metadata

license: mit
base_model: xlm-roberta-base
tags:
  - generated_from_trainer
datasets:
  - fjd_dataset
model-index:
  - name: xlmr-lstm-crf-resume-ner4
    results: []

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