tags: | |
- summarization | |
language: | |
- it | |
metrics: | |
- rouge | |
model-index: | |
- name: summarization_ilpost | |
results: | |
- task: | |
type: summarization | |
name: Summarization | |
dataset: | |
name: xsum | |
type: xsum | |
config: default | |
split: test | |
metrics: | |
- name: ROUGE-1 | |
type: rouge | |
value: 12.1495 | |
verified: true | |
- name: ROUGE-2 | |
type: rouge | |
value: 1.6326 | |
verified: true | |
- name: ROUGE-L | |
type: rouge | |
value: 10.493 | |
verified: true | |
- name: ROUGE-LSUM | |
type: rouge | |
value: 10.5515 | |
verified: true | |
- name: loss | |
type: loss | |
value: 2.5110762119293213 | |
verified: true | |
- name: gen_len | |
type: gen_len | |
value: 18.9924 | |
verified: true | |
datasets: | |
- ARTeLab/ilpost | |
# summarization_ilpost | |
This model is a fine-tuned version of [gsarti/it5-base](https://huggingface.co/gsarti/it5-base) on IlPost dataset for Abstractive Summarization. | |
It achieves the following results: | |
- Loss: 1.6020 | |
- Rouge1: 33.7802 | |
- Rouge2: 16.2953 | |
- Rougel: 27.4797 | |
- Rougelsum: 30.2273 | |
- Gen Len: 45.3175 | |
## Usage | |
```python | |
from transformers import T5Tokenizer, T5ForConditionalGeneration | |
tokenizer = T5Tokenizer.from_pretrained("ARTeLab/it5-summarization-ilpost") | |
model = T5ForConditionalGeneration.from_pretrained("ARTeLab/it5-summarization-ilpost") | |
``` | |
### Training hyperparameters | |
The following hyperparameters were used during training: | |
- learning_rate: 5e-05 | |
- train_batch_size: 6 | |
- eval_batch_size: 6 | |
- seed: 42 | |
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
- lr_scheduler_type: linear | |
- num_epochs: 4.0 | |
### Framework versions | |
- Transformers 4.12.0.dev0 | |
- Pytorch 1.9.1+cu102 | |
- Datasets 1.12.1 | |
- Tokenizers 0.10.3 |