Text Classification
Transformers
PyTorch
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use tmnam20/videberta-base_1024 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tmnam20/videberta-base_1024 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tmnam20/videberta-base_1024")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tmnam20/videberta-base_1024") model = AutoModelForSequenceClassification.from_pretrained("tmnam20/videberta-base_1024", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-100/trainer_state.json from tmnam20/videberta-base_1024: direct link, hf CLI and curl.
- Browser
- Download file 1.25 kB
-
https://huggingface.co/tmnam20/videberta-base_1024/resolve/main/checkpoint-100/trainer_state.json
- Command line
-
hf download hf://tmnam20/videberta-base_1024/checkpoint-100/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/tmnam20/videberta-base_1024/resolve/main/checkpoint-100/trainer_state.json
1.25 kB
| { | |
| "best_metric": 0.5696164965629578, | |
| "best_model_checkpoint": "/kaggle/output/checkpoint-100", | |
| "epoch": 0.2066115702479339, | |
| "eval_steps": 50, | |
| "global_step": 100, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.0, | |
| "learning_rate": 0.0, | |
| "loss": 0.7246, | |
| "step": 1 | |
| }, | |
| { | |
| "epoch": 0.1, | |
| "learning_rate": 7.333333333333332e-05, | |
| "loss": 0.5982, | |
| "step": 50 | |
| }, | |
| { | |
| "epoch": 0.1, | |
| "eval_accuracy": 0.75, | |
| "eval_loss": 0.6296697854995728, | |
| "eval_runtime": 30.5612, | |
| "eval_samples_per_second": 38.742, | |
| "eval_steps_per_second": 19.371, | |
| "step": 50 | |
| }, | |
| { | |
| "epoch": 0.21, | |
| "learning_rate": 0.00015, | |
| "loss": 0.5505, | |
| "step": 100 | |
| }, | |
| { | |
| "epoch": 0.21, | |
| "eval_accuracy": 0.75, | |
| "eval_loss": 0.5696164965629578, | |
| "eval_runtime": 30.5816, | |
| "eval_samples_per_second": 38.716, | |
| "eval_steps_per_second": 19.358, | |
| "step": 100 | |
| } | |
| ], | |
| "logging_steps": 50, | |
| "max_steps": 1000, | |
| "num_train_epochs": 3, | |
| "save_steps": 100, | |
| "total_flos": 348571871706840.0, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |