---
license: apache-2.0
base_model: google/t5-v1_1-large
tags:
- generated_from_trainer
model-index:
- name: Sentiment-google-t5-v1_1-large-intra_model-dataset-frequency-model_annots_str
  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. -->

# Sentiment-google-t5-v1_1-large-intra_model-dataset-frequency-model_annots_str

This model is a fine-tuned version of [google/t5-v1_1-large](https://huggingface.co./google/t5-v1_1-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan

## 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: 0.0001
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 19.7788       | 1.0   | 44   | 23.9563         |
| 15.9283       | 2.0   | 88   | 11.8553         |
| 10.0222       | 3.0   | 132  | 9.4991          |
| 8.9528        | 4.0   | 176  | 9.0638          |
| 8.4671        | 5.0   | 220  | 8.7748          |
| 8.189         | 6.0   | 264  | 8.6661          |
| 8.2203        | 7.0   | 308  | 8.5367          |
| 7.9303        | 8.0   | 352  | 8.1591          |
| 7.5313        | 9.0   | 396  | 7.7616          |
| 7.3476        | 10.0  | 440  | 7.4948          |
| 7.1959        | 11.0  | 484  | 7.3478          |
| 6.9493        | 12.0  | 528  | 7.2555          |
| 6.8091        | 13.0  | 572  | 7.1311          |
| 0.9281        | 14.0  | 616  | 0.6867          |
| 0.7487        | 15.0  | 660  | 0.6683          |
| 0.7383        | 16.0  | 704  | 0.6678          |
| 0.7336        | 17.0  | 748  | 0.6632          |
| 0.7256        | 18.0  | 792  | 0.6566          |
| 0.7344        | 19.0  | 836  | 0.6584          |
| 0.7237        | 20.0  | 880  | 0.6619          |
| 0.7204        | 21.0  | 924  | 0.6619          |
| 0.7088        | 22.0  | 968  | 0.6566          |
| 0.7017        | 23.0  | 1012 | 0.6556          |
| 0.7136        | 24.0  | 1056 | 0.6566          |
| 0.7078        | 25.0  | 1100 | 0.6582          |
| 0.7165        | 26.0  | 1144 | 0.6557          |
| 0.706         | 27.0  | 1188 | 0.6559          |
| 0.7146        | 28.0  | 1232 | 0.6537          |
| 0.7138        | 29.0  | 1276 | 0.6546          |
| 0.706         | 30.0  | 1320 | 0.6559          |
| 0.7067        | 31.0  | 1364 | 0.6567          |
| 0.7137        | 32.0  | 1408 | 0.6599          |
| 0.7           | 33.0  | 1452 | 0.6550          |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1