Instructions to use 123tarunanand/albert-xlarge-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 123tarunanand/albert-xlarge-finetuned 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="123tarunanand/albert-xlarge-finetuned")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("123tarunanand/albert-xlarge-finetuned") model = AutoModelForQuestionAnswering.from_pretrained("123tarunanand/albert-xlarge-finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from 123tarunanand/albert-xlarge-finetuned: direct link, hf CLI and curl.
- Browser
- Download file 1.42 kB
-
https://huggingface.co/123tarunanand/albert-xlarge-finetuned/resolve/main/training_args.bin
- Command line
-
hf download hf://123tarunanand/albert-xlarge-finetuned/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/123tarunanand/albert-xlarge-finetuned/resolve/main/training_args.bin
1.42 kB
- Xet hash:
- f1c4626fe31e816e7447940554876a01bf74453ce27dbdc19670a1ce51b8a9fb
- Size of remote file:
- 1.42 kB
- SHA256:
- c32566fecd5fffd748f6ab2d71404c2a42d714391eaca4453d3e16c2da226284
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.