Summarization
Transformers
PyTorch
ONNX
Safetensors
English
bart
text2text-generation
chemistry
scientific-summarization
distilbart
abstractive
tldr
knowledge-graphs
Eval Results (legacy)
Instructions to use Bocklitz-Lab/lit2vec-tldr-bart-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bocklitz-Lab/lit2vec-tldr-bart-model 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="Bocklitz-Lab/lit2vec-tldr-bart-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Bocklitz-Lab/lit2vec-tldr-bart-model") model = AutoModelForSeq2SeqLM.from_pretrained("Bocklitz-Lab/lit2vec-tldr-bart-model") - Notebooks
- Google Colab
- Kaggle
Gated model You can list files but not access them
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