End of training

README.md

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+---
+license: mit
+base_model: indobenchmark/indobert-base-p1
+tags:
+- generated_from_trainer
+datasets:
+- indonlu
+metrics:
+- accuracy
+model-index:
+- name: IndoBERT-Sentiment-Analysis
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: indonlu
+      type: indonlu
+      config: smsa
+      split: validation
+      args: smsa
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9452380952380952
+---
+
+<!-- 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. -->
+
+# IndoBERT-Sentiment-Analysis
+
+This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on the indonlu dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4221
+- Accuracy: 0.9452
+- F1 Score: 0.9451
+
+## 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: 6
+- eval_batch_size: 6
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
+| 0.3499        | 0.27  | 500  | 0.2392          | 0.9310   | 0.9311   |
+| 0.3181        | 0.55  | 1000 | 0.3354          | 0.9175   | 0.9158   |
+| 0.3001        | 0.82  | 1500 | 0.2965          | 0.9238   | 0.9243   |
+| 0.2534        | 1.09  | 2000 | 0.3513          | 0.9222   | 0.9218   |
+| 0.1692        | 1.36  | 2500 | 0.2657          | 0.9405   | 0.9399   |
+| 0.1543        | 1.64  | 3000 | 0.4046          | 0.9198   | 0.9191   |
+| 0.1827        | 1.91  | 3500 | 0.2800          | 0.9317   | 0.9319   |
+| 0.1061        | 2.18  | 4000 | 0.3352          | 0.9389   | 0.9389   |
+| 0.0639        | 2.45  | 4500 | 0.4033          | 0.9373   | 0.9365   |
+| 0.0709        | 2.73  | 5000 | 0.3508          | 0.9365   | 0.9360   |
+| 0.0922        | 3.0   | 5500 | 0.3313          | 0.9397   | 0.9394   |
+| 0.0274        | 3.27  | 6000 | 0.3635          | 0.9444   | 0.9440   |
+| 0.0273        | 3.54  | 6500 | 0.4074          | 0.9389   | 0.9387   |
+| 0.0414        | 3.82  | 7000 | 0.3863          | 0.9405   | 0.9405   |
+| 0.0156        | 4.09  | 7500 | 0.4128          | 0.9413   | 0.9412   |
+| 0.0067        | 4.36  | 8000 | 0.4469          | 0.9397   | 0.9399   |
+| 0.0056        | 4.63  | 8500 | 0.4297          | 0.9444   | 0.9445   |
+| 0.0124        | 4.91  | 9000 | 0.4227          | 0.9452   | 0.9451   |
+
+
+### Framework versions
+
+- Transformers 4.39.0.dev0
+- Pytorch 2.1.0.dev20230729
+- Datasets 2.14.0
+- Tokenizers 0.15.2

config.json

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+{
+  "_name_or_path": "indobenchmark/indobert-base-p1",
+  "_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": 768,
+  "id2label": {
+    "0": "POSITIVE",
+    "1": "NEUTRAL",
+    "2": "NEGATIVE"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "NEGATIVE": 2,
+    "NEUTRAL": 1,
+    "POSITIVE": 0
+  },
+  "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,
+  "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": "4.39.0.dev0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 50000
+}

model.safetensors

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+oid sha256:33465b0d72a94ba459b1dd79a7979e277ec453698ff6f18c1c28c235e59cc0c5
+size 497798148

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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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "3": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
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+      "special": true
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+    "4": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "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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