Instructions to use ax13/summarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ax13/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="ax13/summarizer")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ax13/summarizer") model = AutoModelForSeq2SeqLM.from_pretrained("ax13/summarizer") - Notebooks
- Google Colab
- Kaggle
Training complete
Browse files
generation_config.json
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{
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"bos_token_id": 0,
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"decoder_start_token_id": 2,
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"early_stopping": true,
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"eos_token_id": 2,
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"forced_bos_token_id": 0,
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"forced_eos_token_id": 2,
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"no_repeat_ngram_size": 3,
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"num_beams": 4,
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"pad_token_id": 1,
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"transformers_version": "4.28.1"
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}
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pytorch_model.bin
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runs/Apr25_00-53-55_5432bd2aa93e/events.out.tfevents.1682384068.5432bd2aa93e.262.0
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runs/Apr25_00-53-55_5432bd2aa93e/events.out.tfevents.1682385536.5432bd2aa93e.262.2
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size 562
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