Summarization
Transformers
PyTorch
English
bart
text2text-generation
sagemaker
Eval Results (legacy)
Instructions to use slauw87/bart_summarisation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use slauw87/bart_summarisation 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="slauw87/bart_summarisation")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("slauw87/bart_summarisation") model = AutoModelForSeq2SeqLM.from_pretrained("slauw87/bart_summarisation") - Notebooks
- Google Colab
- Kaggle
Upload special_tokens_map.json
Browse files- special_tokens_map.json +1 -0
special_tokens_map.json
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{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": false}}
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