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formermagic
/

roberta-base-python-1m

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Instructions to use formermagic/roberta-base-python-1m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use formermagic/roberta-base-python-1m with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("fill-mask", model="formermagic/roberta-base-python-1m")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("formermagic/roberta-base-python-1m")
model = AutoModelForMaskedLM.from_pretrained("formermagic/roberta-base-python-1m")

Notebooks
Google Colab
Kaggle

How to use from the

Use from the

Transformers library

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("fill-mask", model="formermagic/roberta-base-python-1m")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("formermagic/roberta-base-python-1m")
model = AutoModelForMaskedLM.from_pretrained("formermagic/roberta-base-python-1m")

Quick Links

Read model documentation
Read docs on high-level-pipeline
Read our learning resources

roberta-base-python-1m

roberta-base-python-1m

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