Instructions to use hf-tiny-model-private/tiny-random-FlaubertWithLMHeadModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-FlaubertWithLMHeadModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hf-tiny-model-private/tiny-random-FlaubertWithLMHeadModel")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-FlaubertWithLMHeadModel") model = AutoModelForMaskedLM.from_pretrained("hf-tiny-model-private/tiny-random-FlaubertWithLMHeadModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 524e9dac0850e837540b0a66dab89d6801e5202cd45089faf014cd0791cb68e8
- Size of remote file:
- 9.24 MB
- SHA256:
- dbf73345922218f4d9302b32ac86c57afc311f6ed07294e156cac1c021afab32
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