AlekseyKorshuk
commited on
Commit
•
1a17623
1
Parent(s):
2fab1cf
huggingartists
Browse files- README.md +3 -3
- config.json +1 -1
- evaluation.txt +1 -1
- flax_model.msgpack +1 -1
- optimizer.pt +2 -2
- pytorch_model.bin +1 -1
- rng_state.pth +1 -1
- scheduler.pt +1 -1
- trainer_state.json +653 -7
- training_args.bin +2 -2
README.md
CHANGED
@@ -45,15 +45,15 @@ from datasets import load_dataset
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dataset = load_dataset("huggingartists/queen")
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```
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-
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/
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## Training procedure
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The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Queen's lyrics.
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-
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/
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-
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/
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## How to use
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dataset = load_dataset("huggingartists/queen")
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```
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+
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1v5o4ijc/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
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## Training procedure
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The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Queen's lyrics.
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+
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2apdiv6y) for full transparency and reproducibility.
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+
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2apdiv6y/artifacts) is logged and versioned.
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## How to use
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config.json
CHANGED
@@ -35,7 +35,7 @@
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}
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},
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"torch_dtype": "float32",
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-
"transformers_version": "4.
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"use_cache": true,
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"vocab_size": 50257
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}
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}
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},
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"torch_dtype": "float32",
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+
"transformers_version": "4.11.3",
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"use_cache": true,
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"vocab_size": 50257
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}
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evaluation.txt
CHANGED
@@ -1 +1 @@
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-
{"eval_loss":
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+
{"eval_loss": 1.775342345237732, "eval_runtime": 3.9684, "eval_samples_per_second": 21.923, "eval_steps_per_second": 2.772, "epoch": 12.0}
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flax_model.msgpack
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optimizer.pt
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pytorch_model.bin
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rng_state.pth
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scheduler.pt
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trainer_state.json
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{
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"eval_samples_per_second": 22.039,
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"eval_steps_per_second": 2.784,
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"step": 136
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