End of training

README.md

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+---
+license: mit
+base_model: indobenchmark/indobert-large-p2
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+model-index:
+- name: indonli-indobert-large
+  results: []
+---
+
+<!-- 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. -->
+
+# indonli-indobert-large
+
+This model is a fine-tuned version of [indobenchmark/indobert-large-p2](https://huggingface.co/indobenchmark/indobert-large-p2) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.9753
+- Accuracy: 0.6350
+- Precision: 0.6350
+- Recall: 0.6350
+- F1 Score: 0.6362
+
+## 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: 3e-06
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 101
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 6
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1 Score |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:--------:|
+| 1.0324        | 1.0   | 2583  | 0.9492          | 0.5508   | 0.5508    | 0.5508 | 0.5172   |
+| 0.9234        | 2.0   | 5166  | 0.8837          | 0.6099   | 0.6099    | 0.6099 | 0.6106   |
+| 0.8318        | 3.0   | 7749  | 0.8718          | 0.6277   | 0.6277    | 0.6277 | 0.6302   |
+| 0.7417        | 4.0   | 10332 | 0.9005          | 0.6313   | 0.6313    | 0.6313 | 0.6326   |
+| 0.6788        | 5.0   | 12915 | 0.9380          | 0.6368   | 0.6368    | 0.6368 | 0.6381   |
+| 0.6263        | 6.0   | 15498 | 0.9753          | 0.6350   | 0.6350    | 0.6350 | 0.6362   |
+
+
+### Framework versions
+
+- Transformers 4.33.0
+- Pytorch 2.0.0
+- Datasets 2.1.0
+- Tokenizers 0.13.3

config.json

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+{
+  "_name_or_path": "indobenchmark/indobert-large-p2",
+  "_num_labels": 5,
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.33.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

pytorch_model.bin

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training_args.bin

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