Instructions to use fia24/annotator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fia24/annotator with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("fia24/annotator") model = AutoModelForSeq2SeqLM.from_pretrained("fia24/annotator", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| base_model: csebuetnlp/banglat5 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: annotator | |
| 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. --> | |
| # annotator | |
| This model is a fine-tuned version of [csebuetnlp/banglat5](https://huggingface.co/csebuetnlp/banglat5) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - eval_loss: 0.1284 | |
| - eval_Val Accuracy: 0.9452 | |
| - eval_gen_len: 2.9605 | |
| - eval_runtime: 34.6661 | |
| - eval_samples_per_second: 95.771 | |
| - eval_steps_per_second: 3.0 | |
| - epoch: 6.0 | |
| - step: 618 | |
| ## 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: 0.0005 | |
| - train_batch_size: 260 | |
| - eval_batch_size: 32 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 50 | |
| ### Framework versions | |
| - Transformers 4.34.1 | |
| - Pytorch 1.13.1+cu117 | |
| - Datasets 2.14.6 | |
| - Tokenizers 0.14.1 | |