Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use kunalr63/my_awesome_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kunalr63/my_awesome_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kunalr63/my_awesome_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kunalr63/my_awesome_model") model = AutoModelForSequenceClassification.from_pretrained("kunalr63/my_awesome_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 47d88d58a853d5725cbc6bf6fd3fc44d20a8fe8950df814683aa5f0bf00343fd
- Size of remote file:
- 268 MB
- SHA256:
- f244714066417a2c3e294cfc53a5dee27284a9af98a4907ba59281575d72c25d
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