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Training complete

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README.md ADDED
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+ ---
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+ base_model: vinai/bertweet-base
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: BERTweet
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+ results: []
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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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+ # BERTweet
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+
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+ This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2836
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+ - Accuracy: 0.9070
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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: 5e-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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+ - 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: 1
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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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+ | 0.6594 | 0.0994 | 47 | 0.5265 | 0.7661 |
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+ | 0.4744 | 0.1987 | 94 | 0.5127 | 0.7736 |
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+ | 0.389 | 0.2981 | 141 | 0.4142 | 0.8441 |
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+ | 0.4078 | 0.3975 | 188 | 0.3744 | 0.8778 |
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+ | 0.344 | 0.4968 | 235 | 0.4091 | 0.8516 |
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+ | 0.3149 | 0.5962 | 282 | 0.3440 | 0.8853 |
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+ | 0.3883 | 0.6956 | 329 | 0.3599 | 0.8741 |
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+ | 0.3254 | 0.7949 | 376 | 0.3196 | 0.9055 |
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+ | 0.3261 | 0.8943 | 423 | 0.2935 | 0.9055 |
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+ | 0.3308 | 0.9937 | 470 | 0.2836 | 0.9070 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.40.0
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+ - Pytorch 2.2.1+cu121
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+ - Datasets 2.19.0
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+ - Tokenizers 0.19.1
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+ "RobertaForSequenceClassification"
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+ ],
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+ "hidden_act": "gelu",
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+ "hidden_size": 768,
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+ "initializer_range": 0.02,
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+ "position_embedding_type": "absolute",
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+ "problem_type": "single_label_classification",
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+ "tokenizer_class": "BertweetTokenizer",
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