Instructions to use EslamAhmed/google_Job_data_tuned_trial_2_11-2-2022 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EslamAhmed/google_Job_data_tuned_trial_2_11-2-2022 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="EslamAhmed/google_Job_data_tuned_trial_2_11-2-2022")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("EslamAhmed/google_Job_data_tuned_trial_2_11-2-2022") model = AutoModelForMaskedLM.from_pretrained("EslamAhmed/google_Job_data_tuned_trial_2_11-2-2022") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- af7d499be2f49b730611b9c41c21443d4dd22ab488994a8e4ee65da5ab69c017
- Size of remote file:
- 433 MB
- SHA256:
- 73bd5c8676647ef13369a999f9661d9b4c2f50e02a04f752a91df7bc62659f6e
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