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--- |
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license: other |
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base_model: boun-tabi-LMG/TURNA |
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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: TURNA_TSATweets_cond_gen_no_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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# TURNA_TSATweets_cond_gen_no_instruction |
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This model is a fine-tuned version of [boun-tabi-LMG/TURNA](https://huggingface.co/boun-tabi-LMG/TURNA) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0704 |
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- Rouge1: 0.705 |
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- Rouge2: 0.095 |
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- Rougel: 0.705 |
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- Rougelsum: 0.705 |
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- Bleu: 0.0 |
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- Precisions: [0.705, 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: 200 |
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- Reference Length: 200 |
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- Meteor: 0.3525 |
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- Score: 29.5 |
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- Num Edits: 59 |
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- Ref Length: 200.0 |
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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 | 0.0794 | 0.6569 | 0.0586 | 0.6552 | 0.6569 | 0.0 | [0.656896551724138, 0.0, 0.0, 0.0] | 1.0 | 1.0 | 580 | 580 | 0.3284 | 34.3103 | 199 | 580.0 | |
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| 1.7625 | 1.0 | 164 | 0.0707 | 0.6897 | 0.0638 | 0.6897 | 0.6879 | 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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| 1.7625 | 1.5 | 246 | 0.0778 | 0.7017 | 0.0284 | 0.7017 | 0.7 | 0.0 | [0.7017241379310345, 0.0, 0.0, 0.0] | 1.0 | 1.0 | 580 | 580 | 0.3509 | 29.8276 | 173 | 580.0 | |
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| 0.0921 | 2.0 | 328 | 0.0724 | 0.7259 | 0.0362 | 0.7241 | 0.7224 | 0.0 | [0.7241379310344828, 0.0, 0.0, 0.0] | 1.0 | 1.0 | 580 | 580 | 0.3621 | 27.5862 | 160 | 580.0 | |
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| 0.0921 | 2.5 | 410 | 0.0948 | 0.6845 | 0.1224 | 0.6828 | 0.6828 | 0.0 | [0.6827586206896552, 0.0, 0.0, 0.0] | 1.0 | 1.0 | 580 | 580 | 0.3414 | 31.7241 | 184 | 580.0 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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