---
base_model: ybelkada/flan-t5-xl-sharded-bf16
tags:
- generated_from_trainer
model-index:
- name: flan-t5-xl-absa-multitask-rest
  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. -->

# flan-t5-xl-absa-multitask-rest

This model is a fine-tuned version of [ybelkada/flan-t5-xl-sharded-bf16](https://huggingface.co/ybelkada/flan-t5-xl-sharded-bf16) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1127

## 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: 5e-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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 12

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.3549        | 0.32  | 200  | 3.5848          |
| 1.5908        | 0.63  | 400  | 0.5331          |
| 0.4981        | 0.95  | 600  | 0.3159          |
| 0.351         | 1.27  | 800  | 0.2457          |
| 0.2884        | 1.58  | 1000 | 0.2118          |
| 0.2592        | 1.9   | 1200 | 0.2000          |
| 0.2323        | 2.22  | 1400 | 0.1839          |
| 0.2107        | 2.53  | 1600 | 0.1704          |
| 0.2071        | 2.85  | 1800 | 0.1649          |
| 0.1944        | 3.16  | 2000 | 0.1634          |
| 0.1774        | 3.48  | 2200 | 0.1549          |
| 0.1796        | 3.8   | 2400 | 0.1505          |
| 0.1695        | 4.11  | 2600 | 0.1427          |
| 0.1569        | 4.43  | 2800 | 0.1403          |
| 0.1662        | 4.75  | 3000 | 0.1395          |
| 0.15          | 5.06  | 3200 | 0.1351          |
| 0.1448        | 5.38  | 3400 | 0.1283          |
| 0.1444        | 5.7   | 3600 | 0.1302          |
| 0.1506        | 6.01  | 3800 | 0.1237          |
| 0.1321        | 6.33  | 4000 | 0.1264          |
| 0.1318        | 6.65  | 4200 | 0.1269          |
| 0.1298        | 6.96  | 4400 | 0.1207          |
| 0.1273        | 7.28  | 4600 | 0.1224          |
| 0.123         | 7.59  | 4800 | 0.1209          |
| 0.1278        | 7.91  | 5000 | 0.1222          |
| 0.1236        | 8.23  | 5200 | 0.1165          |
| 0.1188        | 8.54  | 5400 | 0.1154          |
| 0.1181        | 8.86  | 5600 | 0.1173          |
| 0.1126        | 9.18  | 5800 | 0.1177          |
| 0.113         | 9.49  | 6000 | 0.1194          |
| 0.1086        | 9.81  | 6200 | 0.1148          |
| 0.1086        | 10.13 | 6400 | 0.1158          |
| 0.1118        | 10.44 | 6600 | 0.1145          |
| 0.105         | 10.76 | 6800 | 0.1125          |
| 0.1119        | 11.08 | 7000 | 0.1146          |
| 0.1007        | 11.39 | 7200 | 0.1123          |
| 0.114         | 11.71 | 7400 | 0.1127          |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2