End of training

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- common_language
+metrics:
+- accuracy
+model-index:
+- name: language-detection-fine-tuned-on-xlm-roberta-base
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: common_language
+      type: common_language
+      args: full
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9738386718094919
+---
+
+<!-- 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. -->
+
+# language-detection-fine-tuned-on-xlm-roberta-base
+
+This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the common_language dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1886
+- Accuracy: 0.9738
+
+## 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: 3e-05
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 1
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|
+| 0.1           | 1.0   | 22194 | 0.1886          | 0.9738   |
+
+
+### Framework versions
+
+- Transformers 4.12.5
+- Pytorch 1.10.0+cu111
+- Datasets 1.15.1
+- Tokenizers 0.10.3

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