update model card README.md

README.md

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+---
+language:
+- mn
+license: mit
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: xlm-roberta-large-ner-demo
+  results: []
+---
+
+<!-- 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. -->
+
+# xlm-roberta-large-ner-demo
+
+This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1273
+- Precision: 0.8961
+- Recall: 0.9143
+- F1: 0.9051
+- Accuracy: 0.9775
+
+## 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: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 7
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.4849        | 1.0   | 64   | 0.1678          | 0.7415    | 0.7950 | 0.7673 | 0.9511   |
+| 0.1432        | 2.0   | 128  | 0.1370          | 0.8276    | 0.8591 | 0.8430 | 0.9667   |
+| 0.096         | 3.0   | 192  | 0.1122          | 0.8096    | 0.8593 | 0.8337 | 0.9685   |
+| 0.0607        | 4.0   | 256  | 0.1246          | 0.8550    | 0.8829 | 0.8687 | 0.9725   |
+| 0.0363        | 5.0   | 320  | 0.1153          | 0.8878    | 0.9089 | 0.8982 | 0.9768   |
+| 0.0228        | 6.0   | 384  | 0.1229          | 0.8974    | 0.9148 | 0.9060 | 0.9775   |
+| 0.0147        | 7.0   | 448  | 0.1273          | 0.8961    | 0.9143 | 0.9051 | 0.9775   |
+
+
+### Framework versions
+
+- Transformers 4.28.1
+- Pytorch 2.0.0+cu118
+- Datasets 2.12.0
+- Tokenizers 0.13.3