Instructions to use arbitropy/bcoqa-bt5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arbitropy/bcoqa-bt5 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("arbitropy/bcoqa-bt5") model = AutoModelForSeq2SeqLM.from_pretrained("arbitropy/bcoqa-bt5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| base_model: csebuetnlp/banglat5 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: bcoqa-bt5 | |
| results: [] | |
| <!-- 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. --> | |
| # bcoqa-bt5 | |
| This model is a fine-tuned version of [csebuetnlp/banglat5](https://huggingface.co/csebuetnlp/banglat5) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.4858 | |
| ## 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: 5 | |
| - eval_batch_size: 5 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 2 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:-----:|:---------------:| | |
| | 2.2574 | 0.36 | 10000 | 1.8188 | | |
| | 1.9623 | 0.72 | 20000 | 1.5883 | | |
| | 1.7387 | 1.08 | 30000 | 1.5452 | | |
| | 1.7283 | 1.44 | 40000 | 1.5080 | | |
| | 1.7291 | 1.8 | 50000 | 1.4858 | | |
| ### Framework versions | |
| - Transformers 4.37.2 | |
| - Pytorch 2.1.1+cu121 | |
| - Datasets 2.16.1 | |
| - Tokenizers 0.15.1 | |