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--- |
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license: apache-2.0 |
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base_model: google/mt5-large |
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tags: |
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- generated_from_trainer |
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metrics: |
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- rouge |
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- bleu |
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model-index: |
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- name: mT5_TSATweets_cond_gen_5_instruction |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# mT5_TSATweets_cond_gen_5_instruction |
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This model is a fine-tuned version of [google/mt5-large](https://huggingface.co/google/mt5-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0710 |
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- Rouge1: 0.709 |
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- Rouge2: 0.094 |
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- Rougel: 0.71 |
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- Rougelsum: 0.709 |
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- Bleu: 0.0 |
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- Precisions: [0.709, 0.0, 0.0, 0.0] |
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- Brevity Penalty: 1.0 |
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- Length Ratio: 1.0 |
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- Translation Length: 1000 |
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- Reference Length: 1000 |
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- Meteor: 0.3545 |
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- Score: 29.1000 |
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- Num Edits: 291 |
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- Ref Length: 1000.0 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu | Precisions | Brevity Penalty | Length Ratio | Translation Length | Reference Length | Meteor | Score | Num Edits | Ref Length | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----:|:-----------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:-------:|:---------:|:----------:| |
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| No log | 0.5 | 82 | 0.1335 | 0.2707 | 0.0 | 0.2707 | 0.2707 | 0.0 | [0.2706896551724138, 0.0, 0.0, 0.0] | 1.0 | 1.0 | 580 | 580 | 0.1353 | 72.9310 | 423 | 580.0 | |
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| 2.9168 | 1.0 | 164 | 0.0903 | 0.6172 | 0.0017 | 0.6190 | 0.6172 | 0.0 | [0.6172413793103448, 0.0, 0.0, 0.0] | 1.0 | 1.0 | 580 | 580 | 0.3086 | 38.2759 | 222 | 580.0 | |
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| 2.9168 | 1.5 | 246 | 0.0968 | 0.6310 | 0.0 | 0.6319 | 0.6310 | 0.0 | [0.6310344827586207, 0.0, 0.0, 0.0] | 1.0 | 1.0 | 580 | 580 | 0.3155 | 36.8966 | 214 | 580.0 | |
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| 0.1116 | 2.0 | 328 | 0.0769 | 0.6603 | 0.0328 | 0.6603 | 0.6586 | 0.0 | [0.6603448275862069, 0.0, 0.0, 0.0] | 1.0 | 1.0 | 580 | 580 | 0.3302 | 33.9655 | 197 | 580.0 | |
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| 0.1116 | 2.5 | 410 | 0.0762 | 0.6931 | 0.0707 | 0.6931 | 0.6914 | 0.0 | [0.6931034482758621, 0.0, 0.0, 0.0] | 1.0 | 1.0 | 580 | 580 | 0.3466 | 30.6897 | 178 | 580.0 | |
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| 0.0921 | 3.0 | 492 | 0.0709 | 0.6931 | 0.0276 | 0.6914 | 0.6931 | 0.0 | [0.6931034482758621, 0.0, 0.0, 0.0] | 1.0 | 1.0 | 580 | 580 | 0.3466 | 30.6897 | 178 | 580.0 | |
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| 0.0921 | 3.5 | 574 | 0.0897 | 0.6897 | 0.0379 | 0.6897 | 0.6897 | 0.0 | [0.6896551724137931, 0.0, 0.0, 0.0] | 1.0 | 1.0 | 580 | 580 | 0.3448 | 31.0345 | 180 | 580.0 | |
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| 0.079 | 4.0 | 656 | 0.0679 | 0.6948 | 0.0707 | 0.6948 | 0.6948 | 0.0 | [0.6948275862068966, 0.0, 0.0, 0.0] | 1.0 | 1.0 | 580 | 580 | 0.3474 | 30.5172 | 177 | 580.0 | |
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| 0.079 | 4.5 | 738 | 0.0771 | 0.7103 | 0.0345 | 0.7103 | 0.7086 | 0.0 | [0.7103448275862069, 0.0, 0.0, 0.0] | 1.0 | 1.0 | 580 | 580 | 0.3552 | 28.9655 | 168 | 580.0 | |
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| 0.0712 | 5.0 | 820 | 0.0675 | 0.7069 | 0.0517 | 0.7069 | 0.7052 | 0.0 | [0.7051724137931035, 0.0, 0.0, 0.0] | 1.0 | 1.0 | 580 | 580 | 0.3526 | 29.4828 | 171 | 580.0 | |
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| 0.0712 | 5.5 | 902 | 0.0657 | 0.7138 | 0.0603 | 0.7138 | 0.7138 | 0.0 | [0.7137931034482758, 0.0, 0.0, 0.0] | 1.0 | 1.0 | 580 | 580 | 0.3569 | 28.6207 | 166 | 580.0 | |
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| 0.065 | 6.0 | 984 | 0.0670 | 0.7069 | 0.0621 | 0.7069 | 0.7069 | 0.0 | [0.7068965517241379, 0.0, 0.0, 0.0] | 1.0 | 1.0 | 580 | 580 | 0.3534 | 29.3103 | 170 | 580.0 | |
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| 0.065 | 6.5 | 1066 | 0.0658 | 0.7103 | 0.0672 | 0.7103 | 0.7103 | 0.0 | [0.7103448275862069, 0.0, 0.0, 0.0] | 1.0 | 1.0 | 580 | 580 | 0.3552 | 28.9655 | 168 | 580.0 | |
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| 0.0596 | 7.0 | 1148 | 0.0741 | 0.7155 | 0.0586 | 0.7155 | 0.7155 | 0.0 | [0.7155172413793104, 0.0, 0.0, 0.0] | 1.0 | 1.0 | 580 | 580 | 0.3578 | 28.4483 | 165 | 580.0 | |
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### Framework versions |
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- Transformers 4.41.0 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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