Model save
Browse files- README.md +81 -0
- pytorch_model.bin +1 -1
README.md
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---
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license: mit
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base_model: microsoft/deberta-v3-large
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tags:
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- generated_from_trainer
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datasets:
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- boolq
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metrics:
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- accuracy
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model-index:
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- name: deberta-v3-large_boolq
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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: boolq
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type: boolq
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config: default
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split: validation
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args: default
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.882262996941896
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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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# deberta-v3-large_boolq
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This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the boolq dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5796
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- Accuracy: 0.8823
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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: 16
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- eval_batch_size: 64
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 32
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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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- num_epochs: 5.0
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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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| No log | 0.85 | 250 | 0.3265 | 0.8740 |
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| 0.3786 | 1.69 | 500 | 0.3212 | 0.8844 |
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| 0.3786 | 2.54 | 750 | 0.4205 | 0.8838 |
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| 0.1324 | 3.39 | 1000 | 0.5393 | 0.8832 |
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| 0.1324 | 4.24 | 1250 | 0.5796 | 0.8823 |
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### Framework versions
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- Transformers 4.32.1
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- Pytorch 2.0.1+cu117
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- Datasets 2.14.4
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- Tokenizers 0.13.3
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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size 1740391797
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version https://git-lfs.github.com/spec/v1
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size 1740391797
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