xlmr-lstm-crf-resume-ner4
This model is a fine-tuned version of xlm-roberta-base on the fjd_dataset dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.1764
- eval_precision: 0.5811
- eval_recall: 0.5602
- eval_f1: 0.5705
- eval_accuracy: 0.9501
- eval_runtime: 52.6822
- eval_samples_per_second: 94.415
- eval_steps_per_second: 2.961
- epoch: 4.0
- step: 3680
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
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Model tree for hiendang7613/xlmr-lstm-crf-resume-ner4
Base model
FacebookAI/xlm-roberta-base