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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- winograd_wsc
metrics:
- rouge
widget:
- text: Sam has a Parker pen. He loves writing with it.
example_title: Example 1
- text: Coronavirus quickly spread worldwide in 2020. The virus mostly affects elderly
people. They can easily catch it.
example_title: Example 2
- text: First, the manager evaluates the candidates. Afterwards, he notifies the candidates
regarding the evaluation.
example_title: Example 3
base_model: google/flan-t5-small
model-index:
- name: flan-t5-small-coref
results:
- task:
type: text2text-generation
name: Sequence-to-sequence Language Modeling
dataset:
name: winograd_wsc
type: winograd_wsc
config: wsc285
split: test
args: wsc285
metrics:
- type: rouge
value: 0.906
name: Rouge1
---
<!-- 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.
The model was trained on the task of coreference resolution.
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
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