Instructions to use textattack/albert-base-v2-RTE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/albert-base-v2-RTE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/albert-base-v2-RTE")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/albert-base-v2-RTE") model = AutoModelForSequenceClassification.from_pretrained("textattack/albert-base-v2-RTE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from textattack/albert-base-v2-RTE: direct link, hf CLI and curl.
- Browser
- Download file 46.7 MB
-
https://huggingface.co/textattack/albert-base-v2-RTE/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://textattack/albert-base-v2-RTE@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/textattack/albert-base-v2-RTE/resolve/refs%2Fpr%2F1/model.safetensors
46.7 MB
- Xet hash:
- a524089714da3a21e75be0c59687411c6cc76f8a689de42302791bb2c1bc7c09
- Size of remote file:
- 46.7 MB
- SHA256:
- f4a6d02f4ecf816f9fd0db58a961aeb3dc891fb3720b2ae8c77fc3ae6411b9de
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.