Instructions to use xiaothung/t5-base-bt3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xiaothung/t5-base-bt3 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("xiaothung/t5-base-bt3") model = AutoModelForSeq2SeqLM.from_pretrained("xiaothung/t5-base-bt3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download trainer_state.json from xiaothung/t5-base-bt3: direct link, hf CLI and curl.
- Browser
- Download file 1.56 kB
-
https://huggingface.co/xiaothung/t5-base-bt3/resolve/main/trainer_state.json
- Command line
-
hf download hf://xiaothung/t5-base-bt3/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/xiaothung/t5-base-bt3/resolve/main/trainer_state.json
1.56 kB
| { | |
| "best_metric": 11.0454, | |
| "best_model_checkpoint": "/content/gdrive/MyDrive/FYP/Models/t5-base-bt3/checkpoint-200", | |
| "epoch": 2.2988505747126435, | |
| "eval_steps": 100, | |
| "global_step": 200, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 1.15, | |
| "grad_norm": 0.2406858205795288, | |
| "learning_rate": 1.850574712643678e-05, | |
| "loss": 0.212, | |
| "step": 100 | |
| }, | |
| { | |
| "epoch": 1.15, | |
| "eval_gen_len": 9.4468, | |
| "eval_loss": 0.18196970224380493, | |
| "eval_rouge1": 9.6492, | |
| "eval_rouge2": 0.3273, | |
| "eval_rougeL": 9.4017, | |
| "eval_rougeLsum": 9.513, | |
| "eval_runtime": 7.4325, | |
| "eval_samples_per_second": 6.324, | |
| "eval_steps_per_second": 0.807, | |
| "step": 100 | |
| }, | |
| { | |
| "epoch": 2.3, | |
| "grad_norm": 0.25088784098625183, | |
| "learning_rate": 7.011494252873563e-06, | |
| "loss": 0.1855, | |
| "step": 200 | |
| }, | |
| { | |
| "epoch": 2.3, | |
| "eval_gen_len": 8.8511, | |
| "eval_loss": 0.1775360256433487, | |
| "eval_rouge1": 11.0454, | |
| "eval_rouge2": 0.7529, | |
| "eval_rougeL": 10.8168, | |
| "eval_rougeLsum": 10.8709, | |
| "eval_runtime": 5.0258, | |
| "eval_samples_per_second": 9.352, | |
| "eval_steps_per_second": 1.194, | |
| "step": 200 | |
| } | |
| ], | |
| "logging_steps": 100, | |
| "max_steps": 261, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 3, | |
| "save_steps": 100, | |
| "total_flos": 971896793333760.0, | |
| "train_batch_size": 8, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |