silmi224/led-risalah-v5

1---2base_model: silmi224/finetune-led-350003tags:4- generated_from_trainer5model-index:6- name: led-risalah-v57  results: []8---9 10<!-- This model card has been generated automatically according to the information the Trainer had access to. You11should probably proofread and complete it, then remove this comment. -->12 13# led-risalah-v514 15This model is a fine-tuned version of [silmi224/finetune-led-35000](https://huggingface.co/silmi224/finetune-led-35000) on an unknown dataset.16It achieves the following results on the evaluation set:17- Loss: 1.524218- Rouge1 Precision: 0.67619- Rouge1 Recall: 0.172120- Rouge1 Fmeasure: 0.272821 22## Model description23 24More information needed25 26## Intended uses & limitations27 28More information needed29 30## Training and evaluation data31 32More information needed33 34## Training procedure35 36### Training hyperparameters37 38The following hyperparameters were used during training:39- learning_rate: 2e-0540- train_batch_size: 141- eval_batch_size: 142- seed: 4243- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-0844- lr_scheduler_type: linear45- num_epochs: 1046 47### Training results48 49| Training Loss | Epoch | Step | Validation Loss | Rouge1 Precision | Rouge1 Recall | Rouge1 Fmeasure |50|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|51| No log        | 1.0   | 70   | 1.6479          | 0.6178           | 0.1618        | 0.2556          |52| 1.8512        | 2.0   | 140  | 1.5744          | 0.6561           | 0.174         | 0.2745          |53| 1.4296        | 3.0   | 210  | 1.5595          | 0.6617           | 0.1704        | 0.2702          |54| 1.4296        | 4.0   | 280  | 1.5402          | 0.685            | 0.1719        | 0.274           |55| 1.1976        | 5.0   | 350  | 1.5242          | 0.676            | 0.1721        | 0.2728          |56| 1.0638        | 6.0   | 420  | 1.5383          | 0.6886           | 0.182         | 0.2873          |57| 1.0638        | 7.0   | 490  | 1.5652          | 0.6636           | 0.1757        | 0.2771          |58| 0.9657        | 8.0   | 560  | 1.5797          | 0.6788           | 0.172         | 0.2733          |59| 0.9215        | 9.0   | 630  | 1.5960          | 0.6644           | 0.1715        | 0.2715          |60| 0.849         | 10.0  | 700  | 1.5943          | 0.6581           | 0.1693        | 0.2681          |61 62 63### Framework versions64 65- Transformers 4.35.266- Pytorch 2.1.1+cu12167- Datasets 2.14.568- Tokenizers 0.15.169