Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use pulkitmehtawork/text_classification_pulkit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pulkitmehtawork/text_classification_pulkit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pulkitmehtawork/text_classification_pulkit")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pulkitmehtawork/text_classification_pulkit") model = AutoModelForSequenceClassification.from_pretrained("pulkitmehtawork/text_classification_pulkit") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aecbb3427cc63efc330ca7c4384c79d204ad63230f617fdc93d374f9f74e0637
- Size of remote file:
- 3.96 kB
- SHA256:
- 6f11d7385032c4f3c1156561e300a58de25faa0214e0814e0ae4d069c1d9f21f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.