Summarization
Transformers
PyTorch
TensorBoard
ONNX
Safetensors
German
bart
text2text-generation
Generated from Trainer
Eval Results (legacy)
Instructions to use Shahm/bart-german with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shahm/bart-german 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="Shahm/bart-german")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Shahm/bart-german") model = AutoModelForSeq2SeqLM.from_pretrained("Shahm/bart-german") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
#15
by librarian-bot - opened
README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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- summarization
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datasets:
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- mlsum
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language: de
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metrics:
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- rouge
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model-index:
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- name: mode-bart-deutsch
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results:
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- task:
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name: Summarization
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type: summarization
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dataset:
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name: mlsum de
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type: mlsum
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args: de
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metrics:
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-
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type: rouge
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value: 41.698
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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---
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language: de
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license: apache-2.0
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tags:
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- generated_from_trainer
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- summarization
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datasets:
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- mlsum
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metrics:
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- rouge
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base_model: facebook/bart-base
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model-index:
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- name: mode-bart-deutsch
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results:
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- task:
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type: summarization
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name: Summarization
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dataset:
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name: mlsum de
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type: mlsum
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args: de
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metrics:
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- type: rouge
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value: 41.698
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name: Rouge1
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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