Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("dudizi/he-router")
model = AutoModelForSequenceClassification.from_pretrained("dudizi/he-router", device_map="auto")Quick Links
results
This model is a fine-tuned version of dicta-il/dictabert on the None dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.3578
- eval_accuracy: 0.9698
- eval_runtime: 5.3281
- eval_samples_per_second: 434.492
- eval_steps_per_second: 6.944
- epoch: 6.0
- step: 870
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- label_smoothing_factor: 0.1
Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
- Downloads last month
- 4
Model tree for dudizi/he-router
Base model
dicta-il/dictabert
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dudizi/he-router")