Summarization
Transformers
PyTorch
Safetensors
Russian
t5
text2text-generation
russian
text-generation-inference
Instructions to use cointegrated/rut5-base-absum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cointegrated/rut5-base-absum 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="cointegrated/rut5-base-absum")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("cointegrated/rut5-base-absum") model = AutoModelForSeq2SeqLM.from_pretrained("cointegrated/rut5-base-absum") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit ·
ba0a6f7
1
Parent(s): c9b878b
Update README.md (#1)
Browse files- Update README.md (364914481be3d71a110e6ff598bea3c94d2014bf)
Co-authored-by: Denis Petrov <Den4ikAI@users.noreply.huggingface.co>
README.md
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It can be used as follows:
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```python
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from transformers import T5ForConditionalGeneration, T5Tokenizer
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MODEL_NAME = 'cointegrated/rut5-base-absum'
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model = T5ForConditionalGeneration.from_pretrained(MODEL_NAME)
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It can be used as follows:
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```python
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import torch
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from transformers import T5ForConditionalGeneration, T5Tokenizer
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MODEL_NAME = 'cointegrated/rut5-base-absum'
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model = T5ForConditionalGeneration.from_pretrained(MODEL_NAME)
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