README.md

---
base_model: readerbench/RoBERT-base
language:
- ro
tags:
- sentiment
- classification
- romanian
- nlp
- bert
datasets:
- decathlon_reviews
- cinemagia_reviews
metrics:
- accuracy
- precision
- recall
- f1
- f1 weighted
model-index:
- name: ro-sentiment
  results: 
  - task:
      type: text-classification             # Required. Example: automatic-speech-recognition
      name: Text Classification             # Optional. Example: Speech Recognition
    dataset:
      type: ro_sent          # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
      name: Rommanian Sentiment Dataset          # Required. A pretty name for the dataset. Example: Common Voice (French)
      config: default      # Optional. The name of the dataset configuration used in `load_dataset()`. Example: fr in `load_dataset("common_voice", "fr")`. See the `datasets` docs for more info: https://huggingface.co/docs/datasets/package_reference/loading_methods#datasets.load_dataset.name
      split: all        # Optional. Example: test
    metrics:
      - type: accuracy         # Required. Example: wer. Use metric id from https://hf.co/metrics
        value: 0.85       # Required. Example: 20.90
        name: Accuracy         # Optional. Example: Test WER
      - type: precision         # Required. Example: wer. Use metric id from https://hf.co/metrics
        value: 0.85       # Required. Example: 20.90
        name: Precision         # Optional. Example: Test WER
      - type: recall         # Required. Example: wer. Use metric id from https://hf.co/metrics
        value: 0.85       # Required. Example: 20.90
        name: Recall         # Optional. Example: Test WER
      - type: f1_weighted         # Required. Example: wer. Use metric id from https://hf.co/metrics
        value: 0.85       # Required. Example: 20.90
        name: Weighted F1         # Optional. Example: Test WER
      - type: f1_macro         # Required. Example: wer. Use metric id from https://hf.co/metrics
        value: 0.84       # Required. Example: 20.90
        name: Macro F1         # Optional. Example: Test WER
  - task:
      type: text-classification             # Required. Example: automatic-speech-recognition
      name: Text Classification             # Optional. Example: Speech Recognition
    dataset:
      type: laroseda          # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
      name: A Large Romanian Sentiment Data Set          # Required. A pretty name for the dataset. Example: Common Voice (French)
      config: default      # Optional. The name of the dataset configuration used in `load_dataset()`. Example: fr in `load_dataset("common_voice", "fr")`. See the `datasets` docs for more info: https://huggingface.co/docs/datasets/package_reference/loading_methods#datasets.load_dataset.name
      split: all        # Optional. Example: test
    metrics:
      - type: accuracy         # Required. Example: wer. Use metric id from https://hf.co/metrics
        value: 0.85       # Required. Example: 20.90
        name: Accuracy         # Optional. Example: Test WER
      - type: precision         # Required. Example: wer. Use metric id from https://hf.co/metrics
        value: 0.86       # Required. Example: 20.90
        name: Precision         # Optional. Example: Test WER
      - type: recall         # Required. Example: wer. Use metric id from https://hf.co/metrics
        value: 0.85       # Required. Example: 20.90
        name: Recall         # Optional. Example: Test WER
      - type: f1_weighted         # Required. Example: wer. Use metric id from https://hf.co/metrics
        value: 0.84       # Required. Example: 20.90
        name: Weighted F1         # Optional. Example: Test WER
      - type: f1_macro         # Required. Example: wer. Use metric id from https://hf.co/metrics
        value: 0.84       # Required. Example: 20.90
        name: Macro F1         # Optional. Example: Test WER
        
---

<!-- 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. -->

# RO-Sentiment

This model is a fine-tuned version of [readerbench/RoBERT-base](https://huggingface.co/readerbench/RoBERT-base) on the Decathlon reviews and Cinemagia reviews dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3923
- Accuracy: 0.8307
- Precision: 0.8366
- Recall: 0.8959
- F1: 0.8652
- F1 Weighted: 0.8287

Output labels:
- LABEL_0 = Negative Sentiment
- LABEL_1 = Positive Sentiment

### Evaluation on other datasets

**SENT_RO**


|  |precision   | recall   | f1-score   |  support |
|:-------------:|:-----:|:----:|:------:|:--------:|
| Negative (0)  | 0.79   | 0.83 | 0.81  | 11,675   |
| Positive (1) | 0.88  | 0.85 | 0.87   | 17,271   |
|   |   |  |   |    |
| Accuracy  |   |  | 0.85   | 28,946   |
| Macro Avg  | 0.84  | 0.84 | 0.84   | 28,946   |
| Weighted Avg  | 0.85  | 0.85 | 0.85   | 28,946   |

**LaRoSeDa**


|  |precision   | recall   | f1-score   |  support |
|:-------------:|:-----:|:----:|:------:|:--------:|
| Negative (0)  | 0.79   | 0.94 | 0.86  | 7,500   |
| Positive (1) | 0.93  | 0.75 | 0.83   | 7,500   |
|   |   |  |   |    |
| Accuracy  |   |  | 0.85   | 15,000   |
| Macro Avg  | 0.86  | 0.85 | 0.84   | 15,000   |
| Weighted Avg  | 0.86  | 0.85 | 0.84   | 15,000   |


## Model description

Finetuned Romanian BERT model for sentiment classification. 

Trained on a mix of product reviews from Decathlon retailer website and movie reviews from cinemagia.



## Intended uses & limitations

Sentiment classification for Romanian Language. 

Biased towards Product reviews. 

There is no "neutral" sentiment label. 

## Training and evaluation data

**Trained on:**
- Decathlon Dataset available on request

- Cinemagia Movie reviews public on kaggle [Link](https://www.kaggle.com/datasets/gringoandy/romanian-sentiment-movie-reviews)

**Evaluated on**

- Holdout data from training dataset
- RO_SENT Dataset
- LaROSeDa Dataset

  
## Training procedure


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 64
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 10 (Early stop epoch 3, best epoch 2)

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | F1 Weighted |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----------:|
| 0.4198        | 1.0   | 1629 | 0.3983          | 0.8377   | 0.8791    | 0.8721 | 0.8756 | 0.8380      |
| 0.3861        | **2.0**   | 3258 | 0.4312          | 0.8429   | 0.8963    | 0.8665 | 0.8812 | **0.8442**      |
| 0.3189        | 3.0   | 4887 | 0.3923          | 0.8307   | 0.8366    | 0.8959 | 0.8652 | 0.8287      |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3