Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use LovenOO/BERT_with_preprocessing_grid_search with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LovenOO/BERT_with_preprocessing_grid_search with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LovenOO/BERT_with_preprocessing_grid_search")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LovenOO/BERT_with_preprocessing_grid_search") model = AutoModelForSequenceClassification.from_pretrained("LovenOO/BERT_with_preprocessing_grid_search", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 86dcfad866ceb8217b5394d78dd37ed2a28a72faa4e0f1aaa178ce254a8ae332
- Size of remote file:
- 438 MB
- SHA256:
- e49d3fbf6ddfe83c09523bd4d570d75c3d0bdd25f8d6b8edba6c06318c79dd99
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.