tiagoblima/qg_squad_v1_pt
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How to use tiagoblima/t5_small-qg-aap with Transformers:
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("tiagoblima/t5_small-qg-aap")
model = AutoModelForSeq2SeqLM.from_pretrained("tiagoblima/t5_small-qg-aap", device_map="auto")This model is a fine-tuned version of unicamp-dl/ptt5-small-t5-vocab on the tiagoblima/qg_squad_v1_pt dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 6.4555 | 1.0 | 808 | 8.1815 |
| 5.7684 | 2.0 | 1616 | 7.3459 |
| 5.3707 | 3.0 | 2424 | 6.8522 |
| 5.1609 | 4.0 | 3232 | 6.5931 |
| 5.1034 | 5.0 | 4040 | 6.5105 |
Base model
unicamp-dl/ptt5-small-t5-vocab