Summarization
Transformers
Safetensors
PyTorch
English
t5
text2text-generation
t5-small
text-summarization
text-generation-inference
Instructions to use unnat17/Text-Summarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unnat17/Text-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="unnat17/Text-Summarizer")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("unnat17/Text-Summarizer") model = AutoModelForSeq2SeqLM.from_pretrained("unnat17/Text-Summarizer") - Notebooks
- Google Colab
- Kaggle
| { | |
| "backend": "tokenizers", | |
| "clean_up_tokenization_spaces": true, | |
| "eos_token": "</s>", | |
| "extra_ids": 100, | |
| "is_local": false, | |
| "model_max_length": 512, | |
| "pad_token": "<pad>", | |
| "tokenizer_class": "T5Tokenizer", | |
| "unk_token": "<unk>", | |
| "extra_special_tokens": {} | |
| } |