Summarization
Transformers
Safetensors
English
bart
text2text-generation
abstractive
hybrid
multistep
Eval Results (legacy)
Instructions to use MikaSie/LexLM_BART_dependent_V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MikaSie/LexLM_BART_dependent_V1 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="MikaSie/LexLM_BART_dependent_V1")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("MikaSie/LexLM_BART_dependent_V1") model = AutoModelForSeq2SeqLM.from_pretrained("MikaSie/LexLM_BART_dependent_V1") - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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---
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language: en
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tags:
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- summarization
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- abstractive
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- hybrid
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- multistep
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base_model: BART
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datasets: dennlinger/eur-lex-sum
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pipeline_tag: summarization
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model-index:
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- name: BART
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results:
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- type: BARTScore
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value: -3.5440571791465922
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- type: BLANC
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value: 0.
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---
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# Model Card for LexLM_BART_dependent_V1
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---
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base_model: BART
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datasets: dennlinger/eur-lex-sum
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language: en
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pipeline_tag: summarization
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tags:
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- summarization
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- abstractive
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- hybrid
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- multistep
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model-index:
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- name: BART
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results:
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- type: BARTScore
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value: -3.5440571791465922
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- type: BLANC
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value: 0.12766121384425627
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---
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# Model Card for LexLM_BART_dependent_V1
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