Instructions to use eunjin/bert_base_cased_semantics with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eunjin/bert_base_cased_semantics with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="eunjin/bert_base_cased_semantics")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("eunjin/bert_base_cased_semantics") model = AutoModel.from_pretrained("eunjin/bert_base_cased_semantics", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from eunjin/bert_base_cased_semantics: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/eunjin/bert_base_cased_semantics/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://eunjin/bert_base_cased_semantics/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/eunjin/bert_base_cased_semantics/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- ac5ccb541b139b6ae9181a460960fb895f47b4d795eb2baa3e5e45bc63c8b252
- Size of remote file:
- 433 MB
- SHA256:
- 75a1e606ad58eb583001ed2b352481ae3892106d3e9f13c198002f75de1347bb
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