---
license: apache-2.0
base_model: google-t5/t5-small
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: t5_small_patent
  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_small_patent

This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9833
- Accuracy: 0.657
- F1 Macro: 0.5822
- F1 Micro: 0.657

## 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: 0.0005
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Micro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|
| 1.4814        | 0.06  | 50   | 1.4704          | 0.492    | 0.3414   | 0.492    |
| 1.3003        | 0.13  | 100  | 1.2652          | 0.5512   | 0.3876   | 0.5512   |
| 1.2291        | 0.19  | 150  | 1.2304          | 0.563    | 0.4000   | 0.563    |
| 1.142         | 0.26  | 200  | 1.1644          | 0.5894   | 0.4586   | 0.5894   |
| 1.0581        | 0.32  | 250  | 1.1396          | 0.603    | 0.4563   | 0.603    |
| 1.2415        | 0.38  | 300  | 1.1215          | 0.613    | 0.4937   | 0.613    |
| 1.1336        | 0.45  | 350  | 1.1042          | 0.6172   | 0.5292   | 0.6172   |
| 1.045         | 0.51  | 400  | 1.0924          | 0.624    | 0.5271   | 0.624    |
| 1.1204        | 0.58  | 450  | 1.0897          | 0.6184   | 0.5146   | 0.6184   |
| 1.0691        | 0.64  | 500  | 1.0827          | 0.6236   | 0.5169   | 0.6236   |
| 0.9782        | 0.7   | 550  | 1.0664          | 0.6258   | 0.5303   | 0.6258   |
| 1.081         | 0.77  | 600  | 1.0548          | 0.638    | 0.5581   | 0.638    |
| 1.1033        | 0.83  | 650  | 1.0300          | 0.6398   | 0.5593   | 0.6398   |
| 1.0946        | 0.9   | 700  | 1.0620          | 0.632    | 0.5545   | 0.632    |
| 1.0261        | 0.96  | 750  | 1.0328          | 0.6422   | 0.5648   | 0.6422   |
| 0.9153        | 1.02  | 800  | 1.0378          | 0.6438   | 0.5706   | 0.6438   |
| 0.9678        | 1.09  | 850  | 1.0520          | 0.6402   | 0.5483   | 0.6402   |
| 0.9619        | 1.15  | 900  | 1.0483          | 0.6408   | 0.5593   | 0.6408   |
| 0.9972        | 1.21  | 950  | 1.0255          | 0.6496   | 0.5685   | 0.6496   |
| 1.027         | 1.28  | 1000 | 1.0296          | 0.645    | 0.5742   | 0.645    |
| 0.8248        | 1.34  | 1050 | 1.0331          | 0.655    | 0.5812   | 0.655    |
| 0.9405        | 1.41  | 1100 | 1.0208          | 0.6502   | 0.5719   | 0.6502   |
| 0.9735        | 1.47  | 1150 | 1.0389          | 0.6388   | 0.5744   | 0.6388   |
| 0.9566        | 1.53  | 1200 | 0.9963          | 0.658    | 0.5750   | 0.658    |
| 0.9423        | 1.6   | 1250 | 0.9966          | 0.6496   | 0.5832   | 0.6496   |
| 0.9248        | 1.66  | 1300 | 0.9953          | 0.6558   | 0.5857   | 0.6558   |
| 1.008         | 1.73  | 1350 | 0.9940          | 0.6588   | 0.5809   | 0.6588   |
| 0.9098        | 1.79  | 1400 | 0.9833          | 0.657    | 0.5822   | 0.657    |
| 0.8679        | 1.85  | 1450 | 0.9842          | 0.6644   | 0.5899   | 0.6644   |
| 1.1342        | 1.92  | 1500 | 0.9933          | 0.6526   | 0.5762   | 0.6526   |
| 0.9157        | 1.98  | 1550 | 0.9869          | 0.6626   | 0.5924   | 0.6626   |
| 0.8084        | 2.05  | 1600 | 0.9909          | 0.6654   | 0.5893   | 0.6654   |
| 0.7373        | 2.11  | 1650 | 0.9894          | 0.6622   | 0.5965   | 0.6622   |
| 0.9081        | 2.17  | 1700 | 0.9997          | 0.6614   | 0.5880   | 0.6614   |
| 0.8064        | 2.24  | 1750 | 0.9998          | 0.659    | 0.5919   | 0.659    |
| 0.8519        | 2.3   | 1800 | 1.0031          | 0.6584   | 0.5880   | 0.6584   |
| 0.8711        | 2.37  | 1850 | 0.9975          | 0.6666   | 0.5981   | 0.6666   |
| 0.7617        | 2.43  | 1900 | 1.0144          | 0.6584   | 0.5849   | 0.6584   |
| 0.717         | 2.49  | 1950 | 1.0102          | 0.6622   | 0.5903   | 0.6622   |
| 0.857         | 2.56  | 2000 | 1.0059          | 0.6622   | 0.5923   | 0.6622   |
| 0.8623        | 2.62  | 2050 | 1.0025          | 0.664    | 0.5971   | 0.664    |
| 0.782         | 2.69  | 2100 | 1.0013          | 0.6644   | 0.5985   | 0.6644   |
| 0.8018        | 2.75  | 2150 | 1.0044          | 0.6652   | 0.5985   | 0.6652   |
| 0.7901        | 2.81  | 2200 | 0.9987          | 0.6678   | 0.6030   | 0.6678   |
| 0.8835        | 2.88  | 2250 | 1.0015          | 0.6644   | 0.5986   | 0.6644   |
| 0.8679        | 2.94  | 2300 | 0.9994          | 0.6636   | 0.5961   | 0.6636   |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2