Instructions to use eunjin/bert_base_cased_semantics_cls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eunjin/bert_base_cased_semantics_cls with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eunjin/bert_base_cased_semantics_cls")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eunjin/bert_base_cased_semantics_cls") model = AutoModelForSequenceClassification.from_pretrained("eunjin/bert_base_cased_semantics_cls", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from eunjin/bert_base_cased_semantics_cls: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/eunjin/bert_base_cased_semantics_cls/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://eunjin/bert_base_cased_semantics_cls/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/eunjin/bert_base_cased_semantics_cls/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- 0d7b729f822f13030078ef8376c234197543e0448be41077096f66a620ee93ff
- Size of remote file:
- 433 MB
- SHA256:
- faf9a3500aacbff683a95e6f4b8f532776f2fe1e504829b878685bcc7dba1f73
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