End of training
Browse files
README.md
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---
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license: mit
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tags:
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- generated_from_trainer
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datasets:
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- clinc_oos
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metrics:
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- accuracy
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model-index:
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- name: roberta-large-finetuned-clinc-3141
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results:
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- task:
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name: Text Classification
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type: text-classification
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dataset:
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name: clinc_oos
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type: clinc_oos
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args: plus
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.9738709677419355
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# roberta-large-finetuned-clinc-3141
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This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the clinc_oos dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1533
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- Accuracy: 0.9739
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 500
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- num_epochs: 3
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 2.6064 | 1.0 | 954 | 0.3041 | 0.9368 |
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| 0.1392 | 2.0 | 1908 | 0.1590 | 0.9723 |
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| 0.044 | 3.0 | 2862 | 0.1533 | 0.9739 |
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### Framework versions
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- Transformers 4.12.3
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- Pytorch 1.9.1
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- Datasets 1.15.1
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- Tokenizers 0.10.3
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eval_results.txt
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epoch = 3.0
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eval_accuracy = 0.9738709677419355
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eval_loss = 0.1532704383134842
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eval_runtime = 11.7541
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eval_samples_per_second = 263.738
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eval_steps_per_second = 16.505
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logs/events.out.tfevents.1649776797.algo-1.36.0
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size 12854
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logs/events.out.tfevents.1649777559.algo-1.36.2
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version https://git-lfs.github.com/spec/v1
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size 363
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