End of training

README.md

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+---
+license: mit
+base_model: indolem/indobert-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- id_nergrit_corpus
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: KIPBERT
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: id_nergrit_corpus
+      type: id_nergrit_corpus
+      config: ner
+      split: test
+      args: ner
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8057702776265651
+    - name: Recall
+      type: recall
+      value: 0.8325084364454444
+    - name: F1
+      type: f1
+      value: 0.8189211618257262
+    - name: Accuracy
+      type: accuracy
+      value: 0.9503167154516874
+---
+
+<!-- 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. -->
+
+# KIPBERT
+
+This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the id_nergrit_corpus dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1731
+- Precision: 0.8058
+- Recall: 0.8325
+- F1: 0.8189
+- Accuracy: 0.9503
+
+## 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: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 2
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.4926        | 1.0   | 784  | 0.1810          | 0.7860    | 0.8172 | 0.8013 | 0.9450   |
+| 0.1627        | 2.0   | 1568 | 0.1731          | 0.8058    | 0.8325 | 0.8189 | 0.9503   |
+
+
+### Framework versions
+
+- Transformers 4.33.2
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.5
+- Tokenizers 0.13.3

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"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "B-CRD": 0,
+    "B-DAT": 1,
+    "B-EVT": 2,
+    "B-FAC": 3,
+    "B-GPE": 4,
+    "B-LAN": 5,
+    "B-LAW": 6,
+    "B-LOC": 7,
+    "B-MON": 8,
+    "B-NOR": 9,
+    "B-ORD": 10,
+    "B-ORG": 11,
+    "B-PER": 12,
+    "B-PRC": 13,
+    "B-PRD": 14,
+    "B-QTY": 15,
+    "B-REG": 16,
+    "B-TIM": 17,
+    "B-WOA": 18,
+    "I-CRD": 19,
+    "I-DAT": 20,
+    "I-EVT": 21,
+    "I-FAC": 22,
+    "I-GPE": 23,
+    "I-LAN": 24,
+    "I-LAW": 25,
+    "I-LOC": 26,
+    "I-MON": 27,
+    "I-NOR": 28,
+    "I-ORD": 29,
+    "I-ORG": 30,
+    "I-PER": 31,
+    "I-PRC": 32,
+    "I-PRD": 33,
+    "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",
+  "torch_dtype": "float32",
+  "transformers_version": "4.33.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 31923
+}

pytorch_model.bin

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special_tokens_map.json

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

tokenizer_config.json

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+{
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "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]"
+}

training_args.bin

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