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/bert_base_rand_5_v2_qnli")
model = AutoModelForSequenceClassification.from_pretrained("Hartunka/bert_base_rand_5_v2_qnli")Quick Links
bert_base_rand_5_v2_qnli
This model is a fine-tuned version of Hartunka/bert_base_rand_5_v2 on the GLUE QNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.6277
- Accuracy: 0.6414
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 |
|---|---|---|---|---|
| 0.6613 | 1.0 | 410 | 0.6415 | 0.6249 |
| 0.6213 | 2.0 | 820 | 0.6277 | 0.6414 |
| 0.5509 | 3.0 | 1230 | 0.6466 | 0.6365 |
| 0.4463 | 4.0 | 1640 | 0.7136 | 0.6511 |
| 0.3322 | 5.0 | 2050 | 0.7906 | 0.6625 |
| 0.2362 | 6.0 | 2460 | 0.9955 | 0.6469 |
| 0.1685 | 7.0 | 2870 | 1.0700 | 0.6546 |
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/bert_base_rand_5_v2_qnli
Base model
Hartunka/bert_base_rand_5_v2Dataset used to train Hartunka/bert_base_rand_5_v2_qnli
Evaluation results
- Accuracy on GLUE QNLIself-reported0.641
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/bert_base_rand_5_v2_qnli")