update model card README.md

README.md

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+---
+license: apache-2.0
+base_model: google/mt5-small
+tags:
+- generated_from_trainer
+metrics:
+- rouge
+- bleu
+model-index:
+- name: mt5-small_mid_lr_mid_decay
+  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. -->
+
+# mt5-small_mid_lr_mid_decay
+
+This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7428
+- Rouge1: 43.12
+- Rouge2: 37.6639
+- Rougel: 41.8367
+- Rougelsum: 41.904
+- Bleu: 31.957
+- Gen Len: 12.1285
+- Meteor: 0.3936
+- No ans accuracy: 22.29
+- Av cosine sim: 0.7406
+
+## 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: 0.001
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 9
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 128
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 20
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Bleu    | Gen Len | Meteor | No ans accuracy | Av cosine sim |
+|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:-------:|:------:|:---------------:|:-------------:|
+| 3.1455        | 1.0   | 175  | 0.9832          | 18.7107 | 15.4897 | 18.1977 | 18.2212   | 7.0634  | 7.6229  | 0.1626 | 22.4000         | 0.3949        |
+| 1.1623        | 1.99  | 350  | 0.8542          | 38.7675 | 32.704  | 37.3557 | 37.3949   | 27.4323 | 12.5135 | 0.3487 | 17.9900         | 0.6992        |
+| 0.9431        | 2.99  | 525  | 0.8017          | 41.6216 | 35.6002 | 40.2386 | 40.2881   | 30.7994 | 12.8117 | 0.3755 | 18.37           | 0.7304        |
+| 0.8119        | 3.98  | 700  | 0.7787          | 43.5805 | 37.4117 | 42.1059 | 42.155    | 32.9646 | 13.2176 | 0.3947 | 17.7400         | 0.7582        |
+| 0.7235        | 4.98  | 875  | 0.7477          | 43.4124 | 37.2017 | 41.8468 | 41.9097   | 32.9345 | 13.116  | 0.3946 | 18.92           | 0.7561        |
+| 0.6493        | 5.97  | 1050 | 0.7266          | 40.4764 | 34.9927 | 39.0999 | 39.1711   | 29.0601 | 11.748  | 0.3687 | 22.6500         | 0.7071        |
+| 0.5871        | 6.97  | 1225 | 0.7284          | 43.3812 | 37.5544 | 42.0405 | 42.0865   | 32.8345 | 12.6063 | 0.3949 | 21.05           | 0.7485        |
+| 0.5453        | 7.96  | 1400 | 0.7389          | 43.4549 | 37.76   | 42.1025 | 42.215    | 32.6726 | 12.4537 | 0.3965 | 21.44           | 0.7496        |
+| 0.5038        | 8.96  | 1575 | 0.7428          | 43.12   | 37.6639 | 41.8367 | 41.904    | 31.957  | 12.1285 | 0.3936 | 22.29           | 0.7406        |
+
+
+### Framework versions
+
+- Transformers 4.31.0
+- Pytorch 2.0.1+cu118
+- Datasets 2.13.1
+- Tokenizers 0.13.3