update model card README.md

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- kh_pos
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: khmer-pos-roberta-10
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: kh_pos
+      type: kh_pos
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9511876225757245
+    - name: Recall
+      type: recall
+      value: 0.9526407682234832
+    - name: F1
+      type: f1
+      value: 0.9519136408243376
+    - name: Accuracy
+      type: accuracy
+      value: 0.9735370853522176
+---
+
+<!-- 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. -->
+
+# khmer-pos-roberta-10
+
+This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the kh_pos dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1063
+- Precision: 0.9512
+- Recall: 0.9526
+- F1: 0.9519
+- Accuracy: 0.9735
+
+## 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: 24
+- 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 | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 450  | 0.1347          | 0.9314    | 0.9333 | 0.9324 | 0.9603   |
+| 0.4834        | 2.0   | 900  | 0.1183          | 0.9407    | 0.9377 | 0.9392 | 0.9653   |
+| 0.1323        | 3.0   | 1350 | 0.1026          | 0.9484    | 0.9482 | 0.9483 | 0.9699   |
+| 0.095         | 4.0   | 1800 | 0.0986          | 0.9502    | 0.9490 | 0.9496 | 0.9712   |
+| 0.0774        | 5.0   | 2250 | 0.0978          | 0.9494    | 0.9491 | 0.9493 | 0.9712   |
+| 0.0616        | 6.0   | 2700 | 0.0991          | 0.9493    | 0.9507 | 0.9500 | 0.9715   |
+| 0.0494        | 7.0   | 3150 | 0.0989          | 0.9529    | 0.9540 | 0.9534 | 0.9731   |
+| 0.0414        | 8.0   | 3600 | 0.1037          | 0.9499    | 0.9501 | 0.9500 | 0.9722   |
+| 0.0339        | 9.0   | 4050 | 0.1056          | 0.9516    | 0.9517 | 0.9516 | 0.9734   |
+| 0.029         | 10.0  | 4500 | 0.1063          | 0.9512    | 0.9526 | 0.9519 | 0.9735   |
+
+
+### Framework versions
+
+- Transformers 4.30.2
+- Pytorch 2.0.1+cu118
+- Datasets 2.13.1
+- Tokenizers 0.13.3