learn3r/gov_report_memsum_bp
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How to use learn3r/longt5_xl_gov_memsum_bp_5 with Transformers:
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("learn3r/longt5_xl_gov_memsum_bp_5")
model = AutoModelForSeq2SeqLM.from_pretrained("learn3r/longt5_xl_gov_memsum_bp_5", device_map="auto")This model is a fine-tuned version of google/long-t5-tglobal-xl on the learn3r/gov_report_memsum_bp 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 1.1562 | 1.0 | 272 | 1.0105 | 37.2934 | 18.6683 | 24.0563 | 35.6575 | 1844.1543 |
| 0.9737 | 2.0 | 545 | 0.9813 | 55.1149 | 30.149 | 31.9694 | 52.9549 | 1101.6060 |
| 0.8395 | 3.0 | 818 | 0.9925 | 57.4498 | 31.9315 | 32.914 | 55.2389 | 1055.9784 |
| 0.7353 | 4.0 | 1091 | 1.0404 | 67.3946 | 39.2034 | 36.8583 | 64.9879 | 829.2881 |
| 0.6212 | 4.99 | 1360 | 1.0752 | 64.5433 | 36.9477 | 35.3482 | 62.2005 | 779.6152 |
Base model
google/long-t5-tglobal-xl