update model card README.md

README.md

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+---
+license: apache-2.0
+base_model: google/flan-t5-base
+tags:
+- generated_from_trainer
+model-index:
+- name: flant5_sum_samsum
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# flant5_sum_samsum
+
+This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: nan
+- Gen Len: 16.6760
+- Rouge Score: {'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456}
+- Bleu Score: {'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569}
+- Bleurt Score: -0.4863
+- Bert Score: [0.9187235832214355, 0.9003126621246338, 0.9092234373092651]
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Gen Len | Rouge Score                                                                                                                  | Bleu Score                                                                                                                                                                                                                                                           | Bleurt Score | Bert Score                                                   |
+|:-------------:|:-----:|:----:|:---------------:|:-------:|:----------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:------------:|:------------------------------------------------------------:|
+| 0.0           | 1.0   | 921  | nan             | 16.6760 | {'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456} | {'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569} | -0.4863      | [0.9187235832214355, 0.9003126621246338, 0.9092234373092651] |
+| 0.0           | 2.0   | 1842 | nan             | 16.6760 | {'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456} | {'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569} | -0.4863      | [0.9187235832214355, 0.9003126621246338, 0.9092234373092651] |
+| 0.0           | 3.0   | 2763 | nan             | 16.6760 | {'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456} | {'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569} | -0.4863      | [0.9187235832214355, 0.9003126621246338, 0.9092234373092651] |
+| 0.0           | 4.0   | 3684 | nan             | 16.6760 | {'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456} | {'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569} | -0.4863      | [0.9187235832214355, 0.9003126621246338, 0.9092234373092651] |
+| 0.0           | 5.0   | 4605 | nan             | 16.6760 | {'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456} | {'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569} | -0.4863      | [0.9187235832214355, 0.9003126621246338, 0.9092234373092651] |
+| 0.0           | 6.0   | 5526 | nan             | 16.6760 | {'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456} | {'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569} | -0.4863      | [0.9187235832214355, 0.9003126621246338, 0.9092234373092651] |
+| 0.0           | 7.0   | 6447 | nan             | 16.6760 | {'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456} | {'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569} | -0.4863      | [0.9187235832214355, 0.9003126621246338, 0.9092234373092651] |
+| 0.0           | 8.0   | 7368 | nan             | 16.6760 | {'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456} | {'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569} | -0.4863      | [0.9187235832214355, 0.9003126621246338, 0.9092234373092651] |
+| 0.0           | 9.0   | 8289 | nan             | 16.6760 | {'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456} | {'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569} | -0.4863      | [0.9187235832214355, 0.9003126621246338, 0.9092234373092651] |
+| 0.0           | 10.0  | 9210 | nan             | 16.6760 | {'rouge1': 0.4648609117501229, 'rouge2': 0.23489748856950105, 'rougeL': 0.3936027885754436, 'rougeLsum': 0.3932448622689456} | {'bleu': 0.12048170853922512, 'precisions': [0.5838656689176857, 0.28994082840236685, 0.17667882428663376, 0.11335841956726246], 'brevity_penalty': 0.49929356415876747, 'length_ratio': 0.5901233238192687, 'translation_length': 10958, 'reference_length': 18569} | -0.4863      | [0.9187235832214355, 0.9003126621246338, 0.9092234373092651] |
+
+
+### Framework versions
+
+- Transformers 4.31.0
+- Pytorch 2.0.1+cu118
+- Datasets 2.10.0
+- Tokenizers 0.13.3