Instructions to use princeton-nlp/bert_small_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use princeton-nlp/bert_small_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="princeton-nlp/bert_small_1")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("princeton-nlp/bert_small_1") model = AutoModelForMaskedLM.from_pretrained("princeton-nlp/bert_small_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4f43cf39042c71b42e81b816565ef448d10c14d64494050af62575d37c772eaa
- Size of remote file:
- 115 MB
- SHA256:
- 13760ecdcff7a02c0e82b7d2d310a11c3a3f0c3af35391d262db6e71a1740f32
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.