Hugging Face's logo

akhooli
/
setfit_ar_ubc_hs

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files Community
setfit_ar_ubc_hs
File size: 11,303 Bytes 
71187fb
 
 
 
 
 
 
 
 
 
 
 
54d3dc1
 
 
 
 
 
 
 
 
 
 
 
 
71187fb
 
 
 
 
 
 
 
 
 
 
 
 
54d3dc1
71187fb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
54d3dc1
 
 
 
71187fb
 
 
 
 
 
54d3dc1
71187fb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
54d3dc1
 
 
 
 
 
 
 
71187fb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
54d3dc1
71187fb
 
 
54d3dc1
 
71187fb
 
 
 
ee97430
71187fb
 
 
 
 
 
 
 
 
 
 
54d3dc1
71187fb
 
 
 
 
 
54d3dc1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8800f9d
71187fb
 
 
 
6f885ba
71187fb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
 
---
base_model: akhooli/sbert_ar_nli_500k_ubc_norm
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 'بلد مخيف، صار القتل بحجه الشرف متل قتل بعوضة، واللي بيخوف اكتر من اللي واقف
    مكتف ايديه ومش مساعد. وين كنآ، ووين وصلنآ، لمتى حنضل عايشين وساكتين!

    '
- text: "من خلال المتابعة ..يتضح أن أكثر اللاعبين الذين يتم تسويقهم هم لاعبي امريكا\
    \ الجنوبية وأقلهم الافارقة.  \nمن خلال الواقع ..أكثر اللاعبين تهاونا ولعب على\
    \ الواقف  في آخر ٦ شهور من عقودهم  هم لاعبي امريكا الجنوبية ."
- text: ' علم الحزب يا فهمانه ما حطوا لانه عم يحكي وطنيا ومشان ماحدا متلك يعترض. اذا
    حطوا بتعترضي واذا ما حطوا كمان بتعترضي.'
- text: "شيوعي \nعلماني \nمسيحي\nانصار سنه \nصوفي \nيمثلك التجمع \nلا يمثلك التجمع\
    \ \nاهلا بكم جميعا فنحن نريد بناء وطن ❤"
- text: كنا نهرب بحصة الرياضيات والمحاسبة وبنرجع آخر الحصة بخمس دئايئ ولمن تسئلنا
    المعلمة بنحكيلها كنا عند المديرة وبتسدئنا وضلينا
inference: true
model-index:
- name: SetFit with akhooli/sbert_ar_nli_500k_ubc_norm
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.8398268398268398
      name: Accuracy
---

# SetFit with akhooli/sbert_ar_nli_500k_ubc_norm

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [akhooli/sbert_ar_nli_500k_ubc_norm](https://huggingface.co./akhooli/sbert_ar_nli_500k_ubc_norm) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

## Model Details

### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [akhooli/sbert_ar_nli_500k_ubc_norm](https://huggingface.co./akhooli/sbert_ar_nli_500k_ubc_norm)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co./datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co./blog/setfit)

### Model Labels
| Label    | Examples                                                                                                                                                                                                                                       |
|:---------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| positive | <ul><li>' سبحان الله الفلسطينيين شعب خاين في كل مكان \nلاحول ولا قوة إلا بالله'</li><li>'يا بيك عّم تخبرنا عن شي ما فينا تعملو نحن ماًعندنا نواب ولا وزراء بمثلونا بالدولة الا اذا زهقان وعبالك ليك'</li><li>'جوز كذابين منافقين...'</li></ul> |
| negative | <ul><li>'ربي لا تجعلني أسيء الظن بأحد ولا تجعل في قلبي شيئا على أحد ، اللهم أسألك قلباً نقياً صافيا'</li><li>'هشام حداد عامل فيها جون ستيوارت'</li><li>'     بحياة اختك من وين بتجيبي اخبارك؟؟ من صغري وانا عبالي كون... LINK'</li></ul>       |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.8398   |

## Uses

### Direct Use for Inference

First install the SetFit library:

```bash
pip install setfit
```

Then you can load this model and run inference.

```python
from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("akhooli/setfit_ar_ubc_hs")
# Run inference
preds = model("شيوعي 
علماني 
مسيحي
انصار سنه 
صوفي 
يمثلك التجمع 
لا يمثلك التجمع 
اهلا بكم جميعا فنحن نريد بناء وطن ❤")
```

