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 tokenizer_config.json from 123tarunanand/albert-xlarge-finetuned: direct link, hf CLI and curl.
- Browser
- Download file 58 Bytes
-
https://huggingface.co/123tarunanand/albert-xlarge-finetuned/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://123tarunanand/albert-xlarge-finetuned/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/123tarunanand/albert-xlarge-finetuned/resolve/main/tokenizer_config.json
58 Bytes
| {"do_lower_case": true, "max_len": 512, "init_inputs": []} |