Training in progress epoch 0
Browse files- README.md +55 -0
- config.json +109 -0
- special_tokens_map.json +7 -0
- tf_model.h5 +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +15 -0
- vocab.txt +0 -0
README.md
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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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<!-- 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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# apwic/indobert-base-uncased-lora-nergrit
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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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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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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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### Training results
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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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### Framework versions
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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
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config.json
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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-ORD": 19,
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"B-ORG": 21,
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"B-PER": 23,
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"B-PRC": 25,
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"B-PRD": 27,
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"B-QTY": 29,
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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-LAW": 12,
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"I-LOC": 14,
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"I-MON": 16,
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"I-NOR": 18,
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"I-ORD": 20,
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"I-ORG": 22,
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"I-PER": 24,
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"I-PRC": 26,
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"I-PRD": 28,
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"I-QTY": 30,
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"I-REG": 32,
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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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}
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special_tokens_map.json
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{
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"cls_token": "[CLS]",
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"mask_token": "[MASK]",
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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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tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:7fc24d5e7ef6756489f0b830ea286d1f9b82b45c5edd13ebe620853cdb1f4fba
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size 440263780
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tokenizer.json
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tokenizer_config.json
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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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"mask_token": "[MASK]",
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"model_max_length": 1000000000000000019884624838656,
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"never_split": null,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"strip_accents": null,
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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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}
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vocab.txt
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