Summarization
Transformers
Safetensors
English
bart
text2text-generation
abstractive
hybrid
multistep
Eval Results (legacy)
Instructions to use MikaSie/RoBERTa_BART_dependent_V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MikaSie/RoBERTa_BART_dependent_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/RoBERTa_BART_dependent_V1")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("MikaSie/RoBERTa_BART_dependent_V1") model = AutoModelForSeq2SeqLM.from_pretrained("MikaSie/RoBERTa_BART_dependent_V1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from MikaSie/RoBERTa_BART_dependent_V1: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/MikaSie/RoBERTa_BART_dependent_V1/resolve/main/training_args.bin
- Command line
-
hf download hf://MikaSie/RoBERTa_BART_dependent_V1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/MikaSie/RoBERTa_BART_dependent_V1/resolve/main/training_args.bin
5.18 kB
- Xet hash:
- 1d1373b6f5c4f80e2ae4a8cf8bf1bfe4d9b4ca7a3aa8d010de101769889f86b6
- Size of remote file:
- 5.18 kB
- SHA256:
- 9ffec4a69796bf169d5c70a35b1ca624c1d0a2e8f4736d9a6ab3884a4e60f6a0
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