metadata

license: mit
base_model: uhhlt/am-roberta
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: am-roberta-finetuned
    results: []

amharic-hate-speech

This model is a fine-tuned version of uhhlt/am-roberta on an AmahricHateSpeechRANL dataset. It achieves the following results on the evaluation set:

Loss: 0.6437
Accuracy: 0.7373
Precision: 0.7216
Recall: 0.7149
F1: 0.7180

How to use it

from transformers import pipeline
amhate_classifier = pipeline("text-classification", model="uhhlt/amharic-hate-speech")
amhate_classifier(["🌳☘️ 🌳☘️ለልጅ ልጅ የሚተላለፍ ዘመን ተሻጋሪ ኢንቨስትመንት !!!🌳☘️ 🌳☘️።", 
                  "አንተ አሁን ምን የሚሉህ ነህ? ግኡዝ አምላኪ ከመሆን ያድነን። ሰውን ያክል ፍጡር እየሞተ ለዛፍ ይሄን ያክል ማምለክ ጤነኝነት አይመስልም ። ፋኖ 100% ያሸንፋል",
                  "በአናትህ ተተከል ባንዳ ተላላኪ"])

Output

[{'label': 'normal', 'score': 0.8840981721878052},
 {'label': 'hate', 'score': 0.519339382648468},
 {'label': 'hate', 'score': 0.9630571007728577}]

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 128
eval_batch_size: 128
seed: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.8441	1.0	94	0.6699	0.7053	0.6913	0.6640	0.6737
0.6199	2.0	188	0.6505	0.72	0.7060	0.6995	0.6994
0.5295	3.0	282	0.6240	0.736	0.7201	0.7125	0.7159
0.4614	4.0	376	0.6437	0.7373	0.7216	0.7149	0.7180
0.3955	5.0	470	0.6922	0.7207	0.7001	0.7072	0.7031
0.3529	6.0	564	0.6995	0.7247	0.7050	0.7029	0.7039
0.3076	7.0	658	0.7352	0.7253	0.7067	0.7000	0.7031
0.2863	8.0	752	0.7470	0.7227	0.7019	0.6983	0.7000

Framework versions

Transformers 4.32.1
Pytorch 2.0.1+cu117
Datasets 2.14.4
Tokenizers 0.13.3