Instructions to use EslamAhmed/customer_data_tuned_trial_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EslamAhmed/customer_data_tuned_trial_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="EslamAhmed/customer_data_tuned_trial_1")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("EslamAhmed/customer_data_tuned_trial_1") model = AutoModelForMaskedLM.from_pretrained("EslamAhmed/customer_data_tuned_trial_1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4e87072d264e8e50041dba58e88a9f46769f0eebb54dfe48be771e227419f1bb
- Size of remote file:
- 433 MB
- SHA256:
- 7312a9159fe459222aba968e18e32c83924364113c189083d717749dba0684aa
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