update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- winograd_wsc
+metrics:
+- rouge
+model-index:
+- name: flan-t5-small-coref
+  results:
+  - task:
+      name: Sequence-to-sequence Language Modeling
+      type: text2text-generation
+    dataset:
+      name: winograd_wsc
+      type: winograd_wsc
+      config: wsc285
+      split: test
+      args: wsc285
+    metrics:
+    - name: Rouge1
+      type: rouge
+      value: 0.906
+---
+
+<!-- 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. -->
+
+# flan-t5-small-coref
+
+This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the winograd_wsc dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5656
+- Rouge1: 0.906
+- Rouge2: 0.8192
+- Rougel: 0.9016
+- Rougelsum: 0.9026
+- Gen Len: 23.1724
+
+## 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: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- 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 | Gen Len |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
+| No log        | 1.0   | 16   | 1.0901          | 0.6849 | 0.561  | 0.6734 | 0.6746    | 18.4483 |
+| No log        | 2.0   | 32   | 0.9083          | 0.8512 | 0.7509 | 0.8438 | 0.8437    | 21.1379 |
+| No log        | 3.0   | 48   | 0.8132          | 0.8638 | 0.7728 | 0.8588 | 0.8595    | 21.8276 |
+| No log        | 4.0   | 64   | 0.7590          | 0.8786 | 0.7842 | 0.8744 | 0.876     | 22.2069 |
+| No log        | 5.0   | 80   | 0.7225          | 0.8846 | 0.7928 | 0.8805 | 0.8817    | 22.3793 |
+| No log        | 6.0   | 96   | 0.6920          | 0.886  | 0.7942 | 0.8821 | 0.8827    | 22.4483 |
+| No log        | 7.0   | 112  | 0.6660          | 0.8861 | 0.7922 | 0.8816 | 0.8827    | 22.5172 |
+| No log        | 8.0   | 128  | 0.6470          | 0.8879 | 0.7953 | 0.8836 | 0.8849    | 22.6897 |
+| No log        | 9.0   | 144  | 0.6318          | 0.8968 | 0.806  | 0.8923 | 0.8933    | 23.069  |
+| No log        | 10.0  | 160  | 0.6160          | 0.8968 | 0.806  | 0.8923 | 0.8933    | 23.069  |
+| No log        | 11.0  | 176  | 0.6055          | 0.9056 | 0.822  | 0.9014 | 0.9021    | 23.1724 |
+| No log        | 12.0  | 192  | 0.5962          | 0.9056 | 0.822  | 0.9014 | 0.9021    | 23.1724 |
+| No log        | 13.0  | 208  | 0.5884          | 0.9074 | 0.8246 | 0.9033 | 0.9042    | 23.2069 |
+| No log        | 14.0  | 224  | 0.5825          | 0.9049 | 0.8182 | 0.9005 | 0.9016    | 23.2414 |
+| No log        | 15.0  | 240  | 0.5769          | 0.9049 | 0.8182 | 0.9005 | 0.9016    | 23.2414 |
+| No log        | 16.0  | 256  | 0.5727          | 0.903  | 0.8132 | 0.8991 | 0.8997    | 23.1724 |
+| No log        | 17.0  | 272  | 0.5698          | 0.906  | 0.8192 | 0.9016 | 0.9026    | 23.1724 |
+| No log        | 18.0  | 288  | 0.5673          | 0.906  | 0.8192 | 0.9016 | 0.9026    | 23.1724 |
+| No log        | 19.0  | 304  | 0.5661          | 0.906  | 0.8192 | 0.9016 | 0.9026    | 23.1724 |
+| No log        | 20.0  | 320  | 0.5656          | 0.906  | 0.8192 | 0.9016 | 0.9026    | 23.1724 |
+
+
+### Framework versions
+
+- Transformers 4.25.1
+- Pytorch 1.13.0+cu116
+- Datasets 2.7.1
+- Tokenizers 0.13.2