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bert-base-uncased_finetuning

Text Classification
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NLP702-bert-base-uncased-finetuning

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  1. README.md +82 -0
  2. best/config.json +151 -0
  3. best/model.safetensors +3 -0
  4. best/training_args.bin +3 -0
  5. config.json +151 -0
  6. model.safetensors +3 -0
  7. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: bert-base-uncased
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - massive
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: bert-base-uncased_finetuning
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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: massive
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+ type: massive
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+ config: en-US
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+ split: validation
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+ args: en-US
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.8648285137861466
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+ ---
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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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+
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+ # bert-base-uncased_finetuning
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+
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+ This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the massive dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.5749
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+ - Accuracy: 0.8648
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0001
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+ - train_batch_size: 16
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+ - eval_batch_size: 8
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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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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 15
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 2.1227 | 1.39 | 500 | 0.7218 | 0.8416 |
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+ | 0.4828 | 2.78 | 1000 | 0.5573 | 0.8775 |
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+ | 0.2355 | 4.17 | 1500 | 0.6613 | 0.8697 |
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+ | 0.1334 | 5.56 | 2000 | 0.6672 | 0.8800 |
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+ | 0.0848 | 6.94 | 2500 | 0.7778 | 0.8800 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.35.2
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+ - Pytorch 2.0.1+cu117
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+ - Datasets 2.14.5
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+ - Tokenizers 0.14.0
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