Text Classification
Transformers
TensorBoard
Safetensors
bert
HHD
10_class
multi_labels
Generated from Trainer
text-embeddings-inference
Instructions to use subzero9954/bert_model_out with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use subzero9954/bert_model_out with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="subzero9954/bert_model_out")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("subzero9954/bert_model_out") model = AutoModelForSequenceClassification.from_pretrained("subzero9954/bert_model_out") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 47a38a6f35ce25a5f37985812fa9e0edffff5499148ac84a753d978560781dec
- Size of remote file:
- 436 MB
- SHA256:
- 33caad2758d17cb5bb8bc7839fe0b44a7a84cbaf8ff0d6e2721ddaca1039b9de
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.