Summarization
Transformers
PyTorch
TensorFlow
JAX
Rust
English
bart
text2text-generation
Eval Results (legacy)
Instructions to use facebook/bart-large-xsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/bart-large-xsum 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="facebook/bart-large-xsum")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-xsum") model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-xsum") - Inference
- Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +2 -2
config.json
CHANGED
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@@ -52,7 +52,7 @@
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"output_hidden_states": false,
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"output_past": true,
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"pad_token_id": 1,
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"prefix":
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"pruned_heads": {},
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"repetition_penalty": 1.0,
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"task_specific_params": null,
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@@ -62,4 +62,4 @@
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"torchscript": false,
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"use_bfloat16": false,
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"vocab_size": 50264
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}
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"output_hidden_states": false,
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"output_past": true,
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"pad_token_id": 1,
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"prefix": " ",
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"pruned_heads": {},
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"repetition_penalty": 1.0,
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"task_specific_params": null,
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"torchscript": false,
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"use_bfloat16": false,
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"vocab_size": 50264
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}
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