Instructions to use NbAiLabArchive/test_NCC_small_pytorch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabArchive/test_NCC_small_pytorch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NbAiLabArchive/test_NCC_small_pytorch")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NbAiLabArchive/test_NCC_small_pytorch") model = AutoModelForMaskedLM.from_pretrained("NbAiLabArchive/test_NCC_small_pytorch") - Notebooks
- Google Colab
- Kaggle
fix
Browse files- run_flax.sh +1 -1
run_flax.sh
CHANGED
|
@@ -11,7 +11,7 @@
|
|
| 11 |
--learning_rate="6e-4" \
|
| 12 |
--warmup_steps="10000" \
|
| 13 |
--overwrite_output_dir \
|
| 14 |
-
--num_train_epochs
|
| 15 |
--adam_beta1="0.9" \
|
| 16 |
--adam_beta2="0.98" \
|
| 17 |
--logging_steps="10000" \
|
|
|
|
| 11 |
--learning_rate="6e-4" \
|
| 12 |
--warmup_steps="10000" \
|
| 13 |
--overwrite_output_dir \
|
| 14 |
+
--num_train_epochs="10" \
|
| 15 |
--adam_beta1="0.9" \
|
| 16 |
--adam_beta2="0.98" \
|
| 17 |
--logging_steps="10000" \
|