Text Classification
Transformers
TensorBoard
Safetensors
bert
HHD
10_class
multi_labels
Generated from Trainer
text-embeddings-inference
Instructions to use 1009bmj/bert_model_out with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 1009bmj/bert_model_out with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="1009bmj/bert_model_out")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("1009bmj/bert_model_out") model = AutoModelForSequenceClassification.from_pretrained("1009bmj/bert_model_out") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 76c1201c2b832531128740fd7b2e3690197d31f05cca931e35bb2cc94a19e5a9
- Size of remote file:
- 5.37 kB
- SHA256:
- d93f22279bf43e5ef7ada4b345a1a199ede4b870298e4c130de697773e3f0a57
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.