Model save
Browse files- README.md +57 -0
- model.safetensors +1 -1
- tokenizer.json +0 -0
- tokenizer_config.json +16 -0
- training_args.bin +1 -1
README.md
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---
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library_name: transformers
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base_model: UBC-NLP/MARBERTv2
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tags:
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- generated_from_trainer
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model-index:
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- name: MASRIBERTv3
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results: []
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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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# MASRIBERTv3
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This model is a fine-tuned version of [UBC-NLP/MARBERTv2](https://huggingface.co/UBC-NLP/MARBERTv2) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- eval_loss: 11.5203
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- eval_runtime: 221.3683
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- eval_samples_per_second: 77.708
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- eval_steps_per_second: 1.215
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- step: 0
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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: 1.6175586289837642e-05
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- train_batch_size: 32
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- eval_batch_size: 64
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- seed: 42
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 256
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine
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- num_epochs: 4
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- mixed_precision_training: Native AMP
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### Framework versions
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- Transformers 5.0.0
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- Pytorch 2.10.0+cu128
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- Datasets 4.8.3
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- Tokenizers 0.22.2
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model.safetensors
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size 775678032
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tokenizer.json
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tokenizer_config.json
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{
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"backend": "tokenizers",
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"cls_token": "[CLS]",
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"do_basic_tokenize": true,
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"do_lower_case": true,
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"is_local": false,
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"mask_token": "[MASK]",
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"model_max_length": 1000000000000000019884624838656,
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"never_split": null,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"unk_token": "[UNK]"
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}
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training_args.bin
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size 5201
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