Instructions to use EslamAhmed/google_Job_data_tuned_trial_1 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_1 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_1")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("EslamAhmed/google_Job_data_tuned_trial_1") model = AutoModelForMaskedLM.from_pretrained("EslamAhmed/google_Job_data_tuned_trial_1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8a7c5b75dba764c8b476638ac9356cc2bcb79362ea7e85b4b0ec9d3cb2eb2987
- Size of remote file:
- 433 MB
- SHA256:
- ad6c5622b7798cb6239368d5ffe161cd2e3f2aea8e0fd20c77028c9793a9556c
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