Instructions to use muhtasham/tiny-mlm-snli-target-glue-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use muhtasham/tiny-mlm-snli-target-glue-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="muhtasham/tiny-mlm-snli-target-glue-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("muhtasham/tiny-mlm-snli-target-glue-mnli") model = AutoModelForSequenceClassification.from_pretrained("muhtasham/tiny-mlm-snli-target-glue-mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
tiny-mlm-snli-target-glue-mnli
This model is a fine-tuned version of muhtasham/tiny-mlm-snli on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.8486
- Accuracy: 0.6182
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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 5000
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.0808 | 0.04 | 500 | 1.0435 | 0.4546 |
| 1.02 | 0.08 | 1000 | 0.9815 | 0.5241 |
| 0.9788 | 0.12 | 1500 | 0.9346 | 0.5770 |
| 0.945 | 0.16 | 2000 | 0.9104 | 0.5881 |
| 0.9291 | 0.2 | 2500 | 0.8902 | 0.6004 |
| 0.9182 | 0.24 | 3000 | 0.8789 | 0.6028 |
| 0.9033 | 0.29 | 3500 | 0.8707 | 0.6121 |
| 0.8967 | 0.33 | 4000 | 0.8585 | 0.6143 |
| 0.8804 | 0.37 | 4500 | 0.8564 | 0.6164 |
| 0.886 | 0.41 | 5000 | 0.8486 | 0.6182 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
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