Instructions to use prajjwal1/albert-base-v2-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prajjwal1/albert-base-v2-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="prajjwal1/albert-base-v2-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("prajjwal1/albert-base-v2-mnli") model = AutoModelForSequenceClassification.from_pretrained("prajjwal1/albert-base-v2-mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download cls_embeddings_mnli.pth from prajjwal1/albert-base-v2-mnli: direct link, hf CLI and curl.
- Browser
- Download file 1.82 GB
-
https://huggingface.co/prajjwal1/albert-base-v2-mnli/resolve/8e938e505595a833a4e26af669462dbe8d1dab59/cls_embeddings_mnli.pth
- Command line
-
hf download hf://prajjwal1/albert-base-v2-mnli@8e938e505595a833a4e26af669462dbe8d1dab59/cls_embeddings_mnli.pth
-
curl -L -o cls_embeddings_mnli.pth https://huggingface.co/prajjwal1/albert-base-v2-mnli/resolve/8e938e505595a833a4e26af669462dbe8d1dab59/cls_embeddings_mnli.pth
1.82 GB
- Xet hash:
- 09703faeca81c5a6798e7a1fa12a7d97594765932d33b3d21b7a9dc4dbf7332d
- Size of remote file:
- 1.82 GB
- SHA256:
- a2582142b23d4cfdedd1d48e0b5efd5f6ebdd2dee05bd54e0e4c8f759f122be1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.