Summarization
Transformers
PyTorch
TensorFlow
JAX
Rust
Safetensors
English
bart
text2text-generation
Eval Results (legacy)
Instructions to use erathi/finetuned-bart with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use erathi/finetuned-bart 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="erathi/finetuned-bart")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("erathi/finetuned-bart") model = AutoModelForSeq2SeqLM.from_pretrained("erathi/finetuned-bart", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3d15b3e92f26c51059f2dbb949b2e510f14ad90adac2c048933a262e9ef02b93
- Size of remote file:
- 135 Bytes
- SHA256:
- 33bae8b845678132267019667306bfe2ee8ca730ced34cc5ddf750ea47b6b1b3
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