Instructions to use jbraha/tweet-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jbraha/tweet-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jbraha/tweet-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jbraha/tweet-bert") model = AutoModelForSequenceClassification.from_pretrained("jbraha/tweet-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jbraha/tweet-bert: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/jbraha/tweet-bert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jbraha/tweet-bert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jbraha/tweet-bert/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 979cb817b35e0a588ceca49e87ffe214f5fc160f61b30ffd47fd7dd6a4a58e9f
- Size of remote file:
- 438 MB
- SHA256:
- 142c20f08e15efd1df3f04f95f95f426e6f1784bea86e5f2524f367853a99f00
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