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-b64
+  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: 0.8691322901849218
+    - name: F1
+      type: f1
+      value: 0.8686267742768865
+    - name: Rouge1
+      type: rouge
+      value: 0.6062872493545299
+    - name: Bleu
+      type: bleu
+      value: 0.4012059786299585
+---
+
+<!-- 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-b64
+
+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: 1.8703
+- Accuracy: 0.8691
+- F1: 0.8686
+- Bertscore F1: 0.9338
+- Rouge1: 0.6063
+- Rouge2: 0.3995
+- Rougel: 0.5500
+- Rougelsum: 0.5521
+- Bleu: 0.4012
+
+## 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: 64
+- eval_batch_size: 64
+- 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   |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------------:|:------:|:------:|:------:|:---------:|:------:|
+| 1.4692        | 0.23  | 2000  | 1.7872          | 0.8212   | 0.8203 | 0.9287       | 0.5787 | 0.3685 | 0.5239 | 0.5257    | 0.3856 |
+| 1.2505        | 0.47  | 4000  | 1.8808          | 0.8263   | 0.8264 | 0.9308       | 0.5870 | 0.3749 | 0.5321 | 0.5337    | 0.3904 |
+| 1.2003        | 0.7   | 6000  | 1.8477          | 0.8475   | 0.8481 | 0.9325       | 0.5984 | 0.3913 | 0.5452 | 0.5469    | 0.4004 |
+| 1.1624        | 0.93  | 8000  | 1.8244          | 0.8599   | 0.8587 | 0.9335       | 0.6029 | 0.3928 | 0.5441 | 0.5457    | 0.4024 |
+| 1.1155        | 1.16  | 10000 | 1.8499          | 0.8695   | 0.8688 | 0.9331       | 0.6083 | 0.4019 | 0.5519 | 0.5540    | 0.4022 |
+| 1.0913        | 1.4   | 12000 | 1.8703          | 0.8691   | 0.8686 | 0.9338       | 0.6063 | 0.3995 | 0.5500 | 0.5521    | 0.4012 |
+
+
+### Framework versions
+
+- Transformers 4.27.4
+- Pytorch 2.0.0+cu117
+- Datasets 2.11.0
+- Tokenizers 0.13.2