Instructions to use transformer3/H2-keywordextractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use transformer3/H2-keywordextractor 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="transformer3/H2-keywordextractor")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("transformer3/H2-keywordextractor") model = AutoModelForSeq2SeqLM.from_pretrained("transformer3/H2-keywordextractor", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from transformer3/H2-keywordextractor: direct link, hf CLI and curl.
- Browser
- Download file 2.11 MB
-
https://huggingface.co/transformer3/H2-keywordextractor/resolve/refs%2Fpr%2F3/tokenizer.json
- Command line
-
hf download hf://transformer3/H2-keywordextractor@refs/pr/3/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/transformer3/H2-keywordextractor/resolve/refs%2Fpr%2F3/tokenizer.json
2.11 MB
- Xet hash:
- 46ee84da8c11adba52105d19e4fb335c42564ebbd2d964742432bf1406566215
- Size of remote file:
- 2.11 MB
- SHA256:
- 72d26cea43bdb866302cf14a8a656e86be45200e3396d5d58b71279f8eba989f
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