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")# pip install -U transformers accelerate # 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
Download flax_model.msgpack from erathi/finetuned-bart: direct link, hf CLI and curl.
- Browser
- Download file 135 Bytes
-
https://huggingface.co/erathi/finetuned-bart/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://erathi/finetuned-bart/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/erathi/finetuned-bart/resolve/main/flax_model.msgpack
135 Bytes
- Xet hash:
- 9a86e6f211d43ca72d7a89aad2dd9f5d0e2106f1d30b170657bcd638225f60db
- Size of remote file:
- 135 Bytes
- SHA256:
- f615a75675b1367dcfaaf8854cae74d3053a7b5c2ed1403992c0348eff5c2e74
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