Instructions to use varun-v-rao/opt-350m-snli-model2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use varun-v-rao/opt-350m-snli-model2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="varun-v-rao/opt-350m-snli-model2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("varun-v-rao/opt-350m-snli-model2") model = AutoModelForSequenceClassification.from_pretrained("varun-v-rao/opt-350m-snli-model2") - Notebooks
- Google Colab
- Kaggle
opt-350m-snli-model2
This model is a fine-tuned version of facebook/opt-350m on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7931
- Accuracy: 0.751
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: 256
- eval_batch_size: 256
- seed: 10
- 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 |
|---|---|---|---|---|
| 0.3296 | 1.0 | 2146 | 0.2628 | 0.9053 |
| 0.2382 | 2.0 | 4292 | 0.2587 | 0.9088 |
| 0.153 | 3.0 | 6438 | 0.3031 | 0.9088 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for varun-v-rao/opt-350m-snli-model2
Base model
facebook/opt-350m