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

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# xlmr-lstm-crf-resume-ner

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: 0.1998
- 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