Instructions to use Azma-AI/bart-conversation-summarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Azma-AI/bart-conversation-summarizer 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="Azma-AI/bart-conversation-summarizer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Azma-AI/bart-conversation-summarizer") model = AutoModelForSeq2SeqLM.from_pretrained("Azma-AI/bart-conversation-summarizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Azma-AI/bart-conversation-summarizer: direct link, hf CLI and curl.
- Browser
- Download file 3.64 kB
-
https://huggingface.co/Azma-AI/bart-conversation-summarizer/resolve/main/training_args.bin
- Command line
-
hf download hf://Azma-AI/bart-conversation-summarizer/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Azma-AI/bart-conversation-summarizer/resolve/main/training_args.bin
3.64 kB
- Xet hash:
- 6dad7de4038f6a83e9a28314084ae285634928a02fdbdd060a7044bcb805cf39
- Size of remote file:
- 3.64 kB
- SHA256:
- ba7aa1fbf15dcb9783f587f16c1e1b0e48461eff10e83ac003e008a9b1cba17d
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