Text Classification
Transformers
PyTorch
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use keremp/deberta-em with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use keremp/deberta-em with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="keremp/deberta-em")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("keremp/deberta-em") model = AutoModelForSequenceClassification.from_pretrained("keremp/deberta-em", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download trainer_state.json from keremp/deberta-em: direct link, hf CLI and curl.
- Browser
- Download file 671 Bytes
-
https://huggingface.co/keremp/deberta-em/resolve/main/trainer_state.json
- Command line
-
hf download hf://keremp/deberta-em/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/keremp/deberta-em/resolve/main/trainer_state.json
671 Bytes
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 2.980891719745223, | |
| "eval_steps": 500, | |
| "global_step": 234, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 2.98, | |
| "step": 234, | |
| "total_flos": 494194942541824.0, | |
| "train_loss": 0.20921571845682257, | |
| "train_runtime": 478.8167, | |
| "train_samples_per_second": 31.39, | |
| "train_steps_per_second": 0.489 | |
| } | |
| ], | |
| "logging_steps": 500, | |
| "max_steps": 234, | |
| "num_train_epochs": 3, | |
| "save_steps": 500, | |
| "total_flos": 494194942541824.0, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |