Instructions to use uhhlt/comp-seqlab-dslim-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use uhhlt/comp-seqlab-dslim-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="uhhlt/comp-seqlab-dslim-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("uhhlt/comp-seqlab-dslim-bert") model = AutoModelForTokenClassification.from_pretrained("uhhlt/comp-seqlab-dslim-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from uhhlt/comp-seqlab-dslim-bert: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/uhhlt/comp-seqlab-dslim-bert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://uhhlt/comp-seqlab-dslim-bert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/uhhlt/comp-seqlab-dslim-bert/resolve/main/pytorch_model.bin
436 MB
- Xet hash:
- fe6aa64483fe55232e4d4e639984b96a5e060f3ec0c7e4e324b49cbb0b464e5a
- Size of remote file:
- 436 MB
- SHA256:
- dd7f160bb5e1b58f8ca8832a3ed140cfe85e16991f5964f87ddfacd0c278140d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.