Summarization
Transformers
PyTorch
Safetensors
English
t5
text2text-generation
text-generation-inference
Instructions to use LukeMoore11/Big-Benjamin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LukeMoore11/Big-Benjamin 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="LukeMoore11/Big-Benjamin")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("LukeMoore11/Big-Benjamin") model = AutoModelForSeq2SeqLM.from_pretrained("LukeMoore11/Big-Benjamin", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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README.md
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datasets:
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- LukeMoore11/autotrain-data-second-attempt
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co2_eq_emissions:
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emissions: 67.54051067286701
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# Model Trained Using AutoTrain
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- Problem type: Summarization
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- Model ID: 68749137463
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- CO2 Emissions (in grams): 67.5405
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## Validation Metrics
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- Loss: 1.379
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- Rouge2: 20.238
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- RougeL: 24.044
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- RougeLsum: 24.222
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- Gen Len: 19.000
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## Usage
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language:
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widget:
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- text: "Enter legal document..."
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datasets:
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- LukeMoore11/autotrain-data-second-attempt
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co2_eq_emissions:
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emissions: 67.54051067286701
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---
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## Validation Metrics
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- Loss: 1.379
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- Rouge2: 20.238
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- RougeL: 24.044
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- RougeLsum: 24.222
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## Usage
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