adding new models

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.gitignore

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+checkpoint-*/

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- indonlu
+metrics:
+- accuracy
+model-index:
+- name: bert-base-indonesian-1.5G-finetuned-sentiment-analysis-smsa
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: indonlu
+      type: indonlu
+      args: smsa
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9373015873015873
+language: id
+widget:
+- text: "Saya mengapresiasi usaha anda"
+---
+
+<!-- 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. -->
+
+# bert-base-indonesian-1.5G-finetuned-sentiment-analysis-smsa
+
+This model is a fine-tuned version of [cahya/bert-base-indonesian-1.5G](https://huggingface.co/cahya/bert-base-indonesian-1.5G) on the indonlu dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3390
+- Accuracy: 0.9373
+
+## 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: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.2864        | 1.0   | 688  | 0.2154          | 0.9286   |
+| 0.1648        | 2.0   | 1376 | 0.2238          | 0.9357   |
+| 0.0759        | 3.0   | 2064 | 0.3351          | 0.9365   |
+| 0.044         | 4.0   | 2752 | 0.3390          | 0.9373   |
+| 0.0308        | 5.0   | 3440 | 0.4346          | 0.9365   |
+| 0.0113        | 6.0   | 4128 | 0.4708          | 0.9365   |
+| 0.006         | 7.0   | 4816 | 0.5533          | 0.9325   |
+| 0.0047        | 8.0   | 5504 | 0.5888          | 0.9310   |
+| 0.0001        | 9.0   | 6192 | 0.5961          | 0.9333   |
+| 0.0           | 10.0  | 6880 | 0.5992          | 0.9357   |
+
+
+### Framework versions
+
+- Transformers 4.14.1
+- Pytorch 1.10.0+cu111
+- Datasets 1.16.1
+- Tokenizers 0.10.3

config.json

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+{
+  "_name_or_path": "cahya/bert-base-indonesian-1.5G",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "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": {
+    "Positive": 0,
+    "Neutral": 1,
+    "Negative": 2
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.14.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 32000
+}

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special_tokens_map.json

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tokenizer_config.json

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