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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- glue
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metrics:
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- f1
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- accuracy
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model-index:
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- name: glue_sst_classifier
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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: glue
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type: glue
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args: sst2
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metrics:
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- name: F1
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type: f1
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value: 0.901123595505618
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- name: Accuracy
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type: accuracy
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value: 0.8990825688073395
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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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# glue_sst_classifier
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2371
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- F1: 0.9011
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- Accuracy: 0.8991
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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: 1e-05
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- train_batch_size: 128
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- eval_batch_size: 128
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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_ratio: 0.1
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- num_epochs: 1.0
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
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| 0.3653 | 0.19 | 100 | 0.3213 | 0.8717 | 0.8727 |
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| 0.291 | 0.38 | 200 | 0.2662 | 0.8936 | 0.8911 |
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| 0.2239 | 0.57 | 300 | 0.2417 | 0.9081 | 0.9060 |
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| 0.2306 | 0.76 | 400 | 0.2359 | 0.9105 | 0.9094 |
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| 0.2185 | 0.95 | 500 | 0.2371 | 0.9011 | 0.8991 |
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### Framework versions
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- Transformers 4.18.0
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- Pytorch 1.11.0+cu113
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- Datasets 2.1.0
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- Tokenizers 0.12.1
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