Instructions to use CareerNinja/BERT_2_Labels with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CareerNinja/BERT_2_Labels with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CareerNinja/BERT_2_Labels")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CareerNinja/BERT_2_Labels") model = AutoModelForSequenceClassification.from_pretrained("CareerNinja/BERT_2_Labels", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Adding `safetensors` variant of this model
Verified signatureThis is an automated PR created with https://huggingface.co/spaces/safetensors/convert This new file is equivalent to `pytorch_model.bin` but safe in the sense that no arbitrary code can be put into it. These files also happen to load much faster than their pytorch counterpart: https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb The widgets on your model page will run using this model even if this is not merged making sure the file actually works. If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions Feel free to ignore this PR.ccf51ac
-
Updating the code
3b2043c -
Upload 2 files
d4b5722 -
Adding model details.
e7ed86c -
initial commit
803a0f9