Summarization
Transformers
PyTorch
English
led
text2text-generation
text-generation
encoder-decoder
longformer
bart
abstractive-summarization
news-summarization
research-summarization
document-summarization
english
NLP
Instructions to use assemsabry/Research-News-AI-Summarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use assemsabry/Research-News-AI-Summarizer 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="assemsabry/Research-News-AI-Summarizer")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("assemsabry/Research-News-AI-Summarizer") model = AutoModelForSeq2SeqLM.from_pretrained("assemsabry/Research-News-AI-Summarizer") - Notebooks
- Google Colab
- Kaggle
Training details edit
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README.md
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**Training Throughput:**
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- Average step time: ~27 seconds
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- Peak GPU memory usage: ~14.
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- Total tokens processed: ~898M (input + target)
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### Dataset Details
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**Training Throughput:**
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- Average step time: ~27 seconds
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- Peak GPU memory usage: ~14.9 GB per GPU
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- Total tokens processed: ~898M (input + target)
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### Dataset Details
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