Instructions to use tner/bertweet-base-tweetner7-2021 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tner/bertweet-base-tweetner7-2021 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="tner/bertweet-base-tweetner7-2021")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("tner/bertweet-base-tweetner7-2021") model = AutoModelForTokenClassification.from_pretrained("tner/bertweet-base-tweetner7-2021", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tner/bertweet-base-tweetner7-2021: direct link, hf CLI and curl.
- Browser
- Download file 537 MB
-
https://huggingface.co/tner/bertweet-base-tweetner7-2021/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tner/bertweet-base-tweetner7-2021/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tner/bertweet-base-tweetner7-2021/resolve/main/pytorch_model.bin
537 MB
- Xet hash:
- 4499bf5792b43e49e824c52a24f2a5c246ddc57dd2642c284ee7537951740dcd
- Size of remote file:
- 537 MB
- SHA256:
- 66ff084ebd0d14d2e826d12d6104d2e34ac1efb38bb26468508a6633a1c844ae
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