Text Classification
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distilbert
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Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_km_5_v1_qnli")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_km_5_v1_qnli")Quick Links
tiny_bert_km_5_v1_qnli
This model is a fine-tuned version of Hartunka/tiny_bert_km_5_v1 on the GLUE QNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.5835
- Accuracy: 0.6994
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: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 10
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6651 | 1.0 | 410 | 0.6433 | 0.6187 |
| 0.63 | 2.0 | 820 | 0.6245 | 0.6427 |
| 0.5711 | 3.0 | 1230 | 0.5835 | 0.6994 |
| 0.4752 | 4.0 | 1640 | 0.5910 | 0.7082 |
| 0.3883 | 5.0 | 2050 | 0.6259 | 0.7130 |
| 0.3147 | 6.0 | 2460 | 0.6885 | 0.7068 |
| 0.2538 | 7.0 | 2870 | 0.8067 | 0.6972 |
| 0.2075 | 8.0 | 3280 | 0.8747 | 0.6996 |
Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1
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Model tree for Hartunka/tiny_bert_km_5_v1_qnli
Base model
Hartunka/tiny_bert_km_5_v1Dataset used to train Hartunka/tiny_bert_km_5_v1_qnli
Evaluation results
- Accuracy on GLUE QNLIself-reported0.699
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/tiny_bert_km_5_v1_qnli")