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
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Download README.md from LukeMoore11/Big-Benjamin: direct link, hf CLI and curl.
- Browser
- Download file 299 Bytes
-
https://huggingface.co/LukeMoore11/Big-Benjamin/resolve/main/README.md
- Command line
-
hf download hf://LukeMoore11/Big-Benjamin/README.md
-
curl -L -o README.md https://huggingface.co/LukeMoore11/Big-Benjamin/resolve/main/README.md
299 Bytes
metadata
tags:
- summarization
language:
- en
widget:
- text: Enter legal document...
datasets:
- LukeMoore11/autotrain-data-second-attempt
co2_eq_emissions:
emissions: 67.54051067286701
Validation Metrics
- Loss: 1.379
- Rouge1: 24.817
- Rouge2: 20.238
- RougeL: 24.044
- RougeLsum: 24.222