Instructions to use Eitanli/albert-base-v2-topic-abstract-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Eitanli/albert-base-v2-topic-abstract-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Eitanli/albert-base-v2-topic-abstract-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Eitanli/albert-base-v2-topic-abstract-classification") model = AutoModelForSequenceClassification.from_pretrained("Eitanli/albert-base-v2-topic-abstract-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Eitanli/albert-base-v2-topic-abstract-classification: direct link, hf CLI and curl.
- Browser
- Download file 46.7 MB
-
https://huggingface.co/Eitanli/albert-base-v2-topic-abstract-classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Eitanli/albert-base-v2-topic-abstract-classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Eitanli/albert-base-v2-topic-abstract-classification/resolve/main/pytorch_model.bin
46.7 MB
- Xet hash:
- 481b2d04bbbce8822baefd12425f64d4b84ebed75ed1b2081ccf9c5b8795039b
- Size of remote file:
- 46.7 MB
- SHA256:
- 82c02483f3222d4a8e113719d4aff1b6ac8c072022bf8b86db124cb9d6bf4e2d
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