Instructions to use PeterBanning71/bart-large-finetuned-eLife with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PeterBanning71/bart-large-finetuned-eLife 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="PeterBanning71/bart-large-finetuned-eLife")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("PeterBanning71/bart-large-finetuned-eLife") model = AutoModelForSeq2SeqLM.from_pretrained("PeterBanning71/bart-large-finetuned-eLife", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from PeterBanning71/bart-large-finetuned-eLife: direct link, hf CLI and curl.
- Browser
- Download file 3.64 kB
-
https://huggingface.co/PeterBanning71/bart-large-finetuned-eLife/resolve/main/training_args.bin
- Command line
-
hf download hf://PeterBanning71/bart-large-finetuned-eLife/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/PeterBanning71/bart-large-finetuned-eLife/resolve/main/training_args.bin
3.64 kB
- Xet hash:
- efbbe07180926afe4981cbb7831c1878ebd1a22f440b585b89b04dd89c1b98d4
- Size of remote file:
- 3.64 kB
- SHA256:
- 966c393d0c7285e556500c2f291d4d2f3eba4495121ca9a3ac404689a1b3ffd3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.