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
- eval_samples_per_second: 95.689
- eval_steps_per_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
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- 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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