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 pytorch_model.bin from prajjwal1/albert-base-v2-mnli: direct link, hf CLI and curl.
- Browser
- Download file 46.7 MB
-
https://huggingface.co/prajjwal1/albert-base-v2-mnli/resolve/3f26119ef90f9188fa03abac724d8ccf40f6335a/pytorch_model.bin
- Command line
-
hf download hf://prajjwal1/albert-base-v2-mnli@3f26119ef90f9188fa03abac724d8ccf40f6335a/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/prajjwal1/albert-base-v2-mnli/resolve/3f26119ef90f9188fa03abac724d8ccf40f6335a/pytorch_model.bin
46.7 MB
- Xet hash:
- d7eb0752a59126f96f24ebfb8381c29e61a8c604cbba37e999290a4a6272ef41
- Size of remote file:
- 46.7 MB
- SHA256:
- 01dd0177545414cd9b7537211be576acdc072e9646fbb6ef4ccdb513762a2e8e
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