metadata

license: mit
base_model: unicamp-dl/ptt5-base-portuguese-vocab
tags:
  - generated_from_trainer
metrics:
  - rouge
model-index:
  - name: ptt5-cstnews
    results: []

ptt5-cstnews

This model is a fine-tuned version of unicamp-dl/ptt5-base-portuguese-vocab on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.2824
Rouge1: 0.1798
Rouge2: 0.1214
Rougel: 0.1629
Rougelsum: 0.1734
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: 2
eval_batch_size: 2
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
No log	1.0	88	3.1376	0.1326	0.089	0.1198	0.1274	19.0
No log	2.0	176	2.5907	0.1782	0.1106	0.1507	0.169	19.0
3.838	3.0	264	2.4840	0.1816	0.1192	0.1607	0.1747	19.0
3.838	4.0	352	2.4281	0.1784	0.117	0.1605	0.1725	19.0
2.4977	5.0	440	2.3860	0.1757	0.1138	0.158	0.1697	19.0
2.4977	6.0	528	2.3611	0.18	0.121	0.1628	0.1744	19.0
2.3101	7.0	616	2.3437	0.1792	0.1203	0.1625	0.1733	19.0
2.3101	8.0	704	2.3327	0.1791	0.121	0.1625	0.1734	19.0
2.3101	9.0	792	2.3165	0.1795	0.1217	0.1625	0.1741	19.0
2.1814	10.0	880	2.3109	0.1772	0.1186	0.1598	0.1711	19.0
2.1814	11.0	968	2.2978	0.1785	0.1201	0.1611	0.1726	19.0
2.1193	12.0	1056	2.2923	0.1792	0.1204	0.1618	0.1733	19.0
2.1193	13.0	1144	2.2958	0.1789	0.1204	0.1613	0.1729	19.0
2.0126	14.0	1232	2.2870	0.1785	0.1204	0.161	0.1725	19.0
2.0126	15.0	1320	2.2872	0.1789	0.1204	0.1613	0.1728	19.0
1.9237	16.0	1408	2.2799	0.1792	0.1204	0.1618	0.1733	19.0
1.9237	17.0	1496	2.2825	0.1787	0.1219	0.1626	0.1732	19.0
1.9237	18.0	1584	2.2788	0.1787	0.1219	0.1626	0.1729	19.0
1.9157	19.0	1672	2.2787	0.1784	0.1215	0.1622	0.1727	19.0
1.9157	20.0	1760	2.2835	0.1776	0.1211	0.1614	0.1721	19.0
1.8614	21.0	1848	2.2785	0.1808	0.1218	0.1636	0.1752	19.0
1.8614	22.0	1936	2.2823	0.1795	0.1214	0.1626	0.1732	19.0
1.8565	23.0	2024	2.2774	0.1798	0.1214	0.1629	0.1734	19.0
1.8565	24.0	2112	2.2797	0.1798	0.1214	0.1629	0.1734	19.0
1.8076	25.0	2200	2.2818	0.1798	0.1214	0.1629	0.1734	19.0
1.8076	26.0	2288	2.2825	0.1795	0.1214	0.1626	0.1732	19.0
1.8076	27.0	2376	2.2825	0.1795	0.1214	0.1626	0.1732	19.0
1.7745	28.0	2464	2.2823	0.1798	0.1214	0.1629	0.1734	19.0
1.7745	29.0	2552	2.2829	0.1795	0.1214	0.1626	0.1732	19.0
1.8083	30.0	2640	2.2824	0.1798	0.1214	0.1629	0.1734	19.0

Framework versions

Transformers 4.34.0
Pytorch 2.0.1+cu117
Datasets 2.14.5
Tokenizers 0.14.1