Instructions to use TehranNLP/albert-base-v2-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TehranNLP/albert-base-v2-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TehranNLP/albert-base-v2-mnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TehranNLP/albert-base-v2-mnli") model = AutoModelForSequenceClassification.from_pretrained("TehranNLP/albert-base-v2-mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from TehranNLP/albert-base-v2-mnli: direct link, hf CLI and curl.
- Browser
- Download file 46.8 MB
-
https://huggingface.co/TehranNLP/albert-base-v2-mnli/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://TehranNLP/albert-base-v2-mnli/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/TehranNLP/albert-base-v2-mnli/resolve/main/pytorch_model.bin
46.8 MB
- Xet hash:
- a716e71c696d1873132039f5205607b58335f40008fa186ed8680296b2f79df0
- Size of remote file:
- 46.8 MB
- SHA256:
- 311e8aa3beca81ab4f90fe00cc051747164d8eea6c39497c13c3f935e8865996
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