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 special_tokens_map.json from prajjwal1/albert-base-v2-mnli: direct link, hf CLI and curl.
- Browser
- Download file 156 Bytes
-
https://huggingface.co/prajjwal1/albert-base-v2-mnli/resolve/2c602a01692efef87e124e2099bc9f27d232c3f4/special_tokens_map.json
- Command line
-
hf download hf://prajjwal1/albert-base-v2-mnli@2c602a01692efef87e124e2099bc9f27d232c3f4/special_tokens_map.json
-
curl -L -o special_tokens_map.json https://huggingface.co/prajjwal1/albert-base-v2-mnli/resolve/2c602a01692efef87e124e2099bc9f27d232c3f4/special_tokens_map.json
156 Bytes
| {"bos_token": "[CLS]", "eos_token": "[SEP]", "unk_token": "<unk>", "sep_token": "[SEP]", "pad_token": "<pad>", "cls_token": "[CLS]", "mask_token": "[MASK]"} |