transformers pytorch bert text-classification generated_from_trainer et dataset:sentiment_reduced base_model:tartuNLP/EstBERT base_model:finetune:tartuNLP/EstBERT license:cc-by-4.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model:
- tartuNLP/EstBERT
datasets:
- sentiment_reduced
language: et
license: cc-by-4.0
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- generated_from_trainer
widget:
- text: Enam kui kümme aastat tagasi tegutses huumorisaates «Wremja» inspektor Kukeke,
    kes kogu aeg vingus väikese palga pärast ja vaatas, mida saaks töö juurest koju
    tassida. Stsenaristid Andrus Kivirähk ja Mart Juur olid Kukekese isikusse kokku
    valanud kõik, mis 1990. aastate Eesti politseinikke halvast küljest iseloomustas.
  example_title: negative
- text: 'Isiklikult kohtasin natukegi Kukekese moodi politseinikku viimati kaheksa
    aasta eest Lätis. Eranditult kõik viimase kümnendi kokkupuuted politseiametnikega
    on kinnitanud: vaatamata raskustele on Eesti riik suutnud korrakaitsjateks värvata
    inimesi, kes on arukad, kohusetundlikud, lugupidamist sisendavas füüsilises vormis
    ja hea väljendusoskusega.'
  example_title: positive
- text: Pisut retooriline küsimus, kelle või mille jaoks on Estonian Ai, nõuab taas
    vastust. Oleme jõudnud olukorda, kus vastus peaks olema juba konkreetne. Siinkohal
    tuleks hoiduda rahvusliku lennukompanii mõistest, mis pärineb ajast, kui lennundusäri
    oli peaaegu sajaprotsendiliselt riigi kontrolli all ning riigid ja nende grupeeringud
    reguleerisid äärmise põhjalikkusega lennundusturgu.
  example_title: neutral
model-index:
- name: EstBERT128_Sentiment
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Estonian Sentiment Corpus
      type: sentiment
    metrics:
    - type: accuracy
      value: 0.747863233089447
      name: Accuracy
---

<!-- 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. -->

# EstBERT128_sentiment

This model is a fine-tuned version of [tartuNLP/EstBERT](https://huggingface.co/tartuNLP/EstBERT) on the reduced version of the [Estonian Valence corpus](https://figshare.com/articles/dataset/Estonian_Valence_Corpus_Eesti_valentsikorpus/24517054), where the items with Mixed labels were removed.
The data (containing Positive, Negative and Neutral labels) was split into 70/10/20 train/dev/test splits.

It achieves the following results on the developments split:
- Loss: 2.2440
- Accuracy: 0.7926

It achieves the following results on the test split:
- Loss: 2.7633
- Accuracy: 0.7479

## How to use?

You can use this model with the Transformers pipeline for text classification.

```
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("tartuNLP/EstBERT128_sentiment")
model = AutoModelForSequenceClassification.from_pretrained("tartuNLP/EstBERT128_sentiment")

nlp = pipeline("text-classification", model=model, tokenizer=tokenizer)
text = "Viimastel nädalatel on üha valjemaks muutunud hääled, mis läbisegi süüdistavad regionaalminister Madis Kallast röövretke korraldamises rikastesse valdadesse ja teisalt tegevusetuses."
result = nlp(text)

print(result)
```

```
[{'label': 'negatiivne', 'score': 0.9999992847442627}]
```

## Model description

A single linear layer classifier is fit on top of the last layer [CLS] token representation of the EstBERT model. The model is fully fine-tuned during training.

## Intended uses & limitations

This model is intended to be used as it is. We hope that it can prove to be useful to somebody but we do not guarantee that the model is useful for anything or that the predictions are accurate on new data.

## Citation information
If you use this model, please cite:

```
@inproceedings{tanvir2021estbert,
  title={EstBERT: A Pretrained Language-Specific BERT for Estonian},
  author={Tanvir, Hasan and Kittask, Claudia and Eiche, Sandra and Sirts, Kairit},
  booktitle={Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)},
  pages={11--19},
  year={2021}
}
```

## Training and evaluation data

The model was trained and evaluated on the sentiment categories of the [Estonian Valence corpus](https://figshare.com/articles/dataset/Estonian_Valence_Corpus_Eesti_valentsikorpus/24517054).
The data was split into train/dev/test parts with 70/10/20 proportions.

The Estonian Valence corpus has four sentiment labels:
- positive
- negative
- neutral
- mixed

Following [Pajupuu et al., 2016](https://www.researchgate.net/profile/Hille-Pajupuu/publication/303837298_Identifying_Polarity_in_Different_Text_Types/links/575711e308ae05c1ec16ce05/Identifying-Polarity-in-Different-Text-Types.pdf), the items with mixed labels were removed.
Thus, the model was trained and evaluated on the reduced version of the dataset containing only three labels (positive, negative and neutral).


## Training procedure
The model was trained for maximu 100 epochs using early stopping procedure. After every epoch, the accuracy was calculated on the development set. 
If the development set accuracy did not improve for 20 epochs, the training was stopped.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06
- lr_scheduler_type: polynomial
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results
The final model was taken after 44th epoch.

