End of training

README.md

@@ -3,8 +3,6 @@ license: mit
 base_model: indolem/indobert-base-uncased
 tags:
 - generated_from_trainer
-datasets:
-- id_nergrit_corpus
 metrics:
 - precision
 - recall
@@ -12,29 +10,7 @@ metrics:
 - accuracy
 model-index:
 - name: indobert-model-ner
- results:
- - task:
- name: Token Classification
- type: token-classification
- dataset:
- name: id_nergrit_corpus
- type: id_nergrit_corpus
- config: ner
- split: test
- args: ner
- metrics:
- - name: Precision
- type: precision
- value: 0.8159014069146036
- - name: Recall
- type: recall
- value: 0.8415073115860517
- - name: F1
- type: f1
- value: 0.8285065618251288
- - name: Accuracy
- type: accuracy
- value: 0.9514472889467082
+ results: []
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -42,13 +18,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # indobert-model-ner
 
-This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the id_nergrit_corpus dataset.
+This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1728
-- Precision: 0.8159
-- Recall: 0.8415
-- F1: 0.8285
-- Accuracy: 0.9514
+- Loss: 0.2296
+- Precision: 0.8307
+- Recall: 0.8454
+- F1: 0.8380
+- Accuracy: 0.9530
 
 ## Model description
 
@@ -73,20 +49,23 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 3
+- num_epochs: 10
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.4948 | 1.0 | 784 | 0.1790 | 0.7868 | 0.8300 | 0.8078 | 0.9461 |
-| 0.1576 | 2.0 | 1568 | 0.1663 | 0.8151 | 0.8283 | 0.8216 | 0.9512 |
-| 0.1237 | 3.0 | 2352 | 0.1728 | 0.8159 | 0.8415 | 0.8285 | 0.9514 |
+| 0.4855 | 1.0 | 784 | 0.1729 | 0.8069 | 0.8389 | 0.8226 | 0.9499 |
+| 0.1513 | 2.0 | 1568 | 0.1781 | 0.8086 | 0.8371 | 0.8226 | 0.9497 |
+| 0.1106 | 3.0 | 2352 | 0.1798 | 0.8231 | 0.8475 | 0.8351 | 0.9531 |
+| 0.0784 | 4.0 | 3136 | 0.1941 | 0.8270 | 0.8442 | 0.8355 | 0.9535 |
+| 0.0636 | 5.0 | 3920 | 0.2085 | 0.8269 | 0.8514 | 0.8389 | 0.9548 |
+| 0.0451 | 6.0 | 4704 | 0.2296 | 0.8307 | 0.8454 | 0.8380 | 0.9530 |
 
 
 ### Framework versions
 
-- Transformers 4.33.2
-- Pytorch 2.0.1+cu118
-- Datasets 2.14.5
-- Tokenizers 0.13.3
+- Transformers 4.38.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

model.safetensors

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 size 440013780

runs/Mar15_05-08-33_81250d50cff2/events.out.tfevents.1710479317.81250d50cff2.928.0

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