Instructions to use lqbin/videberta-xsmall_batch24_epoch30v6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lqbin/videberta-xsmall_batch24_epoch30v6 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="lqbin/videberta-xsmall_batch24_epoch30v6")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("lqbin/videberta-xsmall_batch24_epoch30v6") model = AutoModelForQuestionAnswering.from_pretrained("lqbin/videberta-xsmall_batch24_epoch30v6", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from lqbin/videberta-xsmall_batch24_epoch30v6: direct link, hf CLI and curl.
- Browser
- Download file 283 MB
-
https://huggingface.co/lqbin/videberta-xsmall_batch24_epoch30v6/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://lqbin/videberta-xsmall_batch24_epoch30v6/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/lqbin/videberta-xsmall_batch24_epoch30v6/resolve/main/pytorch_model.bin
283 MB
- Xet hash:
- 6b9d57fb80893f0edef8e805859032ff29ca53787e0a26a3f21ac566a9c94e9b
- Size of remote file:
- 283 MB
- SHA256:
- 19de1a5dc35a359f46471011dafc5ae2ead6d1f55aea2da9ad11f1f682721fa2
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