Summarization
Transformers
Safetensors
English
bart
text2text-generation
abstractive
hybrid
multistep
Eval Results (legacy)
Instructions to use MikaSie/Longformer_BART_fixed_V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MikaSie/Longformer_BART_fixed_V1 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="MikaSie/Longformer_BART_fixed_V1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("MikaSie/Longformer_BART_fixed_V1") model = AutoModelForSeq2SeqLM.from_pretrained("MikaSie/Longformer_BART_fixed_V1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from MikaSie/Longformer_BART_fixed_V1: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/MikaSie/Longformer_BART_fixed_V1/resolve/main/training_args.bin
- Command line
-
hf download hf://MikaSie/Longformer_BART_fixed_V1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/MikaSie/Longformer_BART_fixed_V1/resolve/main/training_args.bin
5.18 kB
- Xet hash:
- e1c222d601e915571e44a83b4b339284d6077df32b8951a548989c65f5a62800
- Size of remote file:
- 5.18 kB
- SHA256:
- e827264280b18c4057b76fa35556cb92fa8b63358c6084ff1706838efa41f75e
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