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")# pip install -U transformers accelerate # 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 README.md
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README.md
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@@ -65,7 +65,7 @@ import math
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from huggingface_hub import hf_hub_download
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import shutil, os
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REPO_ID = "
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# Download the companion module from the model repo and make it importable
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py_path = hf_hub_download(repo_id=REPO_ID, filename="continuous_time_embedding.py")
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from huggingface_hub import hf_hub_download
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import shutil, os
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REPO_ID = "TextMachineProject/NewsBERT_1800-1920-Temporal"
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# Download the companion module from the model repo and make it importable
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py_path = hf_hub_download(repo_id=REPO_ID, filename="continuous_time_embedding.py")
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