Instructions to use saburbutt/roberta_large_tweetqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use saburbutt/roberta_large_tweetqa 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_large_tweetqa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("saburbutt/roberta_large_tweetqa") model = AutoModelForQuestionAnswering.from_pretrained("saburbutt/roberta_large_tweetqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from saburbutt/roberta_large_tweetqa: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/saburbutt/roberta_large_tweetqa/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://saburbutt/roberta_large_tweetqa/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/saburbutt/roberta_large_tweetqa/resolve/main/flax_model.msgpack
1.42 GB
- Xet hash:
- 79d4f56daab1e53fb215143898e0a9793fbc04087678a9296fa7f18a74722c90
- Size of remote file:
- 1.42 GB
- SHA256:
- 6839bd3c5dc315ad99b2f2c97ebaafd04905e89ba539efaaba367e09c5a38aaf
路
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