Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("HCKLab/BiBert-Classification-1")
model = AutoModelForSequenceClassification.from_pretrained("HCKLab/BiBert-Classification-1")Quick Links
BiBert-Classification-1
This model is a fine-tuned version of nlptown/bert-base-multilingual-uncased-sentiment on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.7371
- Accuracy: 0.7881
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: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7927 | 1.0 | 4001 | 0.7371 | 0.7881 |
| 0.7234 | 2.0 | 8002 | 0.7999 | 0.7667 |
| 0.673 | 3.0 | 12003 | 0.7956 | 0.7709 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HCKLab/BiBert-Classification-1")