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--- |
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license: apache-2.0 |
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tags: |
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- summarization |
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- generated_from_trainer |
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datasets: |
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- big_patent |
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metrics: |
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- rouge |
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model-index: |
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- name: mt5-small-finetuned-Big-Patent-h |
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results: |
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- task: |
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name: Sequence-to-sequence Language Modeling |
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type: text2text-generation |
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dataset: |
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name: big_patent |
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type: big_patent |
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config: h |
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split: train |
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args: h |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 33.9091 |
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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-small-finetuned-Big-Patent-h |
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This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the big_patent dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.2622 |
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- Rouge1: 33.9091 |
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- Rouge2: 14.1731 |
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- Rougel: 30.105 |
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- Rougelsum: 30.3666 |
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## Model description |
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In this project, we fine-tuned mT5small, a multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages. |
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The model was fine-tuned on the electric patent corpus using a variety of techniques, including transfer learning, data augmentation, and hyperparameter tuning. |
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## Intended uses & limitations |
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The fine-tuned model showed significant improvements in performance on the electric patent-specific tasks compared to the original pre-trained model. |
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Note: This project is suitable for researchers who are working on electric patent, as it's fine-tuned on electric patents and it can be used for related NLP problems for electric patent and electric patent research. |
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## Training and evaluation data |
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A subset of electric patents were used to fine-tune the model. |
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The fine-tuned model was evaluated using the ROUGE metric on a variety of natural language processing tasks specific to the patent domain, including, named entity recognition, and summarization. |
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## Training procedure |
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The model was fine-tuned on the electric patent corpus using a variety of techniques, including transfer learning, data augmentation, and hyperparameter tuning. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5.6e-05 |
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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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- 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: 8 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| |
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| 2.5817 | 1.0 | 1071 | 2.3830 | 32.8521 | 13.2087 | 29.5594 | 29.7744 | |
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| 2.5657 | 2.0 | 2142 | 2.3345 | 33.9434 | 14.0573 | 30.0135 | 30.2533 | |
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| 2.4915 | 3.0 | 3213 | 2.2761 | 33.2033 | 13.2053 | 29.5126 | 29.8023 | |
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| 2.4365 | 4.0 | 4284 | 2.3041 | 33.8649 | 13.6629 | 30.0377 | 30.257 | |
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| 2.3952 | 5.0 | 5355 | 2.2722 | 33.9208 | 13.8018 | 30.1035 | 30.3432 | |
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| 2.3628 | 6.0 | 6426 | 2.2850 | 33.883 | 13.9537 | 30.0579 | 30.2417 | |
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| 2.3474 | 7.0 | 7497 | 2.2858 | 33.7201 | 14.0808 | 30.0762 | 30.255 | |
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| 2.331 | 8.0 | 8568 | 2.2622 | 33.9091 | 14.1731 | 30.105 | 30.3666 | |
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### Framework versions |
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- Transformers 4.24.0 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.7.1 |
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- Tokenizers 0.13.2 |
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