ro-offense / README.md

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	---
	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