README.md

---
base_model: readerbench/RoBERT-base
language:
  - ro
tags:
- hate speech
- offensive language
- romanian
- classification
- nlp
- bert
metrics:
- accuracy
- precision
- recall
- f1_macro
- f1_micro
- f1_weighted
model-index:
- name: ro-offense
  results: 
  - task:
      type: text-classification             # Required. Example: automatic-speech-recognition
      name: Text Classification             # Optional. Example: Speech Recognition
    dataset:
      type: readerbench/ro-offense          # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
      name: Rommanian Offensive Language 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: test        # Optional. Example: test
    metrics:
      - type: accuracy         # Required. Example: wer. Use metric id from https://hf.co/metrics
        value: 0.8190       # Required. Example: 20.90
        name: Accuracy         # Optional. Example: Test WER
      - type: precision         # Required. Example: wer. Use metric id from https://hf.co/metrics
        value: 0.8138       # Required. Example: 20.90
        name: Precision         # Optional. Example: Test WER
      - type: recall         # Required. Example: wer. Use metric id from https://hf.co/metrics
        value: 0.8118       # 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: 0.8189       # Required. Example: 20.90
        name: Weighted F1         # Optional. Example: Test WER
      - type: f1_micro        # Required. Example: wer. Use metric id from https://hf.co/metrics
        value: 0.8190       # Required. Example: 20.90
        name: Macro F1         # Optional. Example: Test WER
      - type: f1_macro         # Required. Example: wer. Use metric id from https://hf.co/metrics
        value: 0.8126       # 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-Offense

This model is a fine-tuned version of [readerbench/RoBERT-base](https://huggingface.co/readerbench/RoBERT-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8411
- Accuracy: 0.8232
- Precision: 0.8235
- Recall: 0.8210
- F1 Macro: 0.8207
- F1 Micro: 0.8232
- F1 Weighted: 0.8210

Output labels:
- LABEL_0 = No offensive language 
- LABEL_1 = Profanity (no directed insults)
- LABEL_2 = Insults (directed offensive language, lower level of offensiveness)
- LABEL_3 = Abuse (directed hate speech, racial slurs, sexist speech, threat with violence, death wishes, ..)

## Model description

Finetuned Romanian BERT model for offensive classification.

Trained on the [RO-Offense](https://huggingface.co/datasets/readerbench/ro-offense) Dataset 


## Intended uses & limitations

Offensive and Hate speech detection for Romanian Language

## Training and evaluation data

Trained on the train split of [RO-Offense](https://huggingface.co/datasets/readerbench/ro-offense) Dataset

Evaluated on the test split of [RO-Offense](https://huggingface.co/datasets/readerbench/ro-offense) Dataset 

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 4e-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 7, best epoch 4)

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Macro | F1 Micro | F1 Weighted |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|:--------:|:-----------:|
| No log        | 1.0   | 125  | 0.7789          | 0.7037   | 0.6825    | 0.7000 | 0.6873   | 0.7037   | 0.7132      |
| No log        | 2.0   | 250  | 0.5170          | 0.8006   | 0.8066    | 0.8016 | 0.7986   | 0.8006   | 0.7971      |
| No log        | 3.0   | 375  | 0.5139          | 0.8096   | 0.8168    | 0.8237 | 0.8120   | 0.8096   | 0.8047      |
| 0.6074        | **4.0**   | 500  | 0.6180          | 0.8247   | 0.8251    | 0.8187 | 0.8210   | 0.8247   | **0.8233**      |
| 0.6074        | 5.0   | 625  | 0.7311          | 0.8096   | 0.8071    | 0.8085 | 0.8064   | 0.8096   | 0.8071      |
| 0.6074        | 6.0   | 750  | 0.8365          | 0.8101   | 0.8117    | 0.8191 | 0.8105   | 0.8101   | 0.8051      |
| 0.6074        | 7.0   | 875  | 0.8411          | 0.8232   | 0.8235    | 0.8210 | 0.8207   | 0.8232   | 0.8210      |


### Framework versions

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