Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use ManishW/text-classification-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ManishW/text-classification-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ManishW/text-classification-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ManishW/text-classification-model") model = AutoModelForSequenceClassification.from_pretrained("ManishW/text-classification-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 983402cc900ce59da713f41b9b69bb9a0b459a2c541df86097fb4abd6db6ac6a
- Size of remote file:
- 268 MB
- SHA256:
- f1ef16d731296cff51b753fcdbb07c66d436055164d9fb2251c0bb81f7375afc
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