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.836         | 1  | 38   | 0.6966          | 0.7216   |
| 0.5336        | 2  | 76   | 0.5948          | 0.7699   |
| 0.2913        | 3  | 114  | 0.7197          | 0.7358   |
| 0.1048        | 4  | 152  | 0.9570          | 0.7557   |
| 0.0424        | 5  | 190  | 1.2144          | 0.7528   |
| 0.0262        | 6  | 228  | 1.2675          | 0.7727   |
| 0.0169        | 7  | 266  | 1.4788          | 0.75     |
| 0.0048        | 8  | 304  | 1.5053          | 0.7699   |
| 0.0084        | 9  | 342  | 1.5368          | 0.7614   |
| 0.0087        | 10 | 380  | 1.6678          | 0.7699   |
| 0.0082        | 11 | 418  | 1.7598          | 0.7642   |
| 0.0104        | 12 | 456  | 1.6951          | 0.7528   |
| 0.0115        | 13 | 494  | 1.7123          | 0.7727   |
| 0.0111        | 14 | 532  | 1.7577          | 0.7528   |
| 0.0028        | 15 | 570  | 1.7383          | 0.7727   |
| 0.0032        | 16 | 608  | 2.0254          | 0.7727   |
| 0.0107        | 17 | 646  | 2.2123          | 0.7415   |
| 0.0056        | 18 | 684  | 1.9406          | 0.7614   |
| 0.0078        | 19 | 722  | 2.2002          | 0.7642   |
| 0.0041        | 20 | 760  | 2.0157          | 0.7670   |
| 0.0087        | 21 | 798  | 2.1228          | 0.7642   |
| 0.0113        | 22 | 836  | 2.3692          | 0.7727   |
| 0.0025        | 23 | 874  | 2.2211          | 0.75     |
| 0.0083        | 24 | 912  | 2.2120          | 0.7841   |
| 0.0104        | 25 | 950  | 2.1478          | 0.7614   |
| 0.0041        | 26 | 988  | 2.1118          | 0.7756   |
| 0.002         | 27 | 1026 | 1.9929          | 0.7699   |
| 0.001         | 28 | 1064 | 2.0295          | 0.7841   |
| 0.003         | 29 | 1102 | 2.3142          | 0.7699   |
| 0.006         | 30 | 1140 | 2.2957          | 0.7642   |
| 0.0005        | 31 | 1178 | 2.0661          | 0.7642   |
| 0.0007        | 32 | 1216 | 2.4220          | 0.7614   |
| 0.0088        | 33 | 1254 | 2.2842          | 0.7614   |
| 0.0           | 34 | 1292 | 2.4060          | 0.7585   |
| 0.0           | 35 | 1330 | 2.2088          | 0.7585   |
| 0.0           | 36 | 1368 | 2.2181          | 0.7614   |
| 0.0           | 37 | 1406 | 2.2560          | 0.7784   |
| 0.0           | 38 | 1444 | 2.4803          | 0.7585   |
| 0.0           | 39 | 1482 | 2.1163          | 0.7812   |
| 0.0087        | 40 | 1520 | 2.3410          | 0.75     |
| 0.0021        | 41 | 1558 | 2.3583          | 0.75     |
| 0.0054        | 42 | 1596 | 2.3546          | 0.7642   |
| 0.0051        | 43 | 1634 | 2.2295          | 0.7812   |
| 0.0           | 44 | 1672 | 2.2440          | 0.7926   |
| 0.0019        | 45 | 1710 | 2.3248          | 0.7784   |
| 0.0044        | 46 | 1748 | 2.3058          | 0.7841   |
| 0.0006        | 47 | 1786 | 2.3588          | 0.7784   |
| 0.0007        | 48 | 1824 | 2.6541          | 0.7670   |
| 0.0001        | 49 | 1862 | 2.4621          | 0.7614   |
| 0.0           | 50 | 1900 | 2.4696          | 0.7727   |
| 0.0           | 51 | 1938 | 2.4981          | 0.7670   |
| 0.0031        | 52 | 1976 | 2.6702          | 0.7670   |
| 0.0           | 53 | 2014 | 2.4448          | 0.7756   |
| 0.0           | 54 | 2052 | 2.4214          | 0.7756   |
| 0.0           | 55 | 2090 | 2.4308          | 0.7841   |
| 0.0001        | 56 | 2128 | 2.5869          | 0.7642   |
| 0.0007        | 57 | 2166 | 2.4803          | 0.7727   |
| 0.0           | 58 | 2204 | 2.4557          | 0.7784   |
| 0.0           | 59 | 2242 | 2.4702          | 0.7784   |
| 0.0           | 60 | 2280 | 2.5165          | 0.7784   |
| 0.0013        | 61 | 2318 | 2.6322          | 0.7727   |
| 0.0001        | 62 | 2356 | 2.6253          | 0.7756   |
| 0.0011        | 63 | 2394 | 2.6303          | 0.7841   |
| 0.0002        | 64 | 2432 | 2.5646          | 0.7614   |


### Framework versions

- Transformers 4.14.1
- Pytorch 1.10.1+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3

### Contact
  Kairit Sirts: kairit.sirts@ut.ee