Instructions to use k4tel/bert-multilingial-geolocation-prediction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use k4tel/bert-multilingial-geolocation-prediction with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="k4tel/bert-multilingial-geolocation-prediction")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("k4tel/bert-multilingial-geolocation-prediction") model = AutoModelForMaskedLM.from_pretrained("k4tel/bert-multilingial-geolocation-prediction", device_map="auto") - Notebooks
- Google Colab
- Kaggle
test init
Browse files- config.json +1 -1
- pytorch_model.bin +1 -1
- training_args.bin +1 -1
config.json
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"architectures": [
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"BertRegressor"
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"model_type": "
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"problem_type": "regression",
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"torch_dtype": "float32",
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"transformers_version": "4.19.3"
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"architectures": [
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"BertRegressor"
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"model_type": "BertClassifier",
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"problem_type": "regression",
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"torch_dtype": "float32",
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"transformers_version": "4.19.3"
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pytorch_model.bin
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training_args.bin
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size 3247
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