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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| {"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>", "extra_ids": 0, "additional_special_tokens": null, "sp_model_kwargs": {}, "special_tokens_map_file": "rut5-base/special_tokens_map.json", "tokenizer_file": null, "name_or_path": "/gd/MyDrive/models/rut5-base-absum", "tokenizer_class": "T5Tokenizer"} |