Summarization
Transformers
Safetensors
Estonian
mbart
text2text-generation
mbart50
low-resource
research
rahvusarhiiv
Eval Results (legacy)
Instructions to use Rahvusarhiiv/et_summariser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rahvusarhiiv/et_summariser 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="Rahvusarhiiv/et_summariser")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Rahvusarhiiv/et_summariser") model = AutoModelForSeq2SeqLM.from_pretrained("Rahvusarhiiv/et_summariser", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 91bc2caa6508a705efb4a8c838483b2d322e9574158bef09d92ed72e14450c50
- Size of remote file:
- 17.1 MB
- SHA256:
- b8eff9b5761b3ff5c905a3f4eb73800e892b32b6c4f07f210f83ae354c42aa35
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.