-09-09 13:35:24,963 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
+
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train_results.json

@@ -0,0 +1,9 @@
+{
+    "epoch": 10.0,
+    "total_flos": 1.394810359803495e+16,
+    "train_loss": 0.0028968164414402532,
+    "train_runtime": 2196.5741,
+    "train_samples": 32447,
+    "train_samples_per_second": 147.716,
+    "train_steps_per_second": 2.308
+}

trainer_state.json

@@ -0,0 +1,232 @@
+{
+  "best_metric": 0.9335180055401663,
+  "best_model_checkpoint": "/content/dissertation/scripts/ner/output/checkpoint-4056",
+  "epoch": 10.0,
+  "eval_steps": 500,
+  "global_step": 5070,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
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+      "learning_rate": 4.5069033530571994e-05,
+      "loss": 0.0183,
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+    {
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+      "eval_accuracy": 0.9985418469009014,
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