Training complete

README.md

@@ -0,0 +1,97 @@
+---
+license: mit
+base_model: indolem/indobert-base-uncased
+tags:
+- generated_from_trainer
+datasets:
+- indonlu_nergrit
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: belajarner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: indonlu_nergrit
+      type: indonlu_nergrit
+      config: indonlu_nergrit_source
+      split: validation
+      args: indonlu_nergrit_source
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8400335008375209
+    - name: Recall
+      type: recall
+      value: 0.8631669535283993
+    - name: F1
+      type: f1
+      value: 0.8514431239388794
+    - name: Accuracy
+      type: accuracy
+      value: 0.949652118912081
+---
+
+<!-- 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. -->
+
+# belajarner
+
+This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the indonlu_nergrit dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2914
+- Precision: 0.8400
+- Recall: 0.8632
+- F1: 0.8514
+- Accuracy: 0.9497
+
+## 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: 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: 8
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 209  | 0.2655          | 0.8163    | 0.8718 | 0.8431 | 0.9424   |
+| No log        | 2.0   | 418  | 0.2315          | 0.8146    | 0.8546 | 0.8341 | 0.9486   |
+| 0.04          | 3.0   | 627  | 0.2466          | 0.8291    | 0.8640 | 0.8462 | 0.9470   |
+| 0.04          | 4.0   | 836  | 0.2412          | 0.8322    | 0.8623 | 0.8470 | 0.9503   |
+| 0.03          | 5.0   | 1045 | 0.2636          | 0.8386    | 0.8898 | 0.8635 | 0.9521   |
+| 0.03          | 6.0   | 1254 | 0.2830          | 0.8399    | 0.8623 | 0.8510 | 0.9497   |
+| 0.03          | 7.0   | 1463 | 0.2848          | 0.8376    | 0.8657 | 0.8515 | 0.9500   |
+| 0.013         | 8.0   | 1672 | 0.2914          | 0.8400    | 0.8632 | 0.8514 | 0.9497   |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.2

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "indolem/indobertweet-base-uncased",
+  "_name_or_path": "indolem/indobert-base-uncased",
   "architectures": [
     "BertForTokenClassification"
   ],
@@ -7,7 +7,6 @@
   "bos_token_id": 0,
   "classifier_dropout": null,
   "eos_token_ids": 0,
-  "gradient_checkpointing": false,
   "hidden_act": "gelu",
   "hidden_dropout_prob": 0.1,
   "hidden_size": 768,

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