Instructions to use vickt/LLM_Teached_BART_CNNDM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vickt/LLM_Teached_BART_CNNDM with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("vickt/LLM_Teached_BART_CNNDM") model = AutoModelForSeq2SeqLM.from_pretrained("vickt/LLM_Teached_BART_CNNDM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| base_model: facebook/bart-large-cnn | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - rouge | |
| model-index: | |
| - name: LLM_Teached_BART_CNNDM | |
| 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. --> | |
| # LLM_Teached_BART_CNNDM | |
| This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.6578 | |
| - Rouge1: 0.4481 | |
| - Rouge2: 0.1889 | |
| - Rougel: 0.319 | |
| - Rougelsum: 0.3189 | |
| - Gen Len: 80.76 | |
| ## 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: 16 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 2 | |
| - total_train_batch_size: 32 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 2 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | |
| | 1.705 | 1.0 | 625 | 1.6702 | 0.4428 | 0.1879 | 0.3156 | 0.3154 | 82.54 | | |
| | 1.4845 | 2.0 | 1250 | 1.6578 | 0.4481 | 0.1889 | 0.319 | 0.3189 | 80.76 | | |
| ### Framework versions | |
| - Transformers 4.36.0 | |
| - Pytorch 2.0.1+cu117 | |
| - Datasets 2.14.5 | |
| - Tokenizers 0.15.0 | |