Instructions to use StevenLimcorn/bert-large-uncased-facebook-election-ads with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use StevenLimcorn/bert-large-uncased-facebook-election-ads with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="StevenLimcorn/bert-large-uncased-facebook-election-ads")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("StevenLimcorn/bert-large-uncased-facebook-election-ads") model = AutoModelForMaskedLM.from_pretrained("StevenLimcorn/bert-large-uncased-facebook-election-ads", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 3c40262
Model save
Browse files
generation_config.json
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{
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"_from_model_config": true,
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"pad_token_id": 0,
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"transformers_version": "4.29.0.dev0"
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pytorch_model.bin
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runs/Apr26_12-11-39_7a887999508b/events.out.tfevents.1682511110.7a887999508b.10080.0
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size 4895
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