Text Classification
Transformers
PyTorch
Safetensors
English
bert
rte
glue
torchdistill
text-embeddings-inference
Instructions to use yoshitomo-matsubara/bert-base-uncased-rte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yoshitomo-matsubara/bert-base-uncased-rte with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yoshitomo-matsubara/bert-base-uncased-rte")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("yoshitomo-matsubara/bert-base-uncased-rte") model = AutoModelForSequenceClassification.from_pretrained("yoshitomo-matsubara/bert-base-uncased-rte", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download special_tokens_map.json from yoshitomo-matsubara/bert-base-uncased-rte: direct link, hf CLI and curl.
- Browser
- Download file 112 Bytes
-
https://huggingface.co/yoshitomo-matsubara/bert-base-uncased-rte/resolve/main/special_tokens_map.json
- Command line
-
hf download hf://yoshitomo-matsubara/bert-base-uncased-rte/special_tokens_map.json
-
curl -L -o special_tokens_map.json https://huggingface.co/yoshitomo-matsubara/bert-base-uncased-rte/resolve/main/special_tokens_map.json
112 Bytes
| {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"} |