Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use yashss/finetuned-bert-mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yashss/finetuned-bert-mrpc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yashss/finetuned-bert-mrpc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("yashss/finetuned-bert-mrpc") model = AutoModelForSequenceClassification.from_pretrained("yashss/finetuned-bert-mrpc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b2d443488700acbf9826a93ef2a48e7c147b6bcfbc1ef6a2840ab5dc2fc362d6
- Size of remote file:
- 5.18 kB
- SHA256:
- 5ec6c6c2953e42f5bae21797fb3dead22da67e7f08459ed6839521cc92b0149b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.