Text Classification
Transformers
TensorFlow
xlm-roberta
generated_from_keras_callback
text-embeddings-inference
Instructions to use Harshitha0813/intent-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Harshitha0813/intent-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Harshitha0813/intent-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Harshitha0813/intent-classification") model = AutoModelForSequenceClassification.from_pretrained("Harshitha0813/intent-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c6cef6b225a5d0485b636952629085b0374cac0b39a056d798364e44e532c481
- Size of remote file:
- 1.11 GB
- SHA256:
- 4845e846959347ca46e621530b05d07c6f0525d63852dd3bef44300f6a46cb6c
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