learn3r/summ_screen_fd_bp
Viewer • Updated • 4.35k • 14
How to use learn3r/longt5_xl_summ_screen_bp_only_30 with Transformers:
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("learn3r/longt5_xl_summ_screen_bp_only_30")
model = AutoModelForSeq2SeqLM.from_pretrained("learn3r/longt5_xl_summ_screen_bp_only_30")YAML Metadata Error:"base_model" with value "/exports/eddie/scratch/s1970716/models/summarization/longt5_xl_summ_screen_bp_only/checkpoint-210" is not valid. Use a model id from https://hf.co/models.
This model is a fine-tuned version of /exports/eddie/scratch/s1970716/models/summarization/longt5_xl_summ_screen_bp_only/checkpoint-210 on the learn3r/summ_screen_fd_bp dataset. It achieves the following results on the evaluation set:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 0.324 | 0.97 | 14 | 2.2376 | 40.4388 | 16.4662 | 28.0771 | 38.3405 | 246.7396 |
| 0.2707 | 1.95 | 28 | 2.3204 | 40.2873 | 16.7641 | 27.3895 | 38.2689 | 307.3787 |
| 0.2217 | 2.99 | 43 | 2.5281 | 31.9916 | 13.8136 | 22.1895 | 30.623 | 501.9320 |
| 0.1776 | 3.97 | 57 | 2.7530 | 31.7535 | 13.8852 | 22.8653 | 30.3796 | 489.6183 |
| 0.1424 | 4.94 | 71 | 2.6578 | 32.117 | 14.2141 | 22.3733 | 30.8328 | 502.1124 |
| 0.1449 | 5.98 | 86 | 2.5508 | 35.3448 | 13.8478 | 24.9044 | 33.6108 | 357.3136 |
| 0.1191 | 6.96 | 100 | 3.1622 | 37.2189 | 16.0076 | 25.7011 | 35.294 | 408.8669 |
| 0.0879 | 8.0 | 115 | 2.8510 | 39.8825 | 16.8073 | 27.2428 | 37.9568 | 318.2278 |
| 0.0899 | 8.97 | 129 | 2.9138 | 31.7139 | 13.7066 | 21.8844 | 30.5075 | 500.4053 |
| 0.0656 | 9.95 | 143 | 3.1616 | 33.055 | 14.5841 | 22.5883 | 31.7565 | 488.1686 |
| 0.0542 | 10.99 | 158 | 3.3630 | 43.7514 | 18.9011 | 29.9017 | 41.6887 | 198.8077 |
| 0.0557 | 11.97 | 172 | 3.3826 | 42.3089 | 18.2735 | 29.0356 | 40.4154 | 270.9675 |
| 0.0542 | 12.94 | 186 | 3.4408 | 40.7691 | 16.529 | 28.3999 | 38.9723 | 186.7308 |
| 0.0596 | 13.98 | 201 | 3.5253 | 37.0037 | 15.9098 | 25.2808 | 35.3868 | 398.4704 |
| 0.0385 | 14.61 | 210 | 3.4990 | 32.5815 | 14.2951 | 22.4501 | 31.2928 | 499.3107 |