update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- summarization
+- generated_from_trainer
+datasets:
+- cnn_dailymail
+model-index:
+- name: t5-v1_1-small-finetuned-summarization-cnn-ver1
+  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. -->
+
+# t5-v1_1-small-finetuned-summarization-cnn-ver1
+
+This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the cnn_dailymail dataset.
+It achieves the following results on the evaluation set:
+- Loss: 2.7467
+- Bertscore-mean-precision: 0.8764
+- Bertscore-mean-recall: 0.8519
+- Bertscore-mean-f1: 0.8639
+- Bertscore-median-precision: 0.8746
+- Bertscore-median-recall: 0.8518
+- Bertscore-median-f1: 0.8632
+
+## 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: 4e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Bertscore-mean-precision | Bertscore-mean-recall | Bertscore-mean-f1 | Bertscore-median-precision | Bertscore-median-recall | Bertscore-median-f1 |
+|:-------------:|:-----:|:----:|:---------------:|:------------------------:|:---------------------:|:-----------------:|:--------------------------:|:-----------------------:|:-------------------:|
+| 4.6845        | 1.0   | 718  | 2.9003          | 0.8698                   | 0.8456                | 0.8574            | 0.8693                     | 0.8445                  | 0.8570              |
+| 3.7925        | 2.0   | 1436 | 2.7654          | 0.8765                   | 0.8519                | 0.8639            | 0.8745                     | 0.8512                  | 0.8629              |
+| 3.6332        | 3.0   | 2154 | 2.7467          | 0.8764                   | 0.8519                | 0.8639            | 0.8746                     | 0.8518                  | 0.8632              |
+
+
+### Framework versions
+
+- Transformers 4.24.0
+- Pytorch 1.12.1+cu113
+- Datasets 2.7.0
+- Tokenizers 0.13.2