Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use sibstrider/rubert-tiny2-finetuned-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sibstrider/rubert-tiny2-finetuned-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sibstrider/rubert-tiny2-finetuned-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sibstrider/rubert-tiny2-finetuned-classification") model = AutoModelForSequenceClassification.from_pretrained("sibstrider/rubert-tiny2-finetuned-classification") - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_metric": 0.1836734693877551, | |
| "best_model_checkpoint": "rubert-tiny2-finetuned-classification\\run-1\\checkpoint-69", | |
| "epoch": 1.0, | |
| "eval_steps": 500, | |
| "global_step": 69, | |
| "is_hyper_param_search": true, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 1.0, | |
| "eval_accuracy": 0.1836734693877551, | |
| "eval_loss": 2.384721279144287, | |
| "eval_runtime": 1.179, | |
| "eval_samples_per_second": 207.804, | |
| "eval_steps_per_second": 13.571, | |
| "step": 69 | |
| } | |
| ], | |
| "logging_steps": 500, | |
| "max_steps": 138, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 2, | |
| "save_steps": 500, | |
| "total_flos": 0, | |
| "train_batch_size": 32, | |
| "trial_name": null, | |
| "trial_params": { | |
| "learning_rate": 1.0017079803752642e-06, | |
| "num_train_epochs": 2, | |
| "per_device_train_batch_size": 32, | |
| "seed": 24 | |
| } | |
| } | |