Instructions to use saburbutt/roberta_base_tweetqa_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use saburbutt/roberta_base_tweetqa_model with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="saburbutt/roberta_base_tweetqa_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("saburbutt/roberta_base_tweetqa_model") model = AutoModelForQuestionAnswering.from_pretrained("saburbutt/roberta_base_tweetqa_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from saburbutt/roberta_base_tweetqa_model: direct link, hf CLI and curl.
- Browser
- Download file 496 MB
-
https://huggingface.co/saburbutt/roberta_base_tweetqa_model/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://saburbutt/roberta_base_tweetqa_model/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/saburbutt/roberta_base_tweetqa_model/resolve/main/flax_model.msgpack
496 MB
- Xet hash:
- a8a5ce95c960e5400a5f5cd43475a3f20486f325773d3e4863e24ac579edc007
- Size of remote file:
- 496 MB
- SHA256:
- cc72826ea095cd32800a0060098743ec9f7a5936c7055deb5e8174b1e490755e
路
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