Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use wsqstar/bert-finetuned-weibo-luobokuaipao with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wsqstar/bert-finetuned-weibo-luobokuaipao with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wsqstar/bert-finetuned-weibo-luobokuaipao")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("wsqstar/bert-finetuned-weibo-luobokuaipao") model = AutoModelForSequenceClassification.from_pretrained("wsqstar/bert-finetuned-weibo-luobokuaipao") - Notebooks
- Google Colab
- Kaggle
bert-finetuned-weibo-luobokuaipao / runs /Jul28_02-55-18_1b1a04916f84 /events.out.tfevents.1722138157.1b1a04916f84.1141.1
- Xet hash:
- 9c2f7cc4656d14cf55165062fddb7e5709cd1b052fee791aba504cbdeef3b0a4
- Size of remote file:
- 5.64 kB
- SHA256:
- 033ac4341a35884e4420e2f30363bc91dfa1dcc903566ef67daa79565a9fe0e1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.