Instructions to use dima806/medium-article-titles-engagement with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/medium-article-titles-engagement with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dima806/medium-article-titles-engagement")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dima806/medium-article-titles-engagement") model = AutoModelForSequenceClassification.from_pretrained("dima806/medium-article-titles-engagement", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from dima806/medium-article-titles-engagement: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/dima806/medium-article-titles-engagement/resolve/main/training_args.bin
- Command line
-
hf download hf://dima806/medium-article-titles-engagement/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dima806/medium-article-titles-engagement/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- cf0fed3f0a117ba11cdeaceea5046b1dddbeb077593b5d7c1ea7b2fdf7c4fc1d
- Size of remote file:
- 3.58 kB
- SHA256:
- 35e9afce1e3c79b8e48cf5f8ebfabc8d8c664c71a26a1f06b3d10a19ff9b5ca5
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