Text Classification
Transformers
Safetensors
English
bert
sentiment
english
text-embeddings-inference
Instructions to use ExecuteAutomation/bert-base-text-classification-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ExecuteAutomation/bert-base-text-classification-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ExecuteAutomation/bert-base-text-classification-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ExecuteAutomation/bert-base-text-classification-model") model = AutoModelForSequenceClassification.from_pretrained("ExecuteAutomation/bert-base-text-classification-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d3f6b96d9f5d9b384ec5ad502aaa8d8e54fd29efb09f3c4ad5b8d55be49865d9
- Size of remote file:
- 5.37 kB
- SHA256:
- bc3b1ad2c936140c0c93fc3bb4dadf110449dd5757fd206efb2664135cf7c1c2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.