Text Classification
Transformers
TensorBoard
Safetensors
English
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hartunka/tiny_bert_rand_5_v2_qnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hartunka/tiny_bert_rand_5_v2_qnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hartunka/tiny_bert_rand_5_v2_qnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hartunka/tiny_bert_rand_5_v2_qnli") model = AutoModelForSequenceClassification.from_pretrained("Hartunka/tiny_bert_rand_5_v2_qnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Hartunka/tiny_bert_rand_5_v2_qnli: direct link, hf CLI and curl.
- Browser
- Download file 2.11 kB
-
https://huggingface.co/Hartunka/tiny_bert_rand_5_v2_qnli/resolve/main/README.md
- Command line
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hf download hf://Hartunka/tiny_bert_rand_5_v2_qnli/README.md
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curl -L -o README.md https://huggingface.co/Hartunka/tiny_bert_rand_5_v2_qnli/resolve/main/README.md
2.11 kB
| library_name: transformers | |
| language: | |
| - en | |
| base_model: Hartunka/tiny_bert_rand_5_v2 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - glue | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: tiny_bert_rand_5_v2_qnli | |
| results: | |
| - task: | |
| name: Text Classification | |
| type: text-classification | |
| dataset: | |
| name: GLUE QNLI | |
| type: glue | |
| args: qnli | |
| metrics: | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.6267618524620172 | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # tiny_bert_rand_5_v2_qnli | |
| This model is a fine-tuned version of [Hartunka/tiny_bert_rand_5_v2](https://huggingface.co/Hartunka/tiny_bert_rand_5_v2) on the GLUE QNLI dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.6468 | |
| - Accuracy: 0.6268 | |
| ## 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 | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | 0.6655 | 1.0 | 410 | 0.6482 | 0.6207 | | |
| | 0.636 | 2.0 | 820 | 0.6468 | 0.6268 | | |
| | 0.5935 | 3.0 | 1230 | 0.6598 | 0.6231 | | |
| | 0.5345 | 4.0 | 1640 | 0.6996 | 0.6246 | | |
| | 0.4697 | 5.0 | 2050 | 0.7827 | 0.6143 | | |
| | 0.4077 | 6.0 | 2460 | 0.8890 | 0.6041 | | |
| | 0.3487 | 7.0 | 2870 | 1.0397 | 0.6042 | | |
| ### Framework versions | |
| - Transformers 4.50.2 | |
| - Pytorch 2.2.1+cu121 | |
| - Datasets 2.18.0 | |
| - Tokenizers 0.21.1 | |