Fill-Mask
Transformers
Safetensors
bert
masked-language-modeling
historical-nlp
temporal-language-model
Instructions to use TextMachineProject/NewsBERT_1800-1920-Temporal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TextMachineProject/NewsBERT_1800-1920-Temporal with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="TextMachineProject/NewsBERT_1800-1920-Temporal")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("TextMachineProject/NewsBERT_1800-1920-Temporal") model = AutoModelForMaskedLM.from_pretrained("TextMachineProject/NewsBERT_1800-1920-Temporal", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 40bebc52aba0801ecbd191dddbe5ec46fb77fe09dbf4b68a8fa3b8747ced1612
- Size of remote file:
- 8.17 kB
- SHA256:
- e782d66c99a643197f1ec655cbc2f23a4d5f3efabc3d8a69d9afc06adb054e65
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