

# Sentiment Analysis for Tigrinya with TiELECTRA small

This model is a fine-tuned version of [TiELECTRA small](https://huggingface.co/fgaim/tielectra-small) on a YouTube comments Sentiment Analysis dataset for Tigrinya (Tela et al. 2020).


## Basic usage

```python
from transformers import pipeline

ti_sent = pipeline("sentiment-analysis", model="fgaim/tielectra-small-sentiment")
ti_sent("ድምጻዊ ኣብርሃም ኣፈወርቂ ንዘልኣለም ህያው ኮይኑ ኣብ ልብና ይነብር")
```


## Training

### Hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Results

The model achieves the following results on the evaluation set:
- F1: 0.8229
- Precision: 0.8056
- Recall: 0.841
- Accuracy: 0.819
- Loss: 0.4299

### Framework versions

- Transformers 4.10.3
- Pytorch 1.9.0+cu111
- Datasets 1.10.2
- Tokenizers 0.10.1


## Citation

If you use this model in your product or research, please cite as follows:

```
@article{Fitsum2021TiPLMs,
  author={Fitsum Gaim and Wonsuk Yang and Jong C. Park},
  title={Monolingual Pre-trained Language Models for Tigrinya},
  year=2021,
  publisher= {WiNLP 2021/EMNLP 2021}
}
```


## References

```
Tela, A., Woubie, A. and Hautamäki, V. 2020.
Transferring Monolingual Model to Low-Resource Language: The Case of Tigrinya.
ArXiv, abs/2006.07698.
```

{"language": "ti", "metrics": ["f1", "precision", "recall", "accuracy"], "widget": [{"text": "\u12f5\u121d\u133b\u12ca \u12a3\u1265\u122d\u1203\u121d \u12a3\u1348\u12c8\u122d\u1242 \u1295\u12d8\u120d\u12a3\u1208\u121d \u1205\u12eb\u12cd \u12ae\u12ed\u1291 \u12a3\u1265 \u120d\u1265\u1293 \u12ed\u1290\u1265\u122d"}]}
transformers pytorch electra text-classification ti model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #electra #text-classification #ti #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 



# Sentiment Analysis for Tigrinya with TiELECTRA small

This model is a fine-tuned version of TiELECTRA small on a YouTube comments Sentiment Analysis dataset for Tigrinya (Tela et al. 2020).


## Basic usage




## Training

### Hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Results

The model achieves the following results on the evaluation set:
- F1: 0.8229
- Precision: 0.8056
- Recall: 0.841
- Accuracy: 0.819
- Loss: 0.4299

### Framework versions

- Transformers 4.10.3
- Pytorch 1.9.0+cu111
- Datasets 1.10.2
- Tokenizers 0.10.1


If you use this model in your product or research, please cite as follows:




## References



# Sentiment Analysis for Tigrinya with TiELECTRA small

This model is a fine-tuned version of TiELECTRA small on a YouTube comments Sentiment Analysis dataset for Tigrinya (Tela et al. 2020). ## Basic usage ## Training ### Hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0 ### Results

The model achieves the following results on the evaluation set:
- F1: 0.8229
- Precision: 0.8056
- Recall: 0.841
- Accuracy: 0.819
- Loss: 0.4299 ### Framework versions

- Transformers 4.10.3
- Pytorch 1.9.0+cu111
- Datasets 1.10.2
- Tokenizers 0.10.1


If you use this model in your product or research, please cite as follows: ## References
TAGS
#transformers #pytorch #electra #text-classification #ti #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Sentiment Analysis for Tigrinya with TiELECTRA small

This model is a fine-tuned version of TiELECTRA small on a YouTube comments Sentiment Analysis dataset for Tigrinya (Tela et al. 2020). ## Basic usage ## Training ### Hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0 ### Results

The model achieves the following results on the evaluation set:
- F1: 0.8229
- Precision: 0.8056
- Recall: 0.841
- Accuracy: 0.819
- Loss: 0.4299 ### Framework versions

- Transformers 4.10.3
- Pytorch 1.9.0+cu111
- Datasets 1.10.2
- Tokenizers 0.10.1


If you use this model in your product or research, please cite as follows: ## References
passage: TAGS
#transformers #pytorch #electra #text-classification #ti #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Sentiment Analysis for Tigrinya with TiELECTRA small

This model is a fine-tuned version of TiELECTRA small on a YouTube comments Sentiment Analysis dataset for Tigrinya (Tela et al. 2020).## Basic usage## Training### Hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0### Results

The model achieves the following results on the evaluation set:
- F1: 0.8229
- Precision: 0.8056
- Recall: 0.841
- Accuracy: 0.819
- Loss: 0.4299### Framework versions

- Transformers 4.10.3
- Pytorch 1.9.0+cu111
- Datasets 1.10.2
- Tokenizers 0.10.1


If you use this model in your product or research, please cite as follows:## References