Instructions to use NbAiLabArchive/test_NCC_OSCAR_style with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabArchive/test_NCC_OSCAR_style with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NbAiLabArchive/test_NCC_OSCAR_style")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NbAiLabArchive/test_NCC_OSCAR_style") model = AutoModelForMaskedLM.from_pretrained("NbAiLabArchive/test_NCC_OSCAR_style", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| ./run_mlm_flax_stream.py \ | |
| --output_dir="./" \ | |
| --model_type="roberta" \ | |
| --config_name="./" \ | |
| --tokenizer_name="./" \ | |
| --dataset_name="NbAiLab/NCC_small" \ | |
| --max_seq_length="128" \ | |
| --weight_decay="0.01" \ | |
| --per_device_train_batch_size="232" \ | |
| --per_device_eval_batch_size="232" \ | |
| --learning_rate="6e-4" \ | |
| --warmup_steps="10000" \ | |
| --overwrite_output_dir \ | |
| --num_train_epochs="10" \ | |
| --adam_beta1="0.9" \ | |
| --adam_beta2="0.98" \ | |
| --logging_steps="10000" \ | |
| --save_steps="50000" \ | |
| --eval_steps="50000" \ | |
| --preprocessing_num_workers 96 \ | |
| --adafactor \ | |
| --push_to_hub | |