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--- |
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base_model: mistralai/Mistral-7B-v0.1 |
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tags: |
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- alignment-handbook |
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- trl |
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- orpo |
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- generated_from_trainer |
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- trl |
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- orpo |
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- generated_from_trainer |
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- entity linking |
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datasets: |
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- arynkiewicz/anydef-kilt-tasks-v2 |
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model-index: |
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- name: anydef-orpo-v2 |
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results: [] |
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license: apache-2.0 |
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inference: false |
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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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# anydef-orpo-v2 |
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This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the arynkiewicz/anydef-kilt-tasks-v2 dataset. |
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Find out about Model description, Intended uses & limitations and Training and evaluation data on our [github](https://github.com/daisd-ai/universal-el). |
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This is an updated version of the anydef model. The primary goal was to use an improved dataset during fine-tuning, enabling the model to better understand nuances. |
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Overall, anydef-v2 offers better performance in benchmarks, and manual inspection of the results suggests that the model has indeed improved. |
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Precision (%): |
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| Dataset | anydef | anydef-v2 | |
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|------------|------------|------------| |
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| RSS-500 | 66.23| 66.89| |
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| ISTEX-1000| 86.72| 85.82| |
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| Reuters-128| 63.8| 64.88| |
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| TweekiGold| 75.23| 75.93| |
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Retrieval rate (%): |
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| Dataset | anydef | anydef-v2 | |
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|------------|------------|------------| |
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| RSS-500 | 82.78| 84.11| |
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| ISTEX-1000| 97.91| 97.76| |
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| Reuters-128| 80.47| 83.33| |
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| TweekiGold| 89.93| 91.67| |
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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-06 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 8 |
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- total_train_batch_size: 64 |
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- total_eval_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: inverse_sqrt |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 3 |
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### Training results |
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### Framework versions |
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- Transformers 4.43.3 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.19.1 |
