metadata
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- eur-lex-sum
metrics:
- rouge
model-index:
- name: my_legal_summarization_model
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: eur-lex-sum
type: eur-lex-sum
config: english
split: test
args: english
metrics:
- name: Rouge1
type: rouge
value: 0.2166
my_legal_summarization_model
This model is a fine-tuned version of t5-small on the eur-lex-sum dataset. It achieves the following results on the evaluation set:
- Loss: 2.1064
- Rouge1: 0.2166
- Rouge2: 0.1493
- Rougel: 0.1992
- Rougelsum: 0.1991
- Gen Len: 19.0
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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 71 | 2.3019 | 0.2114 | 0.1485 | 0.1934 | 0.1937 | 19.0 |
| No log | 2.0 | 142 | 2.1766 | 0.2156 | 0.1508 | 0.1987 | 0.1988 | 19.0 |
| No log | 3.0 | 213 | 2.1215 | 0.2161 | 0.1499 | 0.1988 | 0.1987 | 19.0 |
| No log | 4.0 | 284 | 2.1064 | 0.2166 | 0.1493 | 0.1992 | 0.1991 | 19.0 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0