Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use ratish/bert-textClassification_v1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ratish/bert-textClassification_v1.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ratish/bert-textClassification_v1.1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ratish/bert-textClassification_v1.1") model = AutoModelForSequenceClassification.from_pretrained("ratish/bert-textClassification_v1.1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ebd3793387d43d25fc6e392ef141efe3a44f3606e87a6d38f85e84ef9ba988cb
- Size of remote file:
- 268 MB
- SHA256:
- e5f2849a77e652daaff9ac1b8555fa7ed5900873f3f4e60fb32d693754a0f8f7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.