Instructions to use kmok1/cs_mT5-large2_2e-5_50_v0.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kmok1/cs_mT5-large2_2e-5_50_v0.3 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("kmok1/cs_mT5-large2_2e-5_50_v0.3") model = AutoModelForSeq2SeqLM.from_pretrained("kmok1/cs_mT5-large2_2e-5_50_v0.3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: google/mt5-large | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - bleu | |
| model-index: | |
| - name: cs_mT5-large2_2e-5_50_v0.3 | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # cs_mT5-large2_2e-5_50_v0.3 | |
| This model is a fine-tuned version of [google/mt5-large](https://huggingface.co/google/mt5-large) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 10.7179 | |
| - Bleu: 8.2299 | |
| - 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: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 50 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | |
| | 22.4209 | 1.0 | 6 | 14.7377 | 5.6697 | 19.0 | | |
| | 20.671 | 2.0 | 12 | 14.9637 | 6.7619 | 19.0 | | |
| | 17.7208 | 3.0 | 18 | 14.6564 | 5.3777 | 19.0 | | |
| | 22.9549 | 4.0 | 24 | 15.1568 | 6.7736 | 19.0 | | |
| | 16.6185 | 5.0 | 30 | 14.1533 | 7.0263 | 19.0 | | |
| | 22.1158 | 6.0 | 36 | 15.0667 | 7.1851 | 19.0 | | |
| | 24.587 | 7.0 | 42 | 15.5166 | 7.6752 | 19.0 | | |
| | 16.4955 | 8.0 | 48 | 14.5515 | 7.5521 | 19.0 | | |
| | 21.0521 | 9.0 | 54 | 13.0890 | 7.7939 | 19.0 | | |
| | 16.1149 | 10.0 | 60 | 11.8305 | 7.7866 | 19.0 | | |
| | 12.8454 | 11.0 | 66 | 11.8727 | 7.7197 | 19.0 | | |
| | 18.482 | 12.0 | 72 | 11.6011 | 7.5761 | 19.0 | | |
| | 18.6175 | 13.0 | 78 | 11.8911 | 7.7925 | 19.0 | | |
| | 12.6805 | 14.0 | 84 | 11.8462 | 7.3764 | 19.0 | | |
| | 14.3151 | 15.0 | 90 | 11.4554 | 7.6604 | 19.0 | | |
| | 17.2287 | 16.0 | 96 | 11.1727 | 8.0204 | 19.0 | | |
| | 16.3546 | 17.0 | 102 | 10.7514 | 8.0859 | 19.0 | | |
| | 16.3339 | 18.0 | 108 | 11.1960 | 8.1381 | 19.0 | | |
| | 16.6065 | 19.0 | 114 | 11.3321 | 8.126 | 19.0 | | |
| | 14.3851 | 20.0 | 120 | 10.9074 | 6.3032 | 19.0 | | |
| | 15.8189 | 21.0 | 126 | 10.5179 | 6.3626 | 19.0 | | |
| | 8.4543 | 22.0 | 132 | 10.6037 | 6.3223 | 19.0 | | |
| | 18.0304 | 23.0 | 138 | 10.3665 | 6.236 | 19.0 | | |
| | 13.1475 | 24.0 | 144 | 10.3107 | 7.4434 | 19.0 | | |
| | 21.3407 | 25.0 | 150 | 10.2976 | 7.4596 | 19.0 | | |
| | 15.8901 | 26.0 | 156 | 10.4723 | 7.2047 | 19.0 | | |
| | 13.3029 | 27.0 | 162 | 10.7863 | 7.2047 | 19.0 | | |
| | 9.6205 | 28.0 | 168 | 11.2429 | 7.2047 | 19.0 | | |
| | 15.4244 | 29.0 | 174 | 11.5663 | 7.1797 | 19.0 | | |
| | 10.8496 | 30.0 | 180 | 11.9665 | 7.1839 | 19.0 | | |
| | 16.4213 | 31.0 | 186 | 12.3102 | 7.1002 | 19.0 | | |
| | 19.9358 | 32.0 | 192 | 12.3951 | 7.1693 | 19.0 | | |
| | 13.9974 | 33.0 | 198 | 12.6037 | 7.1693 | 19.0 | | |
| | 18.1208 | 34.0 | 204 | 12.4725 | 7.0996 | 19.0 | | |
| | 10.2059 | 35.0 | 210 | 12.1561 | 7.286 | 19.0 | | |
| | 15.9016 | 36.0 | 216 | 11.9896 | 7.286 | 19.0 | | |
| | 16.7008 | 37.0 | 222 | 11.4571 | 8.4159 | 19.0 | | |
| | 14.4533 | 38.0 | 228 | 11.1535 | 8.4159 | 19.0 | | |
| | 15.1107 | 39.0 | 234 | 11.1553 | 8.4159 | 19.0 | | |
| | 13.2587 | 40.0 | 240 | 11.0539 | 7.2709 | 19.0 | | |
| | 14.9836 | 41.0 | 246 | 11.3945 | 7.1574 | 19.0 | | |
| | 13.083 | 42.0 | 252 | 11.3690 | 7.1948 | 19.0 | | |
| | 24.9864 | 43.0 | 258 | 11.2586 | 8.2299 | 19.0 | | |
| | 22.1657 | 44.0 | 264 | 11.1126 | 8.2299 | 19.0 | | |
| | 15.6887 | 45.0 | 270 | 11.0112 | 8.2299 | 19.0 | | |
| | 8.581 | 46.0 | 276 | 10.8892 | 8.2299 | 19.0 | | |
| | 14.0141 | 47.0 | 282 | 10.8514 | 8.2299 | 19.0 | | |
| | 11.8402 | 48.0 | 288 | 10.8129 | 8.2299 | 19.0 | | |
| | 14.7845 | 49.0 | 294 | 10.7252 | 8.2299 | 19.0 | | |
| | 18.8443 | 50.0 | 300 | 10.7179 | 8.2299 | 19.0 | | |
| ### Framework versions | |
| - Transformers 4.38.2 | |
| - Pytorch 2.1.0+cu121 | |
| - Datasets 2.18.0 | |
| - Tokenizers 0.15.2 | |