Token Classification
Transformers
PyTorch
Safetensors
English
bert
toponym detection
language model
geospatial understanding
geolm
Instructions to use knowledge-computing/geolm-base-toponym-recognition with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use knowledge-computing/geolm-base-toponym-recognition with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="knowledge-computing/geolm-base-toponym-recognition")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("knowledge-computing/geolm-base-toponym-recognition") model = AutoModelForTokenClassification.from_pretrained("knowledge-computing/geolm-base-toponym-recognition", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from knowledge-computing/geolm-base-toponym-recognition: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/knowledge-computing/geolm-base-toponym-recognition/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://knowledge-computing/geolm-base-toponym-recognition/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/knowledge-computing/geolm-base-toponym-recognition/resolve/main/pytorch_model.bin
431 MB
- Xet hash:
- 10a05086789cc7536c582dad4f3e234a77e9aa2a1142c347edf8442314e05cbf
- Size of remote file:
- 431 MB
- SHA256:
- 22d721b73fdf69b5160e9975bce00c6244d18812fc5e9ffcbff01797f4411393
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