


# multilingual_sentiment_newspaper_headlines

This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on a dataset of 30k newspaper headlines in German, Polish, English, Dutch and Spanish. The dataset contains 6k headlines in each of the five languages. The newspapers used are as follows: 

 + Polish: *Fakt, Rzeczpospolita, Gazeta Wyborcza*
 + English: *The Times, The Guardian, The Sun*
 + Dutch: *De Telegraaf, NRC, Volkskrant*
 + Spanish: *El Mundo, El Pais, ABC*
 + German: *Suddeutsche Zeitung, De Welt, Bild*


It achieves the following results on the evaluation set:
- Train Loss: 0.2886
- Train Sparse Categorical Accuracy: 0.8688
- Validation Loss: 1.0107
- Validation Sparse Categorical Accuracy: 0.6434
- Epoch: 4


```python
import torch
from transformers import AutoTokenizer, TextClassificationPipeline,TFAutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("z-dickson/multilingual_sentiment_newspaper_headlines")
m1 = TFAutoModelForSequenceClassification.from_pretrained("z-dickson/multilingual_sentiment_newspaper_headlines", from_tf=True)
sentiment_classifier = TextClassificationPipeline(tokenizer=tokenizer, model=m1)

sentiment_classifier('Brazylia: Bolsonaro wci±ż nie uznał porażki. Jego zwolennicy blokuj± autostrady')
[{'label': 'negative, 0', 'score': 0.9989686012268066}]
```


### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch |
|:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:|
| 0.8008     | 0.6130                            | 0.7099          | 0.6558                                 | 0     |
| 0.6148     | 0.6973                            | 0.7559          | 0.6200                                 | 1     |
| 0.4626     | 0.7690                            | 0.8233          | 0.6368                                 | 2     |
| 0.3632     | 0.8229                            | 0.9609          | 0.6454                                 | 3     |
| 0.2886     | 0.8688                            | 1.0107          | 0.6434                                 | 4     |


### Framework versions

- Transformers 4.26.0
- TensorFlow 2.9.2
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "multilingual_sentiment_newspaper_headlines", "results": []}]}
transformers tf bert text-classification generated_from_keras_callback license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

multilingual\_sentiment\_newspaper\_headlines
=============================================


This model is a fine-tuned version of bert-base-multilingual-cased on a dataset of 30k newspaper headlines in German, Polish, English, Dutch and Spanish. The dataset contains 6k headlines in each of the five languages. The newspapers used are as follows:


* Polish: *Fakt, Rzeczpospolita, Gazeta Wyborcza*
* English: *The Times, The Guardian, The Sun*
* Dutch: *De Telegraaf, NRC, Volkskrant*
* Spanish: *El Mundo, El Pais, ABC*
* German: *Suddeutsche Zeitung, De Welt, Bild*


It achieves the following results on the evaluation set:


* Train Loss: 0.2886
* Train Sparse Categorical Accuracy: 0.8688
* Validation Loss: 1.0107
* Validation Sparse Categorical Accuracy: 0.6434
* Epoch: 4


### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 2e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.26.0
* TensorFlow 2.9.2
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 2e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.26.0
* TensorFlow 2.9.2
* Tokenizers 0.13.2
TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 2e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.26.0
* TensorFlow 2.9.2
* Tokenizers 0.13.2
TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'Adam', 'learning\_rate': 2e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
* training\_precision: float32### Training results### Framework versions


* Transformers 4.26.0
* TensorFlow 2.9.2
* Tokenizers 0.13.2