# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("YaHi/teacher_electra_small_building_binary")
model = AutoModelForSequenceClassification.from_pretrained("YaHi/teacher_electra_small_building_binary")Quick Links
teacher_electra_small_building_binary
This model is a fine-tuned version of google/electra-small-discriminator on an unknown dataset.
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: 16
- eval_batch_size: 16
- seed: 1022
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 4
Training results
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0
- Datasets 2.12.0
- Tokenizers 0.13.2
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Model tree for YaHi/teacher_electra_small_building_binary
Base model
google/electra-small-discriminator
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YaHi/teacher_electra_small_building_binary")