Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use SubhasishSaha/Resume-Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SubhasishSaha/Resume-Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SubhasishSaha/Resume-Classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SubhasishSaha/Resume-Classifier") model = AutoModelForSequenceClassification.from_pretrained("SubhasishSaha/Resume-Classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4a9c0b1977d5115aa52888027d55abec944c998f7f9b758204f775fb1eedf259
- Size of remote file:
- 4.92 kB
- SHA256:
- 302506ab37a93d5eb20fdee3991bb66961bbc8ffdc5d7a266c24198635d0d79c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.