Instructions to use anandNakat/bart_math_solver_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anandNakat/bart_math_solver_2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("anandNakat/bart_math_solver_2") model = AutoModelForSeq2SeqLM.from_pretrained("anandNakat/bart_math_solver_2") - Notebooks
- Google Colab
- Kaggle
bart_math_solver_2
This model is a fine-tuned version of facebook/bart-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6739
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.6678 | 1.0 | 221 | 0.6366 |
| 0.6333 | 2.0 | 442 | 0.6897 |
| 0.612 | 3.0 | 663 | 0.6775 |
| 0.5361 | 4.0 | 884 | 0.6384 |
| 0.5411 | 5.0 | 1105 | 0.6976 |
| 0.5831 | 6.0 | 1326 | 0.6655 |
| 0.5733 | 7.0 | 1547 | 0.6790 |
| 0.5658 | 8.0 | 1768 | 0.6739 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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Model tree for anandNakat/bart_math_solver_2
Base model
facebook/bart-large