update model card README.md

README.md

@@ -0,0 +1,101 @@
+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- esnli
+metrics:
+- accuracy
+- f1
+- rouge
+- bleu
+model-index:
+- name: 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: 0.8732981101402154
+    - name: F1
+      type: f1
+      value: 0.8729633394714756
+    - name: Rouge1
+      type: rouge
+      value: 0.6144211309547953
+    - name: Bleu
+      type: bleu
+      value: 0.4223746159966924
+---
+
+<!-- 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. -->
+
+# t5-small-e-snli-generation-label_and_explanation-selected-b64
+
+This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the esnli dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.9257
+- Accuracy: 0.8733
+- F1: 0.8730
+- Bertscore F1: 0.9356
+- Rouge1: 0.6144
+- Rouge2: 0.4096
+- Rougel: 0.5592
+- Rougelsum: 0.5611
+- Bleu: 0.4224
+
+## 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   |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------------:|:------:|:------:|:------:|:---------:|:------:|
+| 1.6638        | 0.23  | 2000  | 2.0039          | 0.7883   | 0.7869 | 0.9274       | 0.5705 | 0.3601 | 0.5175 | 0.5192    | 0.3730 |
+| 1.2998        | 0.47  | 4000  | 1.9378          | 0.8283   | 0.8293 | 0.9303       | 0.5861 | 0.3748 | 0.5310 | 0.5329    | 0.3854 |
+| 1.2351        | 0.7   | 6000  | 1.8752          | 0.8431   | 0.8437 | 0.9321       | 0.5951 | 0.3880 | 0.5411 | 0.5430    | 0.3954 |
+| 1.1948        | 0.93  | 8000  | 1.9346          | 0.8536   | 0.8529 | 0.9333       | 0.6018 | 0.3931 | 0.5451 | 0.5472    | 0.4006 |
+| 1.1537        | 1.16  | 10000 | 1.8881          | 0.8654   | 0.8647 | 0.9332       | 0.6070 | 0.4023 | 0.5483 | 0.5506    | 0.4096 |
+| 1.1298        | 1.4   | 12000 | 1.9265          | 0.8690   | 0.8685 | 0.9337       | 0.6053 | 0.3988 | 0.5507 | 0.5526    | 0.4093 |
+| 1.1219        | 1.63  | 14000 | 1.9017          | 0.8713   | 0.8714 | 0.9332       | 0.6029 | 0.3941 | 0.5470 | 0.5489    | 0.4042 |
+| 1.1088        | 1.86  | 16000 | 1.9257          | 0.8733   | 0.8730 | 0.9356       | 0.6144 | 0.4096 | 0.5592 | 0.5611    | 0.4224 |
+
+
+### Framework versions
+
+- Transformers 4.27.4
+- Pytorch 2.0.0+cu117
+- Datasets 2.11.0
+- Tokenizers 0.13.2