Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use ratish/bert-textClassification_v1.4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ratish/bert-textClassification_v1.4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ratish/bert-textClassification_v1.4")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ratish/bert-textClassification_v1.4") model = AutoModelForSequenceClassification.from_pretrained("ratish/bert-textClassification_v1.4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9388fe93d5887e960e4128a73883db2b14c74bb09be332307721cbbe851b736b
- Size of remote file:
- 268 MB
- SHA256:
- b02ad3c3fe7d8e2b9fa1220a06f90c8bfc05fb7545d5682febc1a97cf1e2baec
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