Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use henryscheible/wnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use henryscheible/wnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="henryscheible/wnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("henryscheible/wnli") model = AutoModelForSequenceClassification.from_pretrained("henryscheible/wnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download trainer_state.json from henryscheible/wnli: direct link, hf CLI and curl.
- Browser
- Download file 589 Bytes
-
https://huggingface.co/henryscheible/wnli/resolve/main/trainer_state.json
- Command line
-
hf download hf://henryscheible/wnli/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/henryscheible/wnli/resolve/main/trainer_state.json
589 Bytes
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 5.0, | |
| "global_step": 100, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 5.0, | |
| "step": 100, | |
| "total_flos": 208844400192000.0, | |
| "train_loss": 0.6987480926513672, | |
| "train_runtime": 48.1051, | |
| "train_samples_per_second": 66.001, | |
| "train_steps_per_second": 2.079 | |
| } | |
| ], | |
| "max_steps": 100, | |
| "num_train_epochs": 5, | |
| "total_flos": 208844400192000.0, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |