Instructions to use Srini99/TQA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Srini99/TQA 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="Srini99/TQA")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Srini99/TQA") model = AutoModelForQuestionAnswering.from_pretrained("Srini99/TQA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Srini99/TQA: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/Srini99/TQA/resolve/refs%2Fpr%2F2/tokenizer.json
- Command line
-
hf download hf://Srini99/TQA@refs/pr/2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Srini99/TQA/resolve/refs%2Fpr%2F2/tokenizer.json
17.1 MB
- Xet hash:
- 04d3fd043df32b57c39a84071dfef251cfea9a79d9c64d5e22443040d1789af0
- Size of remote file:
- 17.1 MB
- SHA256:
- 0e04ee5681ff7adc5814ab6f5562b30822762e3fd4f19ec8962cefa191466150
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