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
Update continuous_time_embedding.py
Browse files
continuous_time_embedding.py
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Usage:
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from continuous_time_embedding import load_continuous_time_model
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tokenizer, model = load_continuous_time_model("
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"""
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Usage:
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from continuous_time_embedding import load_continuous_time_model
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tokenizer, model = load_continuous_time_model("TextMachineProject/NewsBERT_1800-1920-Temporal")
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"""
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