Fill-Mask
Transformers
ONNX
Safetensors
English
roberta
music
lilypond
mlm
music-information-retrieval
Eval Results (legacy)
Instructions to use csc-unipd/lilybert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use csc-unipd/lilybert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="csc-unipd/lilybert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("csc-unipd/lilybert") model = AutoModelForMaskedLM.from_pretrained("csc-unipd/lilybert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_config.json from csc-unipd/lilybert: direct link, hf CLI and curl.
- Browser
- Download file 1.6 kB
-
https://huggingface.co/csc-unipd/lilybert/resolve/main/training_config.json
- Command line
-
hf download hf://csc-unipd/lilybert/training_config.json
-
curl -L -o training_config.json https://huggingface.co/csc-unipd/lilybert/resolve/main/training_config.json
1.6 kB
| { | |
| "data_dir": "/nfsd/voce/machine_learning/experiments/artifacts/processed", | |
| "tokenizer_path": "/nfsd/voce/machine_learning/experiments/artifacts/tokenizer", | |
| "output_dir": "/nfsd/voce/machine_learning/experiments/cb-pdmx-bm", | |
| "model_architecture": "microsoft/codebert-base", | |
| "random_init": false, | |
| "hidden_size": 768, | |
| "num_hidden_layers": 12, | |
| "num_attention_heads": 12, | |
| "intermediate_size": 3072, | |
| "max_position_embeddings": 514, | |
| "max_length": 512, | |
| "mlm_probability": 0.15, | |
| "per_device_train_batch_size": 72, | |
| "per_device_eval_batch_size": 72, | |
| "num_train_epochs": 10, | |
| "learning_rate": 0.0002, | |
| "lr_scheduler_type": "cosine", | |
| "max_grad_norm": 1.0, | |
| "weight_decay": 0.01, | |
| "warmup_ratio": 0.1, | |
| "max_steps": -1, | |
| "logging_steps": 50, | |
| "eval_steps": 1000, | |
| "save_steps": 1000, | |
| "seed": 42, | |
| "pretokenized_shards_dir": "/nfsd/voce/machine_learning/experiments/codebert/baroque-music-shards/mlm", | |
| "resume_from_checkpoint": null, | |
| "dataloader_num_workers": 8, | |
| "early_stopping": true, | |
| "early_stopping_patience": 5, | |
| "early_stopping_threshold": 0.0, | |
| "bf16": true, | |
| "gradient_accumulation_steps": 2, | |
| "optim": "adamw_torch_fused", | |
| "dataloader_pin_memory": true, | |
| "dataloader_prefetch_factor": 4, | |
| "save_total_limit": 3, | |
| "torch_compile": false, | |
| "ddp_find_unused_parameters": false, | |
| "wandb_enabled": true, | |
| "wandb_project": "lilybert", | |
| "wandb_entity": null, | |
| "wandb_mode": "online", | |
| "wandb_run_name": "cb-pdmx-baroquemusic", | |
| "tensorboard_enabled": true, | |
| "tensorboard_log_dir": "/nfsd/voce/machine_learning/experiments/cb-pdmx-bm/tensorboard" | |
| } |