Text Classification
Transformers
PyTorch
distilbert
fine-tuning
resume classification
text-embeddings-inference
Instructions to use oussama120/Resume_Sentence_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oussama120/Resume_Sentence_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="oussama120/Resume_Sentence_Classification", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("oussama120/Resume_Sentence_Classification") model = AutoModelForSequenceClassification.from_pretrained("oussama120/Resume_Sentence_Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 88ff1f775ae2b7c519af2bd6b5d8a21f4243e18be79e0ca811282df83ece0b31
- Size of remote file:
- 268 MB
- SHA256:
- 0a87fcbddc6c35520930f4c0411b2c485fb3ee8a732a393a03d99ff3f63b6c05
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.