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--- |
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language: |
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- en |
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widget: |
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- text: "Paste in a 13F Quarterly Report Here." |
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license: apache-2.0 |
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base_model: google/mt5-small |
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tags: |
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- summarization |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: mt5-small-finetuned-13f-reports |
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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-small-finetuned-13f-reports |
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This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4818 |
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- Rouge1: 0.3235 |
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- Rouge2: 0.2725 |
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- Rougel: 0.3146 |
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- Rougelsum: 0.3161 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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The model was fine tuned on a dataset of 1000+ quarterly 13F reports. It is intended for use with automating the |
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generation of summaries of articles before they are published. This allows you to put in a TL;DR summary without |
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having to write one on your own. |
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NOTE: The HuggingFace hosted Inference API interface takes the default parameters and so only outputs about 20 |
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words of text. To get a full summary, use the Inference API directly and pass in max_length=120 or so. |
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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: 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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| 11.4662 | 1.0 | 126 | 2.9329 | 0.2023 | 0.0998 | 0.1717 | 0.1792 | |
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| 3.4401 | 2.0 | 252 | 1.9914 | 0.3142 | 0.2573 | 0.3015 | 0.3036 | |
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| 2.5139 | 3.0 | 378 | 1.7493 | 0.3131 | 0.2576 | 0.3022 | 0.3039 | |
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| 2.152 | 4.0 | 504 | 1.6465 | 0.3114 | 0.2564 | 0.3009 | 0.3024 | |
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| 1.9624 | 5.0 | 630 | 1.5607 | 0.3202 | 0.2695 | 0.3114 | 0.3127 | |
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| 1.851 | 6.0 | 756 | 1.5163 | 0.3205 | 0.2704 | 0.3101 | 0.311 | |
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| 1.8002 | 7.0 | 882 | 1.4848 | 0.3225 | 0.2718 | 0.3148 | 0.3161 | |
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| 1.7864 | 8.0 | 1008 | 1.4818 | 0.3235 | 0.2725 | 0.3146 | 0.3161 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.0 |
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