Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Cheng98/bert-base-rte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cheng98/bert-base-rte with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cheng98/bert-base-rte")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cheng98/bert-base-rte") model = AutoModelForSequenceClassification.from_pretrained("Cheng98/bert-base-rte", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 57ad890aa3c3f600b83fdaeb4e6f889f39f62761dc786713a229aaa530742b25
- Size of remote file:
- 3.96 kB
- SHA256:
- e11f9ae603920249b49f55e6cff82db44494674ed95393466630e127f231159b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.