[Benchmark] Parameter settings of XLM-R on NER tasks
# 🖥 Benchmarking `transformers`

## Benchmark

`XLM-RoBerta` on CoNLL 03 English NER dataset

## Set-up
`model_name_path`: xlm-roberta-base
`max_steps`: 3000 (roughly 6 epochs)
`warmup_steps`: 300
`save_steps`: 200
`learning_rate`: 5e-5
`per_gpu_train_batch_size`: 8
`n_gpu`: 3 (Titan X)

## Results
91.017 (averaged results of five runs)

## Issues
As reported in original [paper](https://arxiv.org/pdf/1911.02116.pdf), XLM-R can achieve 92.25 on this English NER dataset. Since they do not share any parameter settings for fine-tuning, I have to adopt the above ones and the F1 score is about 91.017. Has anybody ever obtained similar results to those in this paper with appropriate settings?

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