Instructions to use nornor02/working with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nornor02/working with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nornor02/working")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("nornor02/working") model = AutoModelForMaskedLM.from_pretrained("nornor02/working", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 1000
Browse files- model.safetensors +1 -1
- runs/Jan18_00-41-31_455f56c2d2db/events.out.tfevents.1705538534.455f56c2d2db.26.0 +2 -2
- wandb/debug-internal.log +38 -0
- wandb/run-20240118_004236-6kmvcnsc/files/output.log +2 -0
- wandb/run-20240118_004236-6kmvcnsc/files/wandb-summary.json +1 -1
- wandb/run-20240118_004236-6kmvcnsc/logs/debug-internal.log +38 -0
model.safetensors
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You're using a PreTrainedTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
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{"train/loss": 2.1182, "train/learning_rate": 1.230769230769231e-05, "train/epoch": 76.92, "train/global_step": 1000, "_timestamp": 1705538750.0786831, "_runtime": 193.26640915870667, "_step": 1}
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