huggingartists

README.md

@@ -45,15 +45,15 @@ from datasets import load_dataset
 dataset = load_dataset("huggingartists/eminem")
 ```
 
-[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2s6aknel/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
+[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3q37f1zd/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
 
 ## Training procedure
 
 The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Eminem's lyrics.
 
-Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3kcljo2y) for full transparency and reproducibility.
+Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3u62y0tb) for full transparency and reproducibility.
 
-At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3kcljo2y/artifacts) is logged and versioned.
+At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3u62y0tb/artifacts) is logged and versioned.
 
 ## How to use
 

evaluation.txt

@@ -1 +1 @@
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+{"eval_loss": 1.000430703163147, "eval_runtime": 28.8376, "eval_samples_per_second": 22.644, "eval_steps_per_second": 2.844, "epoch": 2.0}

flax_model.msgpack

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optimizer.pt

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rng_state.pth

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scheduler.pt

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trainer_state.json

@@ -1,8 +1,8 @@
 {
-  "best_metric": 1.3114155530929565,
-  "best_model_checkpoint": "output/eminem/checkpoint-453",
+  "best_metric": 1.000430703163147,
+  "best_model_checkpoint": "output/eminem/checkpoint-454",
   "epoch": 1.0,
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@@ -554,11 +554,19 @@
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+      "step": 454
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+  "num_train_epochs": 2,
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   "trial_params": null
 }

training_args.bin

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