Text Classification
Transformers
PyTorch
English
distilbert
distilbert-base-uncased-finetuned-sst-2-english
Instructions to use rosherd/job_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rosherd/job_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rosherd/job_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rosherd/job_classification") model = AutoModelForSequenceClassification.from_pretrained("rosherd/job_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from rosherd/job_classification: direct link, hf CLI and curl.
- Browser
- Download file 274 MB
-
https://huggingface.co/rosherd/job_classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://rosherd/job_classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rosherd/job_classification/resolve/main/pytorch_model.bin
274 MB
- Xet hash:
- cf99d2ab30cbdb15b419a7626a513bf3f15b2bc14ce44c71b6fd38144823948a
- Size of remote file:
- 274 MB
- SHA256:
- bac1d80ece71bd7ae7327dff1bf3640237d87549e1efe014cbce0681c5c31ef0
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