Training in progress epoch 0

README.md

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+---
+license: mit
+base_model: indolem/indobert-base-uncased
+tags:
+- generated_from_keras_callback
+model-index:
+- name: aadhistii/indobert-ner-model
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information Keras had access to. You should
+probably proofread and complete it, then remove this comment. -->
+
+# aadhistii/indobert-ner-model
+
+This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Train Loss: 0.5182
+- Validation Loss: 0.2042
+- Train Precision: 0.7770
+- Train Recall: 0.8146
+- Train F1: 0.7954
+- Train Accuracy: 0.9395
+- Epoch: 0
+
+## 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:
+- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2349, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
+- training_precision: float32
+
+### Training results
+
+| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
+|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
+| 0.5182     | 0.2042          | 0.7770          | 0.8146       | 0.7954   | 0.9395         | 0     |
+
+
+### Framework versions
+
+- Transformers 4.41.1
+- TensorFlow 2.15.0
+- Datasets 2.19.1
+- Tokenizers 0.19.1

config.json

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+{
+  "_name_or_path": "indolem/indobert-base-uncased",
+  "architectures": [
+    "BertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_ids": 0,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "B-CRD",
+    "1": "B-DAT",
+    "2": "B-EVT",
+    "3": "B-FAC",
+    "4": "B-GPE",
+    "5": "B-LAN",
+    "6": "B-LAW",
+    "7": "B-LOC",
+    "8": "B-MON",
+    "9": "B-NOR",
+    "10": "B-ORD",
+    "11": "B-ORG",
+    "12": "B-PER",
+    "13": "B-PRC",
+    "14": "B-PRD",
+    "15": "B-QTY",
+    "16": "B-REG",
+    "17": "B-TIM",
+    "18": "B-WOA",
+    "19": "I-CRD",
+    "20": "I-DAT",
+    "21": "I-EVT",
+    "22": "I-FAC",
+    "23": "I-GPE",
+    "24": "I-LAN",
+    "25": "I-LAW",
+    "26": "I-LOC",
+    "27": "I-MON",
+    "28": "I-NOR",
+    "29": "I-ORD",
+    "30": "I-ORG",
+    "31": "I-PER",
+    "32": "I-PRC",
+    "33": "I-PRD",
+    "34": "I-QTY",
+    "35": "I-REG",
+    "36": "I-TIM",
+    "37": "I-WOA",
+    "38": "O"
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+  "intermediate_size": 3072,
+  "label2id": {
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+    "B-TIM": 17,
+    "B-WOA": 18,
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+    "I-EVT": 21,
+    "I-FAC": 22,
+    "I-GPE": 23,
+    "I-LAN": 24,
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+    "I-LOC": 26,
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+    "I-QTY": 34,
+    "I-REG": 35,
+    "I-TIM": 36,
+    "I-WOA": 37,
+    "O": 38
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.41.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 31923
+}

special_tokens_map.json

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+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tf_model.h5

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+size 440263780

tokenizer_config.json

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+{
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+      "normalized": false,
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+  "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]"
+}