metadata
base_model: meta-llama/Llama-2-7b-hf
tags:
- generated_from_trainer
model-index:
- name: Llama2-7bn-xsum-adapter
results: []
datasets:
- EdinburghNLP/xsum
language:
- en
pipeline_tag: summarization
metrics:
- rouge
Llama2-7bn-xsum-adapter
Weights & Biases runs for training and evaluation are available for a detailed overview!
This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on a XSum dataset with Causal LM task. You can view all the implementation details on the GitHub project
Weights & Biases Training and Evaluation Documentation
See the training and evaluation on Weights & Biases for more details!
Summary table of final metrics:
| Metric | rouge1 | rouge2 | rougeL | FactCC | ANLI | SummaC | BARTScore |
|---|---|---|---|---|---|---|---|
| Mean | 0.18 | 0.033 | 0.126 | 0.188 | 0.408 | 0.658 | -3.713 |
| Std | 0.09 | 0.049 | 0.067 | 0.317 | 0.462 | 0.247 | 0.831 |
Training procedure
Causal language modeling. Nesting the summary paragraph in a prompt: {Summarize this article: ''; Summary:
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 450.5
- num_epochs: 3
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.35.0
- Pytorch 2.0.1
- Datasets 2.14.6
- Tokenizers 0.14.1