Initial Commit

README.md

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+---
+license: mit
+base_model: indolem/indobert-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- indolem_sentiment
+metrics:
+- accuracy
+- f1
+model-index:
+- name: scenario-normal-finetune-clf-data-indolem_sentiment-model-indolem-indobert-base-uncased
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: indolem_sentiment
+      type: indolem_sentiment
+      config: indolem_sentiment_nusantara_text
+      split: validation
+      args: indolem_sentiment_nusantara_text
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.899749373433584
+    - name: F1
+      type: f1
+      value: 0.8181818181818181
+---
+
+<!-- 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. -->
+
+# scenario-normal-finetune-clf-data-indolem_sentiment-model-indolem-indobert-base-uncased
+
+This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the indolem_sentiment dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7320
+- Accuracy: 0.8997
+- F1: 0.8182
+
+## 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: 5e-06
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 30
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
+| No log        | 0.44  | 200  | 0.5218          | 0.7343   | 0.2838 |
+| No log        | 0.88  | 400  | 0.4318          | 0.8070   | 0.7138 |
+| 0.4843        | 1.32  | 600  | 0.4092          | 0.8521   | 0.7281 |
+| 0.4843        | 1.76  | 800  | 0.3515          | 0.8772   | 0.7803 |
+| 0.2912        | 2.2   | 1000 | 0.4582          | 0.8697   | 0.7833 |
+| 0.2912        | 2.64  | 1200 | 0.5148          | 0.8747   | 0.7881 |
+| 0.2912        | 3.08  | 1400 | 0.5736          | 0.8672   | 0.7837 |
+| 0.2526        | 3.52  | 1600 | 0.5119          | 0.8797   | 0.7983 |
+| 0.2526        | 3.96  | 1800 | 0.5242          | 0.8997   | 0.8095 |
+| 0.1974        | 4.4   | 2000 | 0.5311          | 0.8997   | 0.8182 |
+| 0.1974        | 4.84  | 2200 | 0.6478          | 0.8797   | 0.7983 |
+| 0.1974        | 5.27  | 2400 | 0.6219          | 0.8822   | 0.8000 |
+| 0.1526        | 5.71  | 2600 | 0.6591          | 0.8872   | 0.8178 |
+| 0.1526        | 6.15  | 2800 | 0.6483          | 0.8947   | 0.8056 |
+| 0.1159        | 6.59  | 3000 | 0.7075          | 0.8847   | 0.8099 |
+| 0.1159        | 7.03  | 3200 | 0.7157          | 0.8872   | 0.8    |
+| 0.1159        | 7.47  | 3400 | 0.7320          | 0.8997   | 0.8182 |
+
+
+### Framework versions
+
+- Transformers 4.33.3
+- Pytorch 2.0.1
+- Datasets 2.14.5
+- Tokenizers 0.13.3

config.json

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+{
+  "_name_or_path": "indolem/indobert-base-uncased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "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,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "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",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.33.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 31923
+}

pytorch_model.bin

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training_args.bin

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