Summarization
Transformers
Safetensors
Spanish
led
text2text-generation
abstractive-summarization
long-text
spanish
Instructions to use excribe/Led_sgd_summarizer_250_sp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use excribe/Led_sgd_summarizer_250_sp 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="excribe/Led_sgd_summarizer_250_sp")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("excribe/Led_sgd_summarizer_250_sp") model = AutoModelForSeq2SeqLM.from_pretrained("excribe/Led_sgd_summarizer_250_sp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 21b71e9cf9eec8de2ac93fe4250ea53b3f45d12ec8b9ee7577f00da62f65bf1e
- Size of remote file:
- 5.5 kB
- SHA256:
- 4b29084bf653dfa8f246d7e6f0997d6bf782fe32d09331d7f741367f0b5eba57
路
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