Instructions to use promforge/working with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use promforge/working with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="promforge/working")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("promforge/working") model = AutoModelForSequenceClassification.from_pretrained("promforge/working", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Sharuka Thirimanne commited on
Training in progress, epoch 3
Browse files- model.safetensors +1 -1
- runs/Apr10_15-06-31_9088970cc926/events.out.tfevents.1712761592.9088970cc926.34.1 +2 -2
- wandb/debug-internal.log +39 -0
- wandb/run-20240410_150123-cuua991r/files/output.log +2 -0
- wandb/run-20240410_150123-cuua991r/files/wandb-summary.json +1 -1
- wandb/run-20240410_150123-cuua991r/logs/debug-internal.log +39 -0
model.safetensors
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{"train/loss": 1.8499, "train/grad_norm": 8.081238746643066, "train/learning_rate": 7.016949152542374e-06, "train/epoch": 3.0, "train/global_step": 354, "_timestamp": 1712761773.6414328, "_runtime": 490.09070086479187, "_step": 13, "eval/loss": 1.8500417470932007, "eval/accuracy": 0.3028169014084507, "eval/precision": 0.15226251402682664, "eval/recall": 0.3028169014084507, "eval/f1": 0.19308999089624623, "eval/runtime": 2.8399, "eval/samples_per_second": 50.001, "eval/steps_per_second": 6.338}
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