Instructions to use pratiknichite/TrainedTextSummerizerBART with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pratiknichite/TrainedTextSummerizerBART with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" 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("summarization", model="pratiknichite/TrainedTextSummerizerBART")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("pratiknichite/TrainedTextSummerizerBART") model = AutoModelForSeq2SeqLM.from_pretrained("pratiknichite/TrainedTextSummerizerBART", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from pratiknichite/TrainedTextSummerizerBART: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/pratiknichite/TrainedTextSummerizerBART/resolve/main/training_args.bin
- Command line
-
hf download hf://pratiknichite/TrainedTextSummerizerBART/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/pratiknichite/TrainedTextSummerizerBART/resolve/main/training_args.bin
4.54 kB
- Xet hash:
- c8fbbf84ca49af22f15550d1ab84112f96eb9daa183ae8c28b296225d61c2f41
- Size of remote file:
- 4.54 kB
- SHA256:
- dc3942b9483e8a09f5ec28281c5fe921fb2bc85bc565064394b07a2a1cce7d82
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