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--- |
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license: mit |
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base_model: xlm-roberta-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- fjd_dataset |
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model-index: |
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- name: xlmr-lstm-crf-resume-ner4 |
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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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# xlmr-lstm-crf-resume-ner4 |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the fjd_dataset dataset. |
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It achieves the following results on the evaluation set: |
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- eval_loss: 0.1764 |
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- eval_precision: 0.5811 |
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- eval_recall: 0.5602 |
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- eval_f1: 0.5705 |
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- eval_accuracy: 0.9501 |
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- eval_runtime: 52.6822 |
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- eval_samples_per_second: 94.415 |
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- eval_steps_per_second: 2.961 |
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- epoch: 4.0 |
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- step: 3680 |
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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: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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: 100 |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.1 |
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