Instructions to use tner/roberta-base-tweetner7-2020 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tner/roberta-base-tweetner7-2020 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="tner/roberta-base-tweetner7-2020")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("tner/roberta-base-tweetner7-2020") model = AutoModelForTokenClassification.from_pretrained("tner/roberta-base-tweetner7-2020", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tner/roberta-base-tweetner7-2020: direct link, hf CLI and curl.
- Browser
- Download file 496 MB
-
https://huggingface.co/tner/roberta-base-tweetner7-2020/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tner/roberta-base-tweetner7-2020/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tner/roberta-base-tweetner7-2020/resolve/main/pytorch_model.bin
496 MB
- Xet hash:
- 88a839d958b7fc83e756de6c0045f0148a18c61f4f872c4b3cb0b8451ab13c0d
- Size of remote file:
- 496 MB
- SHA256:
- 8ea1e384f9061b9576c578e52d41058cabb44f3bf3ed8350d9a7f83e81f77f94
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