Summarization
Transformers
PyTorch
Safetensors
English
bart
text2text-generation
Generated from Trainer
Instructions to use EasyTerms/etsummerizer_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EasyTerms/etsummerizer_v2 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="EasyTerms/etsummerizer_v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("EasyTerms/etsummerizer_v2") model = AutoModelForSeq2SeqLM.from_pretrained("EasyTerms/etsummerizer_v2") - Notebooks
- Google Colab
- Kaggle
Add training dataset to model card
Browse files
README.md
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# etsummerizer_v2
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This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on
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It achieves the following results on the evaluation set:
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- Loss: 0.3484
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- Rouge1: 0.5448
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## Model description
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This model was finetuned on legal text extracted from different terms and conditions documents.
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in a simplified version lacking in legal jargon.
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## Intended uses & limitations
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As it is the second version of this model it effectively summerize legal text however, further training will be required to improve the simplification task.
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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# etsummerizer_v2
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This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on [EasyTerms/Manuel_dataset](https://huggingface.co/datasets/EasyTerms/Manuel_dataset).
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It achieves the following results on the evaluation set:
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- Loss: 0.3484
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- Rouge1: 0.5448
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## Model description
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This model was finetuned on legal text extracted from different terms and conditions documents. Its objective is to efficiently summerize such text and present the generation
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in a simplified version lacking in legal jargon.
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## Intended uses & limitations
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As it is the second version of this model it effectively summerize legal text however, further training will be required to improve the simplification task.
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### Training hyperparameters
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The following hyperparameters were used during training:
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