Instructions to use Palak/albert-large-v2_squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Palak/albert-large-v2_squad 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="Palak/albert-large-v2_squad")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Palak/albert-large-v2_squad") model = AutoModelForQuestionAnswering.from_pretrained("Palak/albert-large-v2_squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from Palak/albert-large-v2_squad: direct link, hf CLI and curl.
- Browser
- Download file 124 Bytes
-
https://huggingface.co/Palak/albert-large-v2_squad/resolve/main/eval_results.json
- Command line
-
hf download hf://Palak/albert-large-v2_squad/eval_results.json
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curl -L -o eval_results.json https://huggingface.co/Palak/albert-large-v2_squad/resolve/main/eval_results.json
124 Bytes
| { | |
| "epoch": 3.0, | |
| "eval_exact_match": 84.80605487228004, | |
| "eval_f1": 91.80638438705844, | |
| "eval_samples": 10808 | |
| } |