Text Classification
Transformers
TensorBoard
Safetensors
English
bert
Generated from Trainer
text-embeddings-inference
Instructions to use ezzaldeen/bert-finetuned-mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ezzaldeen/bert-finetuned-mrpc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ezzaldeen/bert-finetuned-mrpc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ezzaldeen/bert-finetuned-mrpc") model = AutoModelForSequenceClassification.from_pretrained("ezzaldeen/bert-finetuned-mrpc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ezzaldeen/bert-finetuned-mrpc: direct link, hf CLI and curl.
- Browser
- Download file 4.6 kB
-
https://huggingface.co/ezzaldeen/bert-finetuned-mrpc/resolve/main/training_args.bin
- Command line
-
hf download hf://ezzaldeen/bert-finetuned-mrpc/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ezzaldeen/bert-finetuned-mrpc/resolve/main/training_args.bin
4.6 kB
- Xet hash:
- 87e86a0ed6e5f75d8e7a82dee0077062b73f3e9a585bc6bc270dd42cf8c1025e
- Size of remote file:
- 4.6 kB
- SHA256:
- cf17060ca54f3a8601cbb42b60ad71bd6bb8ac05a7e0391fd7adbba6a238fbb6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.