Instructions to use Chega/distill-scibert_scivocab_uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Chega/distill-scibert_scivocab_uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Chega/distill-scibert_scivocab_uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("Chega/distill-scibert_scivocab_uncased") model = AutoModelForPreTraining.from_pretrained("Chega/distill-scibert_scivocab_uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Chega/distill-scibert_scivocab_uncased: direct link, hf CLI and curl.
- Browser
- Download file 49.6 MB
-
https://huggingface.co/Chega/distill-scibert_scivocab_uncased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Chega/distill-scibert_scivocab_uncased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Chega/distill-scibert_scivocab_uncased/resolve/main/pytorch_model.bin
49.6 MB
- Xet hash:
- f44edfdfa1428b57dc0623777292cf36aebf2b228ee1588fb5197c871bfb3e99
- Size of remote file:
- 49.6 MB
- SHA256:
- 5adf5c626001eb165cf63f09812bb0098c8780a52adce17d2927b4e837175d0a
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