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 tokenizer_config.json from prajjwal1/albert-base-v2-mnli: direct link, hf CLI and curl.
- Browser
- Download file 25 Bytes
-
https://huggingface.co/prajjwal1/albert-base-v2-mnli/resolve/287eebeb04b14f3ecbd576de985dcfc29d0b149c/tokenizer_config.json
- Command line
-
hf download hf://prajjwal1/albert-base-v2-mnli@287eebeb04b14f3ecbd576de985dcfc29d0b149c/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/prajjwal1/albert-base-v2-mnli/resolve/287eebeb04b14f3ecbd576de985dcfc29d0b149c/tokenizer_config.json
25 Bytes
| {"model_max_length": 512} |