End of training

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- clinc_oos
+metrics:
+- accuracy
+model-index:
+- name: roberta-large-finetuned-clinc
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    dataset:
+      name: clinc_oos
+      type: clinc_oos
+      args: plus
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9703225806451613
+---
+
+<!-- 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. -->
+
+# roberta-large-finetuned-clinc
+
+This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the clinc_oos dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2109
+- Accuracy: 0.9703
+
+## 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: 128
+- eval_batch_size: 128
+- 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: 5
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 5.0643        | 1.0   | 120  | 5.0440          | 0.0065   |
+| 4.2726        | 2.0   | 240  | 2.7488          | 0.7255   |
+| 1.9687        | 3.0   | 360  | 0.8694          | 0.9174   |
+| 0.5773        | 4.0   | 480  | 0.3267          | 0.9539   |
+| 0.1842        | 5.0   | 600  | 0.2109          | 0.9703   |
+
+
+### Framework versions
+
+- Transformers 4.19.0.dev0
+- Pytorch 1.10.2+cu113
+- Datasets 1.18.4
+- Tokenizers 0.11.6

eval_results.txt

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+epoch = 5.0
+eval_accuracy = 0.9703225806451613
+eval_loss = 0.21087072789669037
+eval_runtime = 13.7362
+eval_samples_per_second = 225.681
+eval_steps_per_second = 1.82

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