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

<!-- 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. -->

# amharic-hate-speech

This model is a fine-tuned version of [uhhlt/am-roberta](https://huggingface.co/uhhlt/am-roberta) on an [AmahricHateSpeechRANL](https://huggingface.co/datasets/uhhlt/amharichatespeechranlp) 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


``` python
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