<!--
### Downstream Use

*List how someone could finetune this model on their own dataset.*
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 1   | 18.8448 | 185 |

| Label    | Training Sample Count |
|:---------|:----------------------|
| negative | 5200                  |
| positive | 4943                  |

### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (1, 1)
- max_steps: 6000
- sampling_strategy: undersampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- run_name: setfit_hate_52k_ubc_6k
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0003 | 1    | 0.297         | -               |
| 0.0333 | 100  | 0.2741        | -               |
| 0.0667 | 200  | 0.2178        | -               |
| 0.1    | 300  | 0.1724        | -               |
| 0.1333 | 400  | 0.1449        | -               |
| 0.1667 | 500  | 0.1137        | -               |
| 0.2    | 600  | 0.0902        | -               |
| 0.2333 | 700  | 0.0708        | -               |
| 0.2667 | 800  | 0.0535        | -               |
| 0.3    | 900  | 0.0483        | -               |
| 0.3333 | 1000 | 0.0386        | -               |
| 0.3667 | 1100 | 0.0319        | -               |
| 0.4    | 1200 | 0.0279        | -               |
| 0.4333 | 1300 | 0.0201        | -               |
| 0.4667 | 1400 | 0.0234        | -               |
| 0.5    | 1500 | 0.0151        | -               |
| 0.5333 | 1600 | 0.0151        | -               |
| 0.5667 | 1700 | 0.0137        | -               |
| 0.6    | 1800 | 0.0117        | -               |
| 0.6333 | 1900 | 0.011         | -               |
| 0.6667 | 2000 | 0.0097        | -               |
| 0.7    | 2100 | 0.0077        | -               |
| 0.7333 | 2200 | 0.0089        | -               |
| 0.7667 | 2300 | 0.0069        | -               |
| 0.8    | 2400 | 0.0064        | -               |
| 0.8333 | 2500 | 0.0083        | -               |
| 0.8667 | 2600 | 0.0061        | -               |
| 0.9    | 2700 | 0.0063        | -               |
| 0.9333 | 2800 | 0.0051        | -               |
| 0.9667 | 2900 | 0.0047        | -               |
| 1.0    | 3000 | 0.0044        | -               |
| 1.0333 | 3100 | 0.0035        | -               |
| 1.0667 | 3200 | 0.0034        | -               |
| 1.1    | 3300 | 0.0035        | -               |
| 1.1333 | 3400 | 0.0043        | -               |
| 1.1667 | 3500 | 0.0035        | -               |
| 1.2    | 3600 | 0.0024        | -               |
| 1.2333 | 3700 | 0.003         | -               |
| 1.2667 | 3800 | 0.002         | -               |
| 1.3    | 3900 | 0.0029        | -               |
| 1.3333 | 4000 | 0.003         | -               |
| 1.3667 | 4100 | 0.002         | -               |
| 1.4    | 4200 | 0.0022        | -               |
| 1.4333 | 4300 | 0.0027        | -               |
| 1.4667 | 4400 | 0.004         | -               |
| 1.5    | 4500 | 0.001         | -               |
| 1.5333 | 4600 | 0.0027        | -               |
| 1.5667 | 4700 | 0.0027        | -               |
| 1.6    | 4800 | 0.0014        | -               |
| 1.6333 | 4900 | 0.0022        | -               |
| 1.6667 | 5000 | 0.0027        | -               |
| 1.7    | 5100 | 0.0018        | -               |
| 1.7333 | 5200 | 0.0018        | -               |
| 1.7667 | 5300 | 0.0012        | -               |
| 1.8    | 5400 | 0.0014        | -               |
| 1.8333 | 5500 | 0.0015        | -               |
| 1.8667 | 5600 | 0.0009        | -               |
| 1.9    | 5700 | 0.0012        | -               |
| 1.9333 | 5800 | 0.0009        | -               |
| 1.9667 | 5900 | 0.001         | -               |
| 2.0    | 6000 | 0.0007        | -               |

### Framework Versions
- Python: 3.10.14
- SetFit: 1.2.0.dev0
- Sentence Transformers: 3.3.0
- Transformers: 4.45.1
- PyTorch: 2.4.0
- Datasets: 3.0.1
- Tokenizers: 0.20.0

## Citation

### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
    doi = {10.48550/ARXIV.2209.11055},
    url = {https://arxiv.org/abs/2209.11055},
    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->