Instructions to use namin0202/pna-roberta-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use namin0202/pna-roberta-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="namin0202/pna-roberta-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("namin0202/pna-roberta-large") model = AutoModel.from_pretrained("namin0202/pna-roberta-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download trainer_state.json from namin0202/pna-roberta-large: direct link, hf CLI and curl.
- Browser
- Download file 1.65 kB
-
https://huggingface.co/namin0202/pna-roberta-large/resolve/main/trainer_state.json
- Command line
-
hf download hf://namin0202/pna-roberta-large/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/namin0202/pna-roberta-large/resolve/main/trainer_state.json
1.65 kB
| { | |
| "best_metric": 0.8797919434527426, | |
| "best_model_checkpoint": "result/clhaif-roberta-large-refse", | |
| "epoch": 0.6963788300835655, | |
| "global_step": 750, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.12, | |
| "eval_avg_sts": 0.8064963302246256, | |
| "eval_sickr_spearman": 0.762082012728511, | |
| "eval_stsb_spearman": 0.8509106477207403, | |
| "step": 125 | |
| }, | |
| { | |
| "epoch": 0.23, | |
| "eval_avg_sts": 0.8452781032001684, | |
| "eval_sickr_spearman": 0.8198200556739618, | |
| "eval_stsb_spearman": 0.8707361507263751, | |
| "step": 250 | |
| }, | |
| { | |
| "epoch": 0.35, | |
| "eval_avg_sts": 0.8271010163591597, | |
| "eval_sickr_spearman": 0.7789582685609077, | |
| "eval_stsb_spearman": 0.8752437641574118, | |
| "step": 375 | |
| }, | |
| { | |
| "epoch": 0.46, | |
| "learning_rate": 4.226245744351594e-05, | |
| "loss": 7.5855, | |
| "step": 500 | |
| }, | |
| { | |
| "epoch": 0.46, | |
| "eval_avg_sts": 0.8293696081597453, | |
| "eval_sickr_spearman": 0.7859766211341461, | |
| "eval_stsb_spearman": 0.8727625951853443, | |
| "step": 500 | |
| }, | |
| { | |
| "epoch": 0.58, | |
| "eval_avg_sts": 0.838054856261987, | |
| "eval_sickr_spearman": 0.8025079656271157, | |
| "eval_stsb_spearman": 0.8736017468968582, | |
| "step": 625 | |
| }, | |
| { | |
| "epoch": 0.7, | |
| "eval_avg_sts": 0.8370776376044988, | |
| "eval_sickr_spearman": 0.794363331756255, | |
| "eval_stsb_spearman": 0.8797919434527426, | |
| "step": 750 | |
| } | |
| ], | |
| "max_steps": 3231, | |
| "num_train_epochs": 3, | |
| "total_flos": 0, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |