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