hiendang7613/xlmr-lstm-crf-resume-ner

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xlmr-lstm-crf-resume-ner

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: 0.1998
• —eval_precision: 0.5659
• —eval_recall: 0.6020
• —eval_f1: 0.5834
• —eval_accuracy: 0.9475
• —eval_runtime: 51.9811
• —evalsamplesper_second: 95.689
• —evalstepsper_second: 1.501
• —epoch: 40.0
• —step: 18400

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
• —trainbatchsize: 64
• —evalbatchsize: 64
• —seed: 42
• —gradientaccumulationsteps: 4
• —totaltrainbatch_size: 256
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —num_epochs: 100

Framework versions

• —Transformers 4.35.2
• —Pytorch 2.1.0+cu121
• —Datasets 2.17.0
• —Tokenizers 0.15.1