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Training in progress epoch 0

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  1. README.md +55 -0
  2. config.json +109 -0
  3. special_tokens_map.json +7 -0
  4. tf_model.h5 +3 -0
  5. tokenizer.json +0 -0
  6. tokenizer_config.json +15 -0
  7. vocab.txt +0 -0
README.md ADDED
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+ ---
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+ license: mit
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+ base_model: indolem/indobert-base-uncased
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+ tags:
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+ - generated_from_keras_callback
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+ model-index:
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+ - name: apwic/indobert-base-uncased-lora-nergrit
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information Keras had access to. You should
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+ probably proofread and complete it, then remove this comment. -->
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+
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+ # apwic/indobert-base-uncased-lora-nergrit
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+
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+ This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Train Loss: 0.4709
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+ - Validation Loss: 0.2069
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+ - Train Accuracy: 0.9399
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+ - Epoch: 0
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2352, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
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+ - training_precision: float32
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+
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+ ### Training results
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+
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+ | Train Loss | Validation Loss | Train Accuracy | Epoch |
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+ |:----------:|:---------------:|:--------------:|:-----:|
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+ | 0.4709 | 0.2069 | 0.9399 | 0 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.33.0
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+ - TensorFlow 2.12.0
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+ - Datasets 2.14.6
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+ - Tokenizers 0.13.3
config.json ADDED
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+ {
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+ "_name_or_path": "indolem/indobert-base-uncased",
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+ "architectures": [
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+ "BertForTokenClassification"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "bos_token_id": 0,
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+ "classifier_dropout": null,
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+ "eos_token_ids": 0,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 768,
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+ "id2label": {
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+ "0": "O",
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+ "1": "B-CRD",
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+ "2": "I-CRD",
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+ "3": "B-DAT",
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+ "4": "I-DAT",
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+ "5": "B-EVT",
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+ "6": "I-EVT",
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+ "7": "B-FAC",
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+ "8": "I-FAC",
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+ "9": "B-GPE",
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+ "10": "I-GPE",
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+ "11": "B-LAW",
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+ "12": "I-LAW",
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+ "13": "B-LOC",
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+ "14": "I-LOC",
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+ "15": "B-MON",
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+ "16": "I-MON",
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+ "17": "B-NOR",
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+ "18": "I-NOR",
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+ "19": "B-ORD",
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+ "20": "I-ORD",
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+ "21": "B-ORG",
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+ "22": "I-ORG",
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+ "23": "B-PER",
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+ "24": "I-PER",
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+ "25": "B-PRC",
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+ "26": "I-PRC",
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+ "27": "B-PRD",
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+ "28": "I-PRD",
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+ "29": "B-QTY",
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+ "30": "I-QTY",
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+ "31": "B-REG",
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+ "32": "I-REG",
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+ "33": "B-TIM",
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+ "34": "I-TIM",
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+ "35": "B-WOA",
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+ "36": "I-WOA",
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+ "37": "B-LAN",
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+ "38": "I-LAN"
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+ },
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+ "initializer_range": 0.02,
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+ "intermediate_size": 3072,
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+ "label2id": {
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+ "B-CRD": 1,
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+ "B-DAT": 3,
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+ "B-EVT": 5,
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+ "B-FAC": 7,
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+ "B-GPE": 9,
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+ "B-LAN": 37,
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+ "B-LAW": 11,
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+ "B-LOC": 13,
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+ "B-MON": 15,
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+ "B-NOR": 17,
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+ "B-REG": 31,
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+ "B-TIM": 33,
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+ "B-WOA": 35,
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+ "I-CRD": 2,
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+ "I-DAT": 4,
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+ "I-EVT": 6,
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+ "I-FAC": 8,
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+ "I-GPE": 10,
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+ "I-LAN": 38,
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+ "I-TIM": 34,
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+ "I-WOA": 36,
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+ "O": 0
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+ },
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+ "layer_norm_eps": 1e-12,
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+ "max_position_embeddings": 512,
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+ "model_type": "bert",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "output_past": true,
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+ "pad_token_id": 0,
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+ "position_embedding_type": "absolute",
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+ "transformers_version": "4.33.0",
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+ "type_vocab_size": 2,
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+ "use_cache": true,
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+ "vocab_size": 31923
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+ }
special_tokens_map.json ADDED
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+ {
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+ "pad_token": "[PAD]",
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+ "sep_token": "[SEP]",
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+ "unk_token": "[UNK]"
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+ }
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+ size 440263780
tokenizer.json ADDED
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tokenizer_config.json ADDED
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+ {
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+ "clean_up_tokenization_spaces": true,
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+ "cls_token": "[CLS]",
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+ "do_basic_tokenize": true,
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+ "do_lower_case": true,
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+ "pad_token": "[PAD]",
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+ "tokenize_chinese_chars": true,
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+ "tokenizer_class": "BertTokenizer",
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+ "unk_token": "[UNK]"
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+ }
vocab.txt ADDED
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