update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- esnli
+metrics:
+- accuracy
+- f1
+- rouge
+- bleu
+model-index:
+- name: google-flan-t5-small-e-snli-generation-label_and_explanation-selected-b48
+  results:
+  - task:
+      name: Sequence-to-sequence Language Modeling
+      type: text2text-generation
+    dataset:
+      name: esnli
+      type: esnli
+      config: plain_text
+      split: validation
+      args: plain_text
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.8622231253810201
+    - name: F1
+      type: f1
+      value: 0.8623314280769628
+    - name: Rouge1
+      type: rouge
+      value: 0.605873896307076
+    - name: Bleu
+      type: bleu
+      value: 0.40472213589689604
+---
+
+<!-- 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. -->
+
+# google-flan-t5-small-e-snli-generation-label_and_explanation-selected-b48
+
+This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the esnli dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.8720
+- Accuracy: 0.8622
+- F1: 0.8623
+- Bertscore F1: 0.9329
+- Rouge1: 0.6059
+- Rouge2: 0.3988
+- Rougel: 0.5475
+- Rougelsum: 0.5496
+- Bleu: 0.4047
+
+## 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: 48
+- eval_batch_size: 48
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.05
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Bertscore F1 | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu   |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------------:|:------:|:------:|:------:|:---------:|:------:|
+| 1.5084        | 0.17  | 2000  | 1.7484          | 0.8001   | 0.7997 | 0.9271       | 0.5768 | 0.3695 | 0.5209 | 0.5229    | 0.3703 |
+| 1.2745        | 0.35  | 4000  | 1.8137          | 0.8113   | 0.8110 | 0.9304       | 0.5881 | 0.3804 | 0.5305 | 0.5325    | 0.3853 |
+| 1.2287        | 0.52  | 6000  | 1.8358          | 0.8392   | 0.8403 | 0.9298       | 0.5828 | 0.3747 | 0.5282 | 0.5301    | 0.3778 |
+| 1.1964        | 0.7   | 8000  | 1.8432          | 0.8430   | 0.8437 | 0.9326       | 0.5974 | 0.3905 | 0.5447 | 0.5462    | 0.3998 |
+| 1.1674        | 0.87  | 10000 | 1.8567          | 0.8507   | 0.8485 | 0.9310       | 0.5947 | 0.3888 | 0.5383 | 0.5402    | 0.3892 |
+| 1.1371        | 1.05  | 12000 | 1.8720          | 0.8622   | 0.8623 | 0.9329       | 0.6059 | 0.3988 | 0.5475 | 0.5496    | 0.4047 |
+
+
+### Framework versions
+
+- Transformers 4.27.4
+- Pytorch 2.0.0+cu117
+- Datasets 2.11.0
+- Tokenizers 0.13.2