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# xlmr-lstm-crf-resume-ner4

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/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

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["fjd_dataset"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlmr-lstm-crf-resume-ner4", "results": []}]}
transformers safetensors xlm-roberta token-classification generated_from_trainer dataset:fjd_dataset base_model:xlm-roberta-base license:mit autotrain_compatible endpoints_compatible region:us