Fill-Mask
Transformers
PyTorch
Safetensors
English
bert
climate-change
domain-adaptation
masked-language-modeling
scientific-nlp
transformer
BERT
SciBERT
Eval Results (legacy)
Instructions to use P0L3/cliscibert_scivocab_uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use P0L3/cliscibert_scivocab_uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="P0L3/cliscibert_scivocab_uncased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("P0L3/cliscibert_scivocab_uncased") model = AutoModelForMaskedLM.from_pretrained("P0L3/cliscibert_scivocab_uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from P0L3/cliscibert_scivocab_uncased: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/P0L3/cliscibert_scivocab_uncased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://P0L3/cliscibert_scivocab_uncased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/P0L3/cliscibert_scivocab_uncased/resolve/main/pytorch_model.bin
440 MB
- Xet hash:
- 7132bfe8841e336eeddac679d7ce2c39708153585b3735121b452a39f3da8eb9
- Size of remote file:
- 440 MB
- SHA256:
- 0522ca606770a0fba8f4ffde8378e78386c283fb8ef635995e58f940794fd330